├── Data Analytics & Visualization Course Materials ├── Statistics │ ├── Statistics.pdf │ ├── Introduction to Basic and Conditional Probability │ │ ├── matches.xlsx │ │ ├── Random Variables.ipynb │ │ └── Basic and Conditional Probability.ipynb │ └── Introduction to Descriptive Statistics │ │ └── Types of Data.ipynb ├── Data Analysis & Visualization.zip ├── What if Analysis │ ├── 3. Data Table.xlsx │ ├── 4. Solver package.xlsx │ ├── 1. Scenario Manager.xlsx │ └── 2. Goal seek Analysis.xlsx ├── Charts & Dashboards │ ├── 2. Types of Charts in Excel.xlsx │ ├── 3. Creating and Formatting Charts.xlsx │ └── 1. Data Visualization Best Practices.xlsx ├── Data Cleaning & Feature Engineering │ ├── Store Data.xlsx │ ├── 2. Basics of Data Cleaning in Excel.xlsx │ ├── 3. Basics of Feature Engineering in Excel.xlsx │ ├── 4. Introduction to Power Query in Excel.xlsx │ └── 1. Introduction to Date and Time Functions.xlsx ├── Lookup Functions & Pivot Tables │ ├── 3. Introduction to Pivot Tables.xlsx │ └── 4. Introduction to Pivot Charts.xlsx ├── Logical Functions & Text Functions │ ├── 3. Introduction to Text Functions.xlsx │ ├── 1. Introduction to logical function.xlsx │ ├── 4. Formatting Cells based on Text Functions.xlsx │ └── 2. Formatting Cells based on Logical Functions.xlsx ├── README.md └── Hypothesis │ ├── Introduction to Chi Squared Tests.ipynb │ ├── Introduction to T Tests.ipynb │ └── Introduction to Z Tests.ipynb ├── LICENSE ├── Advanced Excel Skills ├── VBA and Macros │ ├── Creating_User_Defined_Functions.md │ ├── Recording_Macros.md │ ├── Automating_Repetitive_Tasks.md │ ├── Working_with_Loops_and_Conditions.md │ ├── Writing_VBA_Scripts.md │ └── README.md ├── Array Formulas │ ├── Legacy_Array_Formulas.md │ ├── Multi_Cell_Calculations.md │ ├── Dynamic_Array_Formulas.md │ └── README.md ├── Dashboards │ ├── Dynamic_Ranges_with_OFFSET.md │ ├── Using_Slicers_and_Pivot_Charts.md │ ├── Visual_Best_Practices.md │ ├── Building_Interactive_Dashboards.md │ └── README.md ├── Data Analysis Tools │ ├── Advanced_Filters.md │ ├── Solver_for_Optimization.md │ ├── Data_Tables_for_Sensitivity_Analysis.md │ ├── What_If_Analysis.md │ └── README.md └── Power Query │ ├── Automating_Data_Refresh.md │ ├── Creating_Custom_Columns.md │ ├── Importing_Data.md │ ├── Transforming_Data.md │ └── README.md ├── Intermediate Excel Skills ├── Charts and Visualization │ ├── Trendlines.md │ ├── Sparklines.md │ ├── Custom_Chart_Formatting.md │ ├── Combo_Charts.md │ └── README.md ├── Lookup and Reference │ ├── OFFSET_and_INDIRECT.md │ ├── INDEX_and_MATCH.md │ ├── VLOOKUP_and_HLOOKUP.md │ └── README.md ├── Pivot Tables │ ├── Grouping_Data.md │ ├── Slicers_and_Timelines.md │ ├── Creating_Pivot_Tables.md │ ├── Calculated_Fields.md │ └── README.md └── Conditional Formatting │ ├── Custom_Rules_with_Formulas.md │ ├── Data_Bars_Color_Scales_Icon_Sets.md │ ├── Highlight_Rules.md │ └── README.md ├── Excel Fundamentals ├── 02 Formulas and Functions │ ├── Date_Functions.md │ ├── Lookup_Functions.md │ ├── Text_Functions.md │ ├── Logical_Functions.md │ ├── Arithmetic_Operators.md │ └── README.md ├── 03 Data Management │ ├── Removing_Duplicates.md │ ├── Flash_Fill.md │ ├── Freeze_Panes.md │ ├── Splitting_Text_to_Columns.md │ ├── Data_Entry_and_Validation.md │ └── README.md └── 01 Basic Operations │ ├── Sorting_and_Filtering.md │ ├── Charts.md │ ├── Basic_Formulas.md │ ├── Cell_Formatting.md │ ├── README.md │ └── Navigation.md └── README.md /Data Analytics & Visualization Course Materials/Statistics/Statistics.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Excel-Interview-Preparation/main/Data Analytics & Visualization Course Materials/Statistics/Statistics.pdf -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Data Analysis & Visualization.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Excel-Interview-Preparation/main/Data Analytics & Visualization Course Materials/Data Analysis & Visualization.zip -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/What if Analysis/3. 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Formatting Cells based on Logical Functions.xlsx -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/README.md: -------------------------------------------------------------------------------- 1 | # Data Analytics & Visualization Course Materials 2 | 3 | ## Subfolders 4 | - Charts & Dashboards 5 | - Data Analysis & Visualization 6 | - Data Cleaning & Feature Engineering 7 | - Hypothesis 8 | - Logical Functions & Text Functions 9 | - Lookup Functions & Pivot Tables 10 | - Statistics 11 | - What if Analysis -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 rohanmistry231 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /Advanced Excel Skills/VBA and Macros/Creating_User_Defined_Functions.md: -------------------------------------------------------------------------------- 1 | # Creating User-Defined Functions in VBA 2 | 3 | ## Overview 4 | User-Defined Functions (UDFs) create custom Excel formulas, enhancing analysis for interviews. 5 | 6 | ## Key Concepts 7 | - **UDF Syntax**: `Function` instead of `Sub`. 8 | - **Access**: Use in cells like built-in functions. 9 | - **Scope**: Save in module for workbook-wide use. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Create a UDF to calculate commission (10% of sales). 13 | 1. **Write UDF**: 14 | ```vba 15 | Function CalcCommission(sales As Double) As Double 16 | CalcCommission = sales * 0.1 17 | End Function 18 | ``` 19 | 2. **Use in Excel**: 20 | - In B2, enter `=CalcCommission(A2)` where A2 is sales value. 21 | 22 | ## Practical Scenario 23 | Create a UDF to calculate tax (5% for sales >1000, 3% otherwise). 24 | 25 | ## Practice Tasks 26 | 1. Create a UDF to calculate profit (sales - cost). 27 | 2. Write a UDF for custom text concatenation. 28 | 3. Build a UDF to round numbers to nearest 10. 29 | 4. Test a UDF with multiple arguments. 30 | 5. Debug a UDF with incorrect inputs. 31 | 32 | ## Interview Tip 33 | Interviewers may ask for custom formulas. Practice writing and testing UDFs in Excel. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Charts and Visualization/Trendlines.md: -------------------------------------------------------------------------------- 1 | # Trendlines in Excel 2 | 3 | ## Overview 4 | Trendlines show data trends in charts, useful for forecasting and interviews. 5 | 6 | ## Key Concepts 7 | - **Types**: Linear, Exponential, Polynomial, etc. 8 | - **Insert**: Right-click data series > Add Trendline. 9 | - **Options**: Display equation, R-squared value. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Analyze sales trend over time. 13 | 1. **Create Chart**: 14 | - Data: Month (A2:A13), Sales (B2:B13). 15 | - Insert > Line Chart. 16 | 2. **Add Trendline**: 17 | - Right-click line > Add Trendline > Linear. 18 | - Check “Display Equation on Chart”. 19 | 3. **Customize**: 20 | - Format Trendline to red, dashed line. 21 | 22 | ## Practical Scenario 23 | You have yearly revenue data. Add a Linear Trendline to predict next year’s revenue. 24 | 25 | ## Practice Tasks 26 | 1. Add a Linear Trendline to a sales line chart. 27 | 2. Use an Exponential Trendline for growth data. 28 | 3. Display the trendline equation and R-squared. 29 | 4. Compare two trendlines (e.g., Linear vs. Polynomial). 30 | 5. Forecast values using a trendline equation. 31 | 32 | ## Interview Tip 33 | Interviewers may ask for trend analysis. Practice adding and interpreting trendlines. -------------------------------------------------------------------------------- /Advanced Excel Skills/Array Formulas/Legacy_Array_Formulas.md: -------------------------------------------------------------------------------- 1 | # Legacy Array Formulas in Excel 2 | 3 | ## Overview 4 | Legacy Array Formulas (Ctrl+Shift+Enter) handle complex calculations in older Excel versions, still relevant for interviews. 5 | 6 | ## Key Concepts 7 | - **Syntax**: Enter with Ctrl+Shift+Enter (curly braces appear). 8 | - **Use Cases**: Multi-cell calculations, conditional sums. 9 | - **Example**: `=SUM(IF(A2:A100="North", B2:B100))`. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Sum sales for “North” region. 13 | 1. **Setup Data**: 14 | - Region (A2:A100), Sales (B2:B100). 15 | 2. **Legacy Array Formula**: 16 | - In C2, enter `=SUM(IF(A2:A100="North", B2:B100))`. 17 | - Press Ctrl+Shift+Enter. 18 | 3. **Verify**: Check sum against manual calculation. 19 | 20 | ## Practical Scenario 21 | Calculate average sales for products with quantities >10 using a legacy array formula. 22 | 23 | ## Practice Tasks 24 | 1. Sum sales for a specific category with an array formula. 25 | 2. Calculate max value for filtered data. 26 | 3. Use an array formula with multiple conditions. 27 | 4. Test legacy vs. dynamic arrays. 28 | 5. Debug an array formula error. 29 | 30 | ## Interview Tip 31 | Interviewers may test compatibility knowledge. Practice legacy arrays for older Excel versions. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Charts and Visualization/Sparklines.md: -------------------------------------------------------------------------------- 1 | # Sparklines in Excel 2 | 3 | ## Overview 4 | Sparklines are mini-charts within cells, showing trends compactly, useful for dashboards and interviews. 5 | 6 | ## Key Concepts 7 | - **Types**: Line, Column, Win/Loss. 8 | - **Insert**: Insert > Sparklines. 9 | - **Customization**: Adjust colors, markers, and axes. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Show monthly sales trends in a cell. 13 | 1. **Prepare Data**: 14 | - Columns: Month (A2:A13), Sales (B2:B13). 15 | 2. **Insert Sparkline**: 16 | - Select C2. 17 | - Insert > Sparklines > Line > Data Range: B2:B13. 18 | 3. **Customize**: 19 | - Sparkline Tools > Design > Show High/Low Points. 20 | - Change color to blue. 21 | 22 | ## Practical Scenario 23 | You have weekly website traffic data. Add Line Sparklines to show trends for each region in a single cell. 24 | 25 | ## Practice Tasks 26 | 1. Add Line Sparklines for sales data. 27 | 2. Create Column Sparklines for expense trends. 28 | 3. Use Win/Loss Sparklines for positive/negative profits. 29 | 4. Customize Sparklines with markers for high points. 30 | 5. Add Sparklines to a dashboard table. 31 | 32 | ## Interview Tip 33 | Interviewers may ask for compact visualizations. Practice Sparklines for quick trend analysis. -------------------------------------------------------------------------------- /Advanced Excel Skills/Dashboards/Dynamic_Ranges_with_OFFSET.md: -------------------------------------------------------------------------------- 1 | # Dynamic Ranges with OFFSET in Excel 2 | 3 | ## Overview 4 | Dynamic ranges using OFFSET adapt charts and tables to changing data, a valuable skill for interviews. 5 | 6 | ## Key Concepts 7 | - **OFFSET**: `=OFFSET(reference, rows, cols, [height], [width])`. 8 | - **Named Ranges**: Define dynamic ranges (Formulas > Name Manager). 9 | - **Use Cases**: Charts, Pivot Tables, dashboards. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Create a dynamic chart range. 13 | 1. **Setup Data**: 14 | - A1:B100: Month, Sales. 15 | 2. **Define Named Range**: 16 | - Formulas > Name Manager > New. 17 | - Name: “DynamicSales”, Formula: `=OFFSET(Sheet1!$B$2,0,0,COUNT(Sheet1!$B$2:$B$100),1)`. 18 | 3. **Create Chart**: 19 | - Insert Chart using named range “DynamicSales”. 20 | 21 | ## Practical Scenario 22 | Create a dashboard with a dynamic chart that updates as new sales data is added. 23 | 24 | ## Practice Tasks 25 | 1. Create a dynamic range for a sales column. 26 | 2. Use OFFSET in a chart data source. 27 | 3. Define a named range for a Pivot Table. 28 | 4. Test dynamic range with new data. 29 | 5. Combine OFFSET with COUNTIF for conditional ranges. 30 | 31 | ## Interview Tip 32 | Interviewers may ask for adaptable dashboards. Practice OFFSET for dynamic data sources. -------------------------------------------------------------------------------- /Advanced Excel Skills/Array Formulas/Multi_Cell_Calculations.md: -------------------------------------------------------------------------------- 1 | # Multi-Cell Calculations with Array Formulas 2 | 3 | ## Overview 4 | Multi-cell array formulas perform complex calculations across ranges, useful for advanced analysis and interviews. 5 | 6 | ## Key Concepts 7 | - **Dynamic Arrays**: Spill results automatically (Excel 365). 8 | - **Legacy Arrays**: Ctrl+Shift+Enter for fixed ranges. 9 | - **Examples**: Combine conditions, math operations. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Calculate total revenue per product. 13 | 1. **Setup Data**: 14 | - Product (A2:A100), Quantity (B2:B100), Price (C2:C100). 15 | 2. **Dynamic Array**: 16 | - In D2, enter `=B2:B100*C2:C100` (spills results). 17 | 3. **Legacy Array**: 18 | - Select D2:D100, enter `=B2:B100*C2:C100`, press Ctrl+Shift+Enter. 19 | 20 | ## Practical Scenario 21 | Calculate profit (sales - cost) for multiple products using an array formula, then filter for positive profits. 22 | 23 | ## Practice Tasks 24 | 1. Multiply two columns with a dynamic array. 25 | 2. Use a legacy array for conditional sums. 26 | 3. Combine arrays with FILTER for complex criteria. 27 | 4. Test array formulas with large datasets. 28 | 5. Debug multi-cell array errors. 29 | 30 | ## Interview Tip 31 | Interviewers may test complex calculations. Practice array formulas for efficient data processing. -------------------------------------------------------------------------------- /Advanced Excel Skills/Data Analysis Tools/Advanced_Filters.md: -------------------------------------------------------------------------------- 1 | # Advanced Filters in Excel 2 | 3 | ## Overview 4 | Advanced Filters handle complex filtering criteria, a key skill for advanced data analysis and interviews. 5 | 6 | ## Key Concepts 7 | - **Advanced Filter**: Data > Advanced. 8 | - **Criteria Range**: Define conditions in a separate range. 9 | - **Options**: Filter in place or copy to another location. 10 | - **Conditions**: Use formulas or multiple criteria. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Filter sales >1000 in “North” region. 14 | 1. **Setup Data**: 15 | - Columns: Region (A), Sales (B). 16 | 2. **Create Criteria Range**: 17 | - D1:D2: Region, Sales. 18 | - D2: “North”, E2: “>1000”. 19 | 3. **Apply Filter**: 20 | - Data > Advanced. 21 | - List Range: A1:B100, Criteria Range: D1:E2. 22 | - Copy to: F1. 23 | 24 | ## Practical Scenario 25 | Filter a dataset for employees with salary >$50,000 and years of service >5. 26 | 27 | ## Practice Tasks 28 | 1. Filter data for sales >500 in specific categories. 29 | 2. Use a formula in the criteria range (e.g., `=B2>AVERAGE(B:B)`). 30 | 3. Copy filtered results to a new sheet. 31 | 4. Combine multiple criteria (e.g., date and value). 32 | 5. Test Advanced Filter with text conditions. 33 | 34 | ## Interview Tip 35 | Interviewers may test complex filtering. Practice setting up criteria ranges and explaining logic. -------------------------------------------------------------------------------- /Advanced Excel Skills/VBA and Macros/Recording_Macros.md: -------------------------------------------------------------------------------- 1 | # Recording Macros in Excel 2 | 3 | ## Overview 4 | Recording macros automates repetitive tasks without coding, a great introduction to VBA for interviews. 5 | 6 | ## Key Concepts 7 | - **Record Macro**: Developer > Record Macro. 8 | - **Steps**: Perform actions to record (e.g., format cells). 9 | - **Storage**: Save in This Workbook or Personal Macro Workbook. 10 | - **Run Macro**: Developer > Macros > Run. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Automate formatting a sales table. 14 | 1. **Start Recording**: 15 | - Developer > Record Macro > Name: “FormatTable”. 16 | 2. **Perform Actions**: 17 | - Select A1:D10. 18 | - Apply bold, blue fill, and borders. 19 | 3. **Stop Recording**: 20 | - Developer > Stop Recording. 21 | 4. **Run Macro**: 22 | - Developer > Macros > FormatTable > Run. 23 | 24 | ## Practical Scenario 25 | You have a monthly report. Record a macro to format headers, apply currency format, and add borders. 26 | 27 | ## Practice Tasks 28 | 1. Record a macro to bold and center a header row. 29 | 2. Save a macro to the Personal Macro Workbook. 30 | 3. Run a recorded macro on a new dataset. 31 | 4. Edit a macro to add a new formatting step. 32 | 5. Record a macro to sort data by sales. 33 | 34 | ## Interview Tip 35 | Interviewers may ask for automation. Practice recording and running macros to demonstrate efficiency. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Lookup and Reference/OFFSET_and_INDIRECT.md: -------------------------------------------------------------------------------- 1 | # OFFSET and INDIRECT in Excel 2 | 3 | ## Overview 4 | OFFSET and INDIRECT create dynamic references, useful for advanced analysis and interviews. 5 | 6 | ## Key Functions 7 | - **OFFSET**: `=OFFSET(reference, rows, cols, [height], [width])` – Returns a dynamic range. 8 | - **INDIRECT**: `=INDIRECT(ref_text)` – References a cell or range by text. 9 | 10 | ## Step-by-Step Example 11 | **Scenario**: Create a dynamic sum based on user input. 12 | 1. **OFFSET**: 13 | - Data: Sales in B2:B100. 14 | - In C1, enter number of rows (e.g., 5). 15 | - In D1, enter `=SUM(OFFSET(B2, 0, 0, C1, 1))` to sum first 5 rows. 16 | 2. **INDIRECT**: 17 | - In E1, enter sheet name (e.g., “Sheet1”). 18 | - In F1, enter `=INDIRECT(E1 & "!B2")` to reference B2 on Sheet1. 19 | 20 | ## Practical Scenario 21 | You have monthly sales in different sheets. Use INDIRECT to pull data from a specified sheet and OFFSET to sum a dynamic range. 22 | 23 | ## Practice Tasks 24 | 1. Use OFFSET to sum the last 10 rows of data. 25 | 2. Use INDIRECT to reference a cell in another sheet. 26 | 3. Combine OFFSET with COUNT for dynamic ranges. 27 | 4. Create a dynamic chart range with OFFSET. 28 | 5. Test INDIRECT with a dropdown list of sheet names. 29 | 30 | ## Interview Tip 31 | Interviewers may test dynamic referencing. Practice OFFSET for ranges and INDIRECT for cross-sheet tasks. -------------------------------------------------------------------------------- /Excel Fundamentals/02 Formulas and Functions/Date_Functions.md: -------------------------------------------------------------------------------- 1 | # Date Functions in Excel 2 | 3 | ## Overview 4 | Date functions handle temporal data for analysis. This guide covers TODAY, NOW, and DATEDIF. 5 | 6 | ## Key Functions 7 | - **TODAY**: `=TODAY()` – Returns current date. 8 | - **NOW**: `=NOW()` – Returns current date and time. 9 | - **DATEDIF**: `=DATEDIF(start_date, end_date, unit)` – Calculates time difference (e.g., “d” for days). 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Track project deadlines. 13 | 1. **Current Date**: 14 | - In A1, enter `=TODAY()` to show today’s date. 15 | 2. **Current Date and Time**: 16 | - In A2, enter `=NOW()` to show date and time. 17 | 3. **Days Until Deadline**: 18 | - Data: Start date in B2, Deadline in C2. 19 | - In D2, enter `=DATEDIF(B2, C2, "d")` to calculate days. 20 | 21 | ## Practical Scenario 22 | You have employee hire dates and need to calculate years of service as of today, formatting dates as “MM/DD/YYYY”. 23 | 24 | ## Practice Tasks 25 | 1. Display today’s date in a cell. 26 | 2. Calculate days between two project dates using `DATEDIF`. 27 | 3. Format a date column as “DD-MMM-YYYY”. 28 | 4. Use `NOW` to track task submission times. 29 | 5. Calculate months of service for employees (use “m” in `DATEDIF`). 30 | 31 | ## Interview Tip 32 | Interviewers may ask for date calculations. Practice formatting dates and using `DATEDIF` for precise results. -------------------------------------------------------------------------------- /Advanced Excel Skills/Dashboards/Using_Slicers_and_Pivot_Charts.md: -------------------------------------------------------------------------------- 1 | # Using Slicers and Pivot Charts in Excel 2 | 3 | ## Overview 4 | Slicers and Pivot Charts add interactivity to dashboards, making data exploration user-friendly for interviews. 5 | 6 | ## Key Concepts 7 | - **Pivot Charts**: Charts based on Pivot Tables. 8 | - **Slicers**: Interactive filter buttons. 9 | - **Insert**: PivotTable Analyze > Insert Slicer/PivotChart. 10 | - **Connections**: Link Slicers to multiple Pivot Tables/Charts. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Add interactivity to a sales dashboard. 14 | 1. **Create Pivot Table**: 15 | - Data: Date, Region, Sales. 16 | - Insert Pivot Table with Region in Rows, Sales in Values. 17 | 2. **Add Pivot Chart**: 18 | - PivotTable Analyze > PivotChart > Line. 19 | 3. **Add Slicer**: 20 | - Insert Slicer > Date. 21 | - Connect Slicer to Pivot Table and Chart. 22 | 23 | ## Practical Scenario 24 | Create a dashboard with a Pivot Chart for sales trends and Slicers for region and product category. 25 | 26 | ## Practice Tasks 27 | 1. Add a Slicer to a Pivot Chart for categories. 28 | 2. Connect a Slicer to two Pivot Tables. 29 | 3. Create a Pivot Chart with a Timeline. 30 | 4. Customize Slicer appearance (e.g., colors). 31 | 5. Test Slicer filtering on multiple charts. 32 | 33 | ## Interview Tip 34 | Interviewers may test interactivity. Practice linking Slicers to charts and explaining filtering. -------------------------------------------------------------------------------- /Advanced Excel Skills/Data Analysis Tools/Solver_for_Optimization.md: -------------------------------------------------------------------------------- 1 | # Solver for Optimization in Excel 2 | 3 | ## Overview 4 | Solver optimizes solutions by adjusting variables, ideal for complex interview problems. 5 | 6 | ## Key Concepts 7 | - **Enable Solver**: Data > Solver (enable via File > Options > Add-ins). 8 | - **Components**: 9 | - Objective: Cell to optimize (max/min). 10 | - Variables: Cells to adjust. 11 | - Constraints: Limits on variables. 12 | - **Solve**: Find optimal solution. 13 | 14 | ## Step-by-Step Example 15 | **Scenario**: Maximize profit by adjusting product mix. 16 | 1. **Setup Model**: 17 | - A1:A2: Units of Product A and B. 18 | - B1:B2: Profit per unit. 19 | - C1: Total Profit (`=A1*B1+A2*B2`). 20 | - D1:D2: Resource constraints. 21 | 2. **Run Solver**: 22 | - Data > Solver. 23 | - Set Objective: C1 (Max), Variables: A1:A2, Constraints: D1:D2<=100. 24 | - Solve. 25 | 26 | ## Practical Scenario 27 | Optimize a budget allocation across three projects to maximize ROI under resource limits. 28 | 29 | ## Practice Tasks 30 | 1. Use Solver to maximize sales with budget constraints. 31 | 2. Minimize costs with fixed output. 32 | 3. Add multiple constraints (e.g., min/max units). 33 | 4. Test Solver with non-linear objectives. 34 | 5. Save and load Solver scenarios. 35 | 36 | ## Interview Tip 37 | Interviewers may test optimization. Practice setting up Solver models and explaining constraints. -------------------------------------------------------------------------------- /Excel Fundamentals/03 Data Management/Removing_Duplicates.md: -------------------------------------------------------------------------------- 1 | # Removing Duplicates in Excel 2 | 3 | ## Overview 4 | Removing duplicates cleans datasets by eliminating redundant entries, a common interview task. 5 | 6 | ## Key Concepts 7 | - **Remove Duplicates** (Data > Remove Duplicates): 8 | - Select columns to check for duplicates. 9 | - Keeps first occurrence, removes others. 10 | - **Case Sensitivity**: “Apple” and “apple” are treated as the same. 11 | - **Backup**: Copy data before removing duplicates. 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: Clean a customer list. 15 | 1. **Prepare Data**: 16 | - Columns: ID (A2:A100), Name (B2:B100). 17 | 2. **Remove Duplicates**: 18 | - Select A2:B100. 19 | - Data > Remove Duplicates > Check “ID” only. 20 | - Result: Duplicate IDs removed. 21 | 3. **Verify**: 22 | - Check row count before and after. 23 | 24 | ## Practical Scenario 25 | You have a dataset with repeated customer emails. Remove duplicates based on email while keeping other columns. 26 | 27 | ## Practice Tasks 28 | 1. Remove duplicate customer IDs from a list. 29 | 2. Clean a dataset by checking two columns (e.g., Name and Email). 30 | 3. Verify data integrity after removing duplicates. 31 | 4. Test case sensitivity with text data. 32 | 5. Combine with sorting for a clean dataset. 33 | 34 | ## Interview Tip 35 | Interviewers may ask for data cleaning. Practice removing duplicates and explaining the process. -------------------------------------------------------------------------------- /Advanced Excel Skills/Data Analysis Tools/Data_Tables_for_Sensitivity_Analysis.md: -------------------------------------------------------------------------------- 1 | # Data Tables for Sensitivity Analysis in Excel 2 | 3 | ## Overview 4 | Data Tables perform sensitivity analysis by testing multiple variable combinations, useful for interviews. 5 | 6 | ## Key Concepts 7 | - **Data Table**: Data > What-If Analysis > Data Table. 8 | - **Types**: 9 | - One-Variable: Test one input. 10 | - Two-Variable: Test two inputs. 11 | - **Setup**: Link to a formula model. 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: Analyze profit with varying sales and costs. 15 | 1. **Setup Model**: 16 | - B1: Sales, B2: Cost, B3: Profit (`=B1-B2`). 17 | 2. **Create Data Table**: 18 | - Column D2:D10: Sales values (1000, 2000, ...). 19 | - Row C1:J1: Cost values (500, 600, ...). 20 | - In C2, enter `=B3`. 21 | - Select C1:J10, Data > What-If Analysis > Data Table. 22 | - Row Input: B2, Column Input: B1. 23 | 24 | ## Practical Scenario 25 | Test loan payments with varying interest rates and terms using a two-variable Data Table. 26 | 27 | ## Practice Tasks 28 | 1. Create a one-variable Data Table for sales impact. 29 | 2. Build a two-variable Data Table for price and quantity. 30 | 3. Link a Data Table to a complex formula. 31 | 4. Format a Data Table for clarity. 32 | 5. Analyze Data Table results for trends. 33 | 34 | ## Interview Tip 35 | Interviewers may ask for sensitivity analysis. Practice Data Tables to show variable impacts. -------------------------------------------------------------------------------- /Advanced Excel Skills/VBA and Macros/Automating_Repetitive_Tasks.md: -------------------------------------------------------------------------------- 1 | # Automating Repetitive Tasks with VBA 2 | 3 | ## Overview 4 | VBA automates repetitive tasks, saving time and impressing interviewers with efficiency. 5 | 6 | ## Key Concepts 7 | - **Common Tasks**: Formatting, data cleaning, report generation. 8 | - **Loops**: Automate across ranges. 9 | - **Events**: Trigger macros on actions (e.g., worksheet change). 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Automate monthly report formatting. 13 | 1. **Write Script**: 14 | ```vba 15 | Sub FormatReport() 16 | With Range("A1:D1") 17 | .Font.Bold = True 18 | .Interior.Color = vbBlue 19 | End With 20 | Range("B2:B100").NumberFormat = "$#,##0.00" 21 | Range("A1:D100").Borders.Weight = xlMedium 22 | End Sub 23 | ``` 24 | 2. **Run Script**: 25 | - Developer > Macros > FormatReport > Run. 26 | 27 | ## Practical Scenario 28 | Automate a script to clean a dataset: remove duplicates, format numbers, and add headers. 29 | 30 | ## Practice Tasks 31 | 1. Automate formatting for a table (bold headers, currency format). 32 | 2. Write a script to remove blank rows. 33 | 3. Automate a data copy to a new sheet. 34 | 4. Create a macro to apply filters. 35 | 5. Link a macro to a button for easy access. 36 | 37 | ## Interview Tip 38 | Interviewers may ask for automation examples. Practice scripting repetitive tasks and explaining benefits. -------------------------------------------------------------------------------- /Excel Fundamentals/03 Data Management/Flash_Fill.md: -------------------------------------------------------------------------------- 1 | # Flash Fill in Excel 2 | 3 | ## Overview 4 | Flash Fill automates data formatting by recognizing patterns, saving time in data cleaning. 5 | 6 | ## Key Concepts 7 | - **Flash Fill** (Data > Flash Fill or Ctrl+E): 8 | - Detects patterns from examples. 9 | - Auto-fills remaining cells. 10 | - **Use Cases**: Extract names, format dates, combine text. 11 | - **Manual Trigger**: Ctrl+E or Data > Flash Fill. 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: Extract first names from full names. 15 | 1. **Prepare Data**: 16 | - Column A: Full names (e.g., “John Doe” in A2:A10). 17 | 2. **Start Flash Fill**: 18 | - In B2, type “John” (first name from A2). 19 | - In B3, start typing “Jane”; press Ctrl+E. 20 | - Result: B2:B10 filled with first names. 21 | 3. **Combine Text**: 22 | - In C2, type “J. Doe” for “John Doe”. 23 | - Press Ctrl+E to fill C3:C10. 24 | 25 | ## Practical Scenario 26 | You have a column of dates in “YYYYMMDD” format. Use Flash Fill to reformat as “MM/DD/YYYY”. 27 | 28 | ## Practice Tasks 29 | 1. Extract last names from full names. 30 | 2. Format phone numbers as (XXX) XXX-XXXX. 31 | 3. Combine first and last names with a comma. 32 | 4. Reformat dates from “DDMMYYYY” to “MM-DD-YYYY”. 33 | 5. Use Flash Fill to standardize product codes. 34 | 35 | ## Interview Tip 36 | Interviewers may test automation skills. Practice Flash Fill for quick data formatting tasks. -------------------------------------------------------------------------------- /Advanced Excel Skills/Array Formulas/Dynamic_Array_Formulas.md: -------------------------------------------------------------------------------- 1 | # Dynamic Array Formulas in Excel 2 | 3 | ## Overview 4 | Dynamic Array Formulas (FILTER, SORT, UNIQUE) automatically spill results, simplifying advanced calculations for interviews. 5 | 6 | ## Key Functions 7 | - **FILTER**: `=FILTER(A2:B100, B2:B100>1000)` – Filter data by condition. 8 | - **SORT**: `=SORT(A2:A100)` – Sort data dynamically. 9 | - **UNIQUE**: `=UNIQUE(A2:A100)` – Extract unique values. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Filter and sort high sales. 13 | 1. **FILTER**: 14 | - Data: Product (A2:A100), Sales (B2:B100). 15 | - In C2, enter `=FILTER(A2:B100, B2:B100>1000)`. 16 | - Result: Spills products with sales >1000. 17 | 2. **SORT**: 18 | - In E2, enter `=SORT(B2:B100, 1, -1)` for descending order. 19 | 3. **UNIQUE**: 20 | - In G2, enter `=UNIQUE(A2:A100)` for unique products. 21 | 22 | ## Practical Scenario 23 | You have a dataset of employees (name, department, salary). Use FILTER for salaries >$50,000, SORT by salary, and UNIQUE for departments. 24 | 25 | ## Practice Tasks 26 | 1. Use FILTER to show sales >500 in a region. 27 | 2. Sort a column of dates dynamically. 28 | 3. Extract unique customer IDs with UNIQUE. 29 | 4. Combine FILTER and SORT for a sorted filtered list. 30 | 5. Test dynamic arrays with changing data. 31 | 32 | ## Interview Tip 33 | Interviewers may test modern formulas. Practice dynamic arrays for efficient data manipulation. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Charts and Visualization/Custom_Chart_Formatting.md: -------------------------------------------------------------------------------- 1 | # Custom Chart Formatting in Excel 2 | 3 | ## Overview 4 | Custom chart formatting enhances visual appeal and clarity, a key skill for professional reports and interviews. 5 | 6 | ## Key Concepts 7 | - **Elements**: Chart Title, Axis Titles, Data Labels, Legend. 8 | - **Formatting**: Colors, fonts, gridlines, and styles. 9 | - **Access**: Right-click chart elements or use Chart Tools > Design/Format. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Format a sales bar chart. 13 | 1. **Create Chart**: 14 | - Data: Region (A2:A6), Sales (B2:B6). 15 | - Insert > Bar Chart. 16 | 2. **Customize**: 17 | - Add Chart Title: “Regional Sales”. 18 | - Enable Data Labels (Chart Elements > Data Labels). 19 | - Change bar color to blue (Format > Shape Fill). 20 | - Adjust font size of axis labels to 12. 21 | 22 | ## Practical Scenario 23 | You have a dataset of product sales. Create a bar chart with custom colors, data labels, and a professional title. 24 | 25 | ## Practice Tasks 26 | 1. Customize a chart title and axis labels. 27 | 2. Change chart colors to match a company theme. 28 | 3. Add and format data labels for clarity. 29 | 4. Remove gridlines for a cleaner look. 30 | 5. Create a chart with a custom legend position. 31 | 32 | ## Interview Tip 33 | Interviewers may ask for polished visuals. Practice formatting charts to look professional and explain design choices. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Lookup and Reference/INDEX_and_MATCH.md: -------------------------------------------------------------------------------- 1 | # INDEX and MATCH in Excel 2 | 3 | ## Overview 4 | INDEX and MATCH offer a flexible alternative to VLOOKUP, ideal for complex lookups in interviews. 5 | 6 | ## Key Functions 7 | - **INDEX**: `=INDEX(array, row_num, [col_num])` – Returns value at specified position. 8 | - **MATCH**: `=MATCH(lookup_value, lookup_array, [match_type])` – Finds position of a value. 9 | - **Combined**: `=INDEX(B2:B10, MATCH(D2, A2:A10, 0))` – Dynamic lookup. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Find product prices by ID. 13 | 1. **Prepare Data**: 14 | - Columns: ID (A2:A10), Price (B2:B10). 15 | 2. **MATCH**: 16 | - In E2, enter `=MATCH(D2, A2:A10, 0)` to find row of ID in D2. 17 | 3. **INDEX**: 18 | - In F2, enter `=INDEX(B2:B10, E2)` to get price. 19 | 4. **Combine**: 20 | - In G2, enter `=INDEX(B2:B10, MATCH(D2, A2:A10, 0))`. 21 | 22 | ## Practical Scenario 23 | You have a dataset with employee IDs, names, and salaries. Use INDEX and MATCH to retrieve salaries by ID. 24 | 25 | ## Practice Tasks 26 | 1. Use INDEX and MATCH to find product names by code. 27 | 2. Perform a two-way lookup (row and column). 28 | 3. Handle errors with `IFERROR`. 29 | 4. Compare VLOOKUP vs. INDEX/MATCH performance. 30 | 5. Use MATCH with approximate match type (1 or -1). 31 | 32 | ## Interview Tip 33 | Interviewers may prefer INDEX/MATCH for flexibility. Practice explaining why it’s more versatile than VLOOKUP. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Pivot Tables/Grouping_Data.md: -------------------------------------------------------------------------------- 1 | # Grouping Data in Pivot Tables 2 | 3 | ## Overview 4 | Grouping in Pivot Tables organizes data by dates or categories for better analysis. 5 | 6 | ## Key Concepts 7 | - **Date Grouping**: Group by years, quarters, months. 8 | - **Category Grouping**: Group text or numbers into custom ranges. 9 | - **Ungroup**: Right-click > Ungroup to revert. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Group sales data by quarter and product type. 13 | 1. **Create Pivot Table**: 14 | - Data: Date (A), Product (B), Sales (C). 15 | - Insert Pivot Table with Date in Rows, Product in Columns, Sales in Values. 16 | 2. **Group by Quarter**: 17 | - Right-click a date in the Pivot Table > Group > Quarters. 18 | 3. **Group by Custom Category**: 19 | - Add a new column for Sales Range (e.g., High/Low). 20 | - Group by this column in the Pivot Table. 21 | 22 | ## Practical Scenario 23 | You have sales data with dates and regions. Group by month and create custom groups for sales (<500, 500-1000, >1000). 24 | 25 | ## Practice Tasks 26 | 1. Group sales data by year and month. 27 | 2. Create custom groups for employee ages (e.g., 20-30, 31-40). 28 | 3. Group dates by quarters and years. 29 | 4. Ungroup and reapply different groupings. 30 | 5. Combine date and category grouping in one Pivot Table. 31 | 32 | ## Interview Tip 33 | Interviewers may test data aggregation. Practice grouping and explaining summarized results. -------------------------------------------------------------------------------- /Excel Fundamentals/03 Data Management/Freeze_Panes.md: -------------------------------------------------------------------------------- 1 | # Freeze Panes in Excel 2 | 3 | ## Overview 4 | Freeze Panes keeps headers visible while scrolling, essential for large datasets. This guide covers freezing rows and columns. 5 | 6 | ## Key Concepts 7 | - **Freeze Panes** (View > Freeze Panes): 8 | - Freeze Top Row: Locks first row. 9 | - Freeze First Column: Locks first column. 10 | - Freeze Custom: Locks rows and columns above/left of selected cell. 11 | - **Unfreeze**: View > Freeze Panes > Unfreeze Panes. 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: View headers in a large sales dataset. 15 | 1. **Freeze Top Row**: 16 | - View > Freeze Panes > Freeze Top Row. 17 | - Scroll down; row 1 stays visible. 18 | 2. **Freeze First Column**: 19 | - View > Freeze Panes > Freeze First Column. 20 | 3. **Custom Freeze**: 21 | - Select B2. 22 | - View > Freeze Panes > Freeze Panes (locks row 1 and column A). 23 | 24 | ## Practical Scenario 25 | You have a dataset with 50 rows and 20 columns. Freeze the header row and ID column to keep them visible while scrolling. 26 | 27 | ## Practice Tasks 28 | 1. Freeze the top row of a 100-row dataset. 29 | 2. Freeze the first column of a wide table. 30 | 3. Freeze both row 1 and column A. 31 | 4. Unfreeze and reapply a different freeze setting. 32 | 5. Test scrolling with frozen panes. 33 | 34 | ## Interview Tip 35 | Interviewers may ask you to navigate large datasets. Practice freezing panes to show data awareness. -------------------------------------------------------------------------------- /Excel Fundamentals/02 Formulas and Functions/Lookup_Functions.md: -------------------------------------------------------------------------------- 1 | # Lookup Functions in Excel 2 | 3 | ## Overview 4 | Lookup functions retrieve data from tables. This guide covers VLOOKUP and HLOOKUP. 5 | 6 | ## Key Functions 7 | - **VLOOKUP**: `=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])` – Vertical lookup. 8 | - **HLOOKUP**: `=HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup])` – Horizontal lookup. 9 | - **Range Lookup**: `FALSE` for exact match, `TRUE` for approximate. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Find product prices in a table. 13 | 1. **Setup Data**: 14 | - Table: Product ID (A2:A10), Price (B2:B10). 15 | 2. **VLOOKUP**: 16 | - In C2, enter `=VLOOKUP(D2, A2:B10, 2, FALSE)` to find price for ID in D2. 17 | 3. **HLOOKUP**: 18 | - Table: Months (B1:M1), Sales (B2:M2). 19 | - In B3, enter `=HLOOKUP("Jan", B1:M2, 2, FALSE)` to get January sales. 20 | 21 | ## Practical Scenario 22 | You have a product catalog with IDs and prices. Use `VLOOKUP` to retrieve prices for a list of ordered IDs. 23 | 24 | ## Practice Tasks 25 | 1. Use `VLOOKUP` to find employee salaries by ID. 26 | 2. Apply `HLOOKUP` to get sales for a specific month. 27 | 3. Test exact vs. approximate matches in `VLOOKUP`. 28 | 4. Handle errors with `IFERROR` in lookups. 29 | 5. Combine `VLOOKUP` with another function (e.g., `SUM`). 30 | 31 | ## Interview Tip 32 | Interviewers may test lookup accuracy. Practice exact matches and handling missing data. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Lookup and Reference/VLOOKUP_and_HLOOKUP.md: -------------------------------------------------------------------------------- 1 | # VLOOKUP and HLOOKUP in Excel 2 | 3 | ## Overview 4 | VLOOKUP and HLOOKUP retrieve data from tables, widely used in data analysis and interviews. 5 | 6 | ## Key Functions 7 | - **VLOOKUP**: `=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])` – Vertical lookup. 8 | - **HLOOKUP**: `=HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup])` – Horizontal lookup. 9 | - **Range Lookup**: `FALSE` for exact match, `TRUE` for approximate. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Find product prices and monthly sales. 13 | 1. **VLOOKUP**: 14 | - Data: Product ID (A2:A10), Price (B2:B10). 15 | - In C2, enter `=VLOOKUP(D2, A2:B10, 2, FALSE)` to find price for ID in D2. 16 | 2. **HLOOKUP**: 17 | - Data: Months (B1:M1), Sales (B2:M2). 18 | - In B3, enter `=HLOOKUP("Jan", B1:M2, 2, FALSE)` to get January sales. 19 | 20 | ## Practical Scenario 21 | You have a product catalog (ID, Name, Price) and sales data by month. Use VLOOKUP to get prices and HLOOKUP for monthly totals. 22 | 23 | ## Practice Tasks 24 | 1. Use VLOOKUP to find employee names by ID. 25 | 2. Apply HLOOKUP to get quarterly sales. 26 | 3. Test exact vs. approximate matches. 27 | 4. Handle errors with `IFERROR(VLOOKUP(...), "Not Found")`. 28 | 5. Combine VLOOKUP with SUM for total price of multiple items. 29 | 30 | ## Interview Tip 31 | Interviewers may test lookup accuracy. Practice exact matches and error handling. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Pivot Tables/Slicers_and_Timelines.md: -------------------------------------------------------------------------------- 1 | # Slicers and Timelines in Pivot Tables 2 | 3 | ## Overview 4 | Slicers and Timelines add interactivity to Pivot Tables, making filtering intuitive. 5 | 6 | ## Key Concepts 7 | - **Slicers**: Interactive buttons to filter by categories. 8 | - **Timelines**: Filter by date ranges (e.g., months, years). 9 | - **Insert**: PivotTable Analyze > Insert Slicer/Timeline. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Filter sales data by region and date. 13 | 1. **Create Pivot Table**: 14 | - Data: Region (A), Date (B), Sales (C). 15 | - Insert Pivot Table with Region in Rows, Sales in Values. 16 | 2. **Add Slicer**: 17 | - PivotTable Analyze > Insert Slicer > Region. 18 | - Click buttons to filter (e.g., “North”). 19 | 3. **Add Timeline**: 20 | - PivotTable Analyze > Insert Timeline > Date. 21 | - Select date range (e.g., Q1 2025). 22 | 23 | ## Practical Scenario 24 | You have a Pivot Table with sales by product and date. Add Slicers for product categories and a Timeline for 2024-2025. 25 | 26 | ## Practice Tasks 27 | 1. Add a Slicer for departments in a Pivot Table. 28 | 2. Use a Timeline to filter sales by quarter. 29 | 3. Combine multiple Slicers (e.g., Region and Product). 30 | 4. Customize Slicer appearance (e.g., colors). 31 | 5. Clear Slicer and Timeline filters. 32 | 33 | ## Interview Tip 34 | Interviewers may ask for interactive reports. Practice using Slicers and Timelines to filter data dynamically. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Pivot Tables/Creating_Pivot_Tables.md: -------------------------------------------------------------------------------- 1 | # Creating Pivot Tables in Excel 2 | 3 | ## Overview 4 | Pivot Tables summarize large datasets by aggregating and organizing data, a must-know for interviews. 5 | 6 | ## Key Concepts 7 | - **Insert Pivot Table**: Insert > PivotTable. 8 | - **Fields**: Drag columns to Rows, Columns, Values, or Filters. 9 | - **Value Settings**: Sum, Count, Average, etc. 10 | - **Layout**: Compact, Outline, or Tabular. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Summarize sales by region and product. 14 | 1. **Prepare Data**: 15 | - Columns: Region (A), Product (B), Sales (C). 16 | 2. **Insert Pivot Table**: 17 | - Select A1:C100. 18 | - Insert > PivotTable > New Worksheet > OK. 19 | 3. **Configure**: 20 | - Drag Region to Rows, Product to Columns, Sales to Values. 21 | - Set Values to Sum of Sales. 22 | 4. **Format**: Choose Tabular layout, enable Grand Totals. 23 | 24 | ## Practical Scenario 25 | You have a dataset with sales, dates, and categories. Create a Pivot Table to show total sales by category and month. 26 | 27 | ## Practice Tasks 28 | 1. Create a Pivot Table for sales by employee. 29 | 2. Summarize expenses by department and quarter. 30 | 3. Change Value setting to Average instead of Sum. 31 | 4. Add a filter for a specific region. 32 | 5. Format a Pivot Table with banded rows. 33 | 34 | ## Interview Tip 35 | Interviewers may ask you to summarize data. Practice building Pivot Tables quickly and explaining insights. -------------------------------------------------------------------------------- /Advanced Excel Skills/Dashboards/Visual_Best_Practices.md: -------------------------------------------------------------------------------- 1 | # Visual Best Practices for Dashboards 2 | 3 | ## Overview 4 | Effective dashboard design ensures clarity and professionalism, critical for interviews. 5 | 6 | ## Key Concepts 7 | - **Clarity**: Use clear titles, labels, and legends. 8 | - **Consistency**: Uniform colors, fonts, and layouts. 9 | - **Simplicity**: Avoid clutter, focus on key insights. 10 | - **Interactivity**: Use Slicers and dynamic charts. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Design a professional sales dashboard. 14 | 1. **Layout**: 15 | - Place title at top: “Sales Performance Dashboard”. 16 | - Arrange charts and tables logically. 17 | 2. **Formatting**: 18 | - Use a consistent color scheme (e.g., blue tones). 19 | - Set font to Calibri, size 12 for labels. 20 | 3. **Interactivity**: 21 | - Add Slicers for region and date. 22 | - Ensure charts update dynamically. 23 | 24 | ## Practical Scenario 25 | Design a dashboard for sales by region with a clean layout, consistent colors, and Slicers for interactivity. 26 | 27 | ## Practice Tasks 28 | 1. Design a dashboard with a professional color scheme. 29 | 2. Add clear titles and labels to all visuals. 30 | 3. Remove unnecessary gridlines and clutter. 31 | 4. Test dashboard with different Slicer selections. 32 | 5. Create a dashboard with three charts and two Slicers. 33 | 34 | ## Interview Tip 35 | Interviewers may evaluate dashboard aesthetics. Practice designing clean, interactive dashboards. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Pivot Tables/Calculated_Fields.md: -------------------------------------------------------------------------------- 1 | # Calculated Fields in Pivot Tables 2 | 3 | ## Overview 4 | Calculated Fields add custom calculations to Pivot Tables, enhancing analysis capabilities. 5 | 6 | ## Key Concepts 7 | - **Insert Calculated Field**: PivotTable Analyze > Fields, Items & Sets > Calculated Field. 8 | - **Formula**: Use existing fields (e.g., `=Sales*0.1` for 10% commission). 9 | - **Limitations**: Cannot use cell references, only Pivot Table fields. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Add a commission field (10% of sales). 13 | 1. **Create Pivot Table**: 14 | - Data: Product (A), Sales (B). 15 | - Insert Pivot Table with Product in Rows, Sales in Values. 16 | 2. **Add Calculated Field**: 17 | - PivotTable Analyze > Fields, Items & Sets > Calculated Field. 18 | - Name: “Commission”, Formula: `=Sales*0.1`. 19 | - Click Add, then OK. 20 | 3. **View Results**: New column shows commission values. 21 | 22 | ## Practical Scenario 23 | You have sales and cost data. Add a Calculated Field for profit (Sales - Cost) in a Pivot Table. 24 | 25 | ## Practice Tasks 26 | 1. Add a 5% tax field to a sales Pivot Table. 27 | 2. Calculate profit margin (Profit/Sales) in a Pivot Table. 28 | 3. Create a bonus field (Sales >1000 get $50). 29 | 4. Test multiple Calculated Fields in one Pivot Table. 30 | 5. Update a Calculated Field formula. 31 | 32 | ## Interview Tip 33 | Interviewers may ask for custom calculations. Practice creating and explaining Calculated Fields. -------------------------------------------------------------------------------- /Advanced Excel Skills/Dashboards/Building_Interactive_Dashboards.md: -------------------------------------------------------------------------------- 1 | # Building Interactive Dashboards in Excel 2 | 3 | ## Overview 4 | Interactive dashboards combine charts, tables, and filters to present data dynamically, a key skill for interviews. 5 | 6 | ## Key Concepts 7 | - **Components**: Pivot Tables, Pivot Charts, Slicers. 8 | - **Layout**: Organize visuals on a single sheet. 9 | - **Interactivity**: Use Slicers for user-friendly filtering. 10 | - **Design**: Clear titles, consistent colors. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Build a sales dashboard. 14 | 1. **Create Pivot Table**: 15 | - Data: Region, Product, Sales. 16 | - Insert Pivot Table with Region in Rows, Sales in Values. 17 | 2. **Add Pivot Chart**: 18 | - PivotTable Analyze > PivotChart > Bar. 19 | 3. **Add Slicer**: 20 | - PivotTable Analyze > Insert Slicer > Product. 21 | 4. **Format**: 22 | - Add title: “Sales Dashboard”. 23 | - Arrange visuals cleanly. 24 | 25 | ## Practical Scenario 26 | Create a dashboard showing sales by region and category with a bar chart and Slicers for filtering. 27 | 28 | ## Practice Tasks 29 | 1. Build a dashboard with two Pivot Charts. 30 | 2. Add Slicers for multiple fields (e.g., Region, Date). 31 | 3. Format a dashboard with a company color scheme. 32 | 4. Include a summary table in the dashboard. 33 | 5. Test interactivity with different filters. 34 | 35 | ## Interview Tip 36 | Interviewers may ask for dynamic reports. Practice building dashboards and explaining insights. -------------------------------------------------------------------------------- /Advanced Excel Skills/Power Query/Automating_Data_Refresh.md: -------------------------------------------------------------------------------- 1 | # Automating Data Refresh in Power Query 2 | 3 | ## Overview 4 | Automating data refresh in Power Query saves time by updating datasets automatically, a valuable skill for interviews. 5 | 6 | ## Key Concepts 7 | - **Refresh**: Data > Refresh All updates queries. 8 | - **Connection Properties**: Set refresh intervals or manual refresh. 9 | - **Data Sources**: Ensure source files/databases are accessible. 10 | - **Load Settings**: Optimize for large datasets. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Automate refresh for a monthly sales report. 14 | 1. **Create Query**: 15 | - Import `sales.csv` via Power Query. 16 | - Transform: Filter for 2025, group by month. 17 | 2. **Set Refresh**: 18 | - Data > Queries & Connections > Right-click query > Properties. 19 | - Enable “Refresh every 60 minutes” or “Refresh on open”. 20 | 3. **Test Refresh**: 21 | - Update `sales.csv`, click Data > Refresh All. 22 | 23 | ## Practical Scenario 24 | You have a query pulling data from a shared folder. Set it to refresh automatically when the workbook opens. 25 | 26 | ## Practice Tasks 27 | 1. Set a query to refresh every 30 minutes. 28 | 2. Configure a query to refresh on workbook open. 29 | 3. Test refresh with an updated CSV file. 30 | 4. Combine multiple queries with automatic refresh. 31 | 5. Optimize refresh for a large dataset. 32 | 33 | ## Interview Tip 34 | Interviewers may ask about automation. Practice setting refresh options and explaining benefits. -------------------------------------------------------------------------------- /Advanced Excel Skills/Power Query/Creating_Custom_Columns.md: -------------------------------------------------------------------------------- 1 | # Creating Custom Columns in Power Query 2 | 3 | ## Overview 4 | Custom columns in Power Query add calculated fields to datasets, enhancing analysis capabilities for interviews. 5 | 6 | ## Key Concepts 7 | - **Add Column**: Home > Add Column > Custom Column. 8 | - **Formula**: Use Power Query’s M language (e.g., `[Sales] * 0.1`). 9 | - **Common Uses**: Calculations, conditional logic, text manipulation. 10 | - **Preview**: See results in Query Editor. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Add a profit column to sales data. 14 | 1. **Import Data**: 15 | - Load `sales.csv` (Product, Sales, Cost). 16 | 2. **Add Custom Column**: 17 | - In Query Editor, Add Column > Custom Column. 18 | - Name: “Profit”, Formula: `[Sales] - [Cost]`. 19 | 3. **Conditional Column**: 20 | - Add Column > Conditional Column. 21 | - Name: “Status”, If `[Profit] > 0` then “Positive” else “Negative”. 22 | 23 | ## Practical Scenario 24 | You have sales data with quantities and prices. Add a custom column for total revenue and another for high/low sales status. 25 | 26 | ## Practice Tasks 27 | 1. Create a custom column for 10% commission. 28 | 2. Add a conditional column for sales >1000. 29 | 3. Calculate a custom date column (e.g., add 30 days). 30 | 4. Combine text columns in a custom column. 31 | 5. Test custom columns with complex logic. 32 | 33 | ## Interview Tip 34 | Interviewers may test custom calculations. Practice creating and explaining custom columns. -------------------------------------------------------------------------------- /Excel Fundamentals/02 Formulas and Functions/Text_Functions.md: -------------------------------------------------------------------------------- 1 | # Text Functions in Excel 2 | 3 | ## Overview 4 | Text functions manipulate strings for data cleaning. This guide covers LEFT, RIGHT, and CONCATENATE. 5 | 6 | ## Key Functions 7 | - **LEFT**: `=LEFT(text, num_chars)` – Extracts characters from the start. 8 | - **RIGHT**: `=RIGHT(text, num_chars)` – Extracts characters from the end. 9 | - **CONCATENATE**: `=CONCATENATE(text1, text2, ...)` – Joins text strings. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Clean and combine customer names. 13 | 1. **Extract First Name**: 14 | - Data: Full names in A2:A10 (e.g., “John Doe”). 15 | - In B2, enter `=LEFT(A2, FIND(" ", A2)-1)` to get “John”. 16 | 2. **Extract Last Name**: 17 | - In C2, enter `=RIGHT(A2, LEN(A2)-FIND(" ", A2))` to get “Doe”. 18 | 3. **Combine Names**: 19 | - In D2, enter `=CONCATENATE(B2, " ", C2)` to get “John Doe”. 20 | 21 | ## Practical Scenario 22 | You have a column of email addresses (e.g., john.doe@company.com). Extract usernames and domains, then combine with a custom suffix. 23 | 24 | ## Practice Tasks 25 | 1. Extract the first 3 characters of product codes. 26 | 2. Get the last 4 digits of phone numbers using `RIGHT`. 27 | 3. Combine city and state with a comma using `CONCATENATE`. 28 | 4. Use `LEFT` and `FIND` to extract area codes. 29 | 5. Clean inconsistent text data (e.g., trim extra spaces). 30 | 31 | ## Interview Tip 32 | Interviewers may test text manipulation. Practice combining functions like `FIND` with `LEFT` for complex tasks. -------------------------------------------------------------------------------- /Advanced Excel Skills/Data Analysis Tools/What_If_Analysis.md: -------------------------------------------------------------------------------- 1 | # What-If Analysis in Excel 2 | 3 | ## Overview 4 | What-If Analysis, including Goal Seek and Scenario Manager, explores data outcomes, a key skill for interviews. 5 | 6 | ## Key Concepts 7 | - **Goal Seek**: Data > What-If Analysis > Goal Seek. 8 | - Adjusts a variable to achieve a target value. 9 | - **Scenario Manager**: Data > What-If Analysis > Scenario Manager. 10 | - Compares multiple input scenarios. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Find sales needed for a target profit. 14 | 1. **Goal Seek**: 15 | - Cell B1: Sales, B2: Cost, B3: Profit (`=B1-B2`). 16 | - Data > What-If Analysis > Goal Seek. 17 | - Set Cell: B3, To Value: 1000, By Changing Cell: B1. 18 | 2. **Scenario Manager**: 19 | - Data > What-If Analysis > Scenario Manager. 20 | - Add scenarios: “High Sales” (B1=5000), “Low Sales” (B1=2000). 21 | - Show or summarize scenarios. 22 | 23 | ## Practical Scenario 24 | You have a loan model (principal, rate, term). Use Goal Seek to find the rate for a $500 monthly payment and Scenario Manager to compare terms. 25 | 26 | ## Practice Tasks 27 | 1. Use Goal Seek to find units sold for $10,000 revenue. 28 | 2. Create three scenarios for different budget levels. 29 | 3. Summarize scenarios in a report. 30 | 4. Combine Goal Seek with a formula model. 31 | 5. Test Scenario Manager with multiple variables. 32 | 33 | ## Interview Tip 34 | Interviewers may ask for predictive analysis. Practice Goal Seek and Scenario Manager for quick solutions. -------------------------------------------------------------------------------- /Excel Fundamentals/03 Data Management/Splitting_Text_to_Columns.md: -------------------------------------------------------------------------------- 1 | # Splitting Text to Columns in Excel 2 | 3 | ## Overview 4 | Text to Columns separates combined data into multiple columns, useful for data cleaning. 5 | 6 | ## Key Concepts 7 | - **Text to Columns** (Data > Text to Columns): 8 | - **Delimited**: Split by commas, spaces, tabs, etc. 9 | - **Fixed Width**: Split at specific positions. 10 | - **Common Delimiters**: Comma, space, tab, semicolon. 11 | - **Preview**: See split results before applying. 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: Split full names into first and last names. 15 | 1. **Prepare Data**: 16 | - Column A: Names (e.g., “John Doe” in A2:A10). 17 | 2. **Split by Delimiter**: 18 | - Select A2:A10. 19 | - Data > Text to Columns > Delimited > Space > Finish. 20 | - Result: First names in A, last names in B. 21 | 3. **Fixed Width**: 22 | - For fixed-format codes (e.g., “123-ABC”). 23 | - Data > Text to Columns > Fixed Width > Set break at position 3. 24 | 25 | ## Practical Scenario 26 | You have addresses in one column (e.g., “123 Main St, NY”). Split into street and city using a comma delimiter. 27 | 28 | ## Practice Tasks 29 | 1. Split “City,State” into two columns. 30 | 2. Split product codes (e.g., “A123-B”) using a hyphen. 31 | 3. Use fixed width to split “MMDDYYYY” dates. 32 | 4. Split email addresses at “@”. 33 | 5. Clean a dataset with inconsistent delimiters. 34 | 35 | ## Interview Tip 36 | Interviewers may test data cleaning. Practice splitting text and verifying results. -------------------------------------------------------------------------------- /Advanced Excel Skills/VBA and Macros/Working_with_Loops_and_Conditions.md: -------------------------------------------------------------------------------- 1 | # Working with Loops and Conditions in VBA 2 | 3 | ## Overview 4 | Loops and conditions in VBA add logic to scripts, essential for complex automation in interviews. 5 | 6 | ## Key Concepts 7 | - **Loops**: 8 | - `For Each`: Iterate over ranges. 9 | - `For`: Fixed iterations. 10 | - `Do While`: Condition-based loops. 11 | - **Conditions**: `If`, `ElseIf`, `Else` for decision-making. 12 | - **Variables**: Store temporary data. 13 | 14 | ## Step-by-Step Example 15 | **Scenario**: Flag sales >1000 in a column. 16 | 1. **Write Script**: 17 | ```vba 18 | Sub FlagHighSales() 19 | Dim cell As Range 20 | For Each cell In Range("B2:B100") 21 | If cell.Value > 1000 Then 22 | cell.Offset(0, 1).Value = "High" 23 | Else 24 | cell.Offset(0, 1).Value = "Low" 25 | End If 26 | Next cell 27 | End Sub 28 | ``` 29 | 2. **Run Script**: 30 | - Developer > Macros > FlagHighSales > Run. 31 | 32 | ## Practical Scenario 33 | Write a script to loop through a dataset and highlight negative values in red. 34 | 35 | ## Practice Tasks 36 | 1. Use a `For` loop to number rows 1-50. 37 | 2. Write a `For Each` loop to format cells >500. 38 | 3. Create a `Do While` loop to process data until empty. 39 | 4. Combine `If` with loops to categorize data. 40 | 5. Debug a loop with incorrect conditions. 41 | 42 | ## Interview Tip 43 | Interviewers may test VBA logic. Practice writing loops and conditions for data processing. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Charts and Visualization/Combo_Charts.md: -------------------------------------------------------------------------------- 1 | # Combo Charts in Excel 2 | 3 | ## Overview 4 | Combo Charts combine multiple chart types (e.g., column and line) to show different data series, ideal for interviews requiring multi-dimensional analysis. 5 | 6 | ## Key Concepts 7 | - **Combo Chart**: Insert > Combo Chart. 8 | - **Chart Types**: Combine column, line, or area charts. 9 | - **Secondary Axis**: Use for different scales. 10 | - **Customization**: Adjust titles, axes, and series. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Show sales and profit margin in one chart. 14 | 1. **Prepare Data**: 15 | - Columns: Month (A2:A13), Sales (B2:B13), Profit Margin % (C2:C13). 16 | 2. **Insert Combo Chart**: 17 | - Select A1:C13. 18 | - Insert > Combo Chart > Clustered Column - Line on Secondary Axis. 19 | 3. **Customize**: 20 | - Sales as columns, Profit Margin as line. 21 | - Add chart title: “Sales vs. Profit Margin”. 22 | - Enable Data Labels for clarity. 23 | 24 | ## Practical Scenario 25 | You have revenue and growth rate data. Create a Combo Chart with revenue as columns and growth rate as a line on a secondary axis. 26 | 27 | ## Practice Tasks 28 | 1. Create a Combo Chart for sales and costs. 29 | 2. Use a secondary axis for percentage data. 30 | 3. Combine bar and area charts for two metrics. 31 | 4. Add data labels and customize colors. 32 | 5. Create a Combo Chart with three data series. 33 | 34 | ## Interview Tip 35 | Interviewers may ask for multi-series visualizations. Practice explaining the relationship between charted metrics. -------------------------------------------------------------------------------- /Advanced Excel Skills/Power Query/Importing_Data.md: -------------------------------------------------------------------------------- 1 | # Importing Data in Power Query 2 | 3 | ## Overview 4 | Power Query simplifies importing data from various sources like CSV files, databases, and web sources, a key skill for advanced Excel users and interviews. 5 | 6 | ## Key Concepts 7 | - **Access Power Query**: Data > Get Data. 8 | - **Sources**: 9 | - Files: CSV, Excel, Text. 10 | - Databases: SQL Server, Access. 11 | - Other: Web, JSON. 12 | - **Query Editor**: Preview and transform data before loading. 13 | - **Load Options**: Load to table, data model, or connection only. 14 | 15 | ## Step-by-Step Example 16 | **Scenario**: Import sales data from a CSV file. 17 | 1. **Open Power Query**: 18 | - Data > Get Data > From File > From CSV. 19 | 2. **Select File**: 20 | - Choose `sales_data.csv` (columns: Date, Product, Sales). 21 | 3. **Preview Data**: 22 | - In Query Editor, review data structure. 23 | 4. **Load Data**: 24 | - Click Load to create a new table in Excel. 25 | 26 | ## Practical Scenario 27 | You need to combine sales data from two CSV files (Q1 and Q2). Import both into Power Query and load them as a single table. 28 | 29 | ## Practice Tasks 30 | 1. Import a CSV file with customer data. 31 | 2. Import data from an Excel workbook with multiple sheets. 32 | 3. Connect to a sample SQL database (if available). 33 | 4. Import web data (e.g., a public dataset). 34 | 5. Load data to the Data Model instead of a table. 35 | 36 | ## Interview Tip 37 | Interviewers may ask about data integration. Practice importing multiple sources and explaining the process. -------------------------------------------------------------------------------- /Advanced Excel Skills/VBA and Macros/Writing_VBA_Scripts.md: -------------------------------------------------------------------------------- 1 | # Writing VBA Scripts in Excel 2 | 3 | ## Overview 4 | Writing VBA scripts allows custom automation, a key skill for advanced Excel interviews. 5 | 6 | ## Key Concepts 7 | - **VBA Editor**: Developer > Visual Basic. 8 | - **Structure**: Subroutines (`Sub`), variables, and objects. 9 | - **Common Objects**: 10 | - `Range`: Access cells (e.g., `Range("A1")`). 11 | - `Worksheet`: Access sheets (e.g., `Worksheets("Sheet1")`). 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: Highlight cells >1000. 15 | 1. **Open VBA Editor**: 16 | - Developer > Visual Basic. 17 | 2. **Insert Module**: 18 | - Insert > Module. 19 | 3. **Write Code**: 20 | ```vba 21 | Sub HighlightHighSales() 22 | Dim cell As Range 23 | For Each cell In Range("B2:B100") 24 | If cell.Value > 1000 Then 25 | cell.Interior.Color = vbYellow 26 | End If 27 | Next cell 28 | End Sub 29 | ``` 30 | 4. **Run Script**: 31 | - Developer > Macros > HighlightHighSales > Run. 32 | 33 | ## Practical Scenario 34 | Write a VBA script to format all negative values in a column with red font. 35 | 36 | ## Practice Tasks 37 | 1. Write a script to bold a range (A1:A10). 38 | 2. Create a script to copy data to another sheet. 39 | 3. Clear formatting in a range using VBA. 40 | 4. Write a script to insert a new row. 41 | 5. Test a script with error handling (`On Error Resume Next`). 42 | 43 | ## Interview Tip 44 | Interviewers may ask for custom automation. Practice writing and debugging simple VBA scripts. -------------------------------------------------------------------------------- /Excel Fundamentals/01 Basic Operations/Sorting_and_Filtering.md: -------------------------------------------------------------------------------- 1 | # Sorting and Filtering in Excel 2 | 3 | ## Overview 4 | Sorting and filtering organize data for analysis. This guide covers sorting by values and filtering specific data. 5 | 6 | ## Key Concepts 7 | - **Sorting** (Data > Sort): 8 | - A-Z or Z-A for text/numbers. 9 | - Custom sort by multiple columns (e.g., Region, then Sales). 10 | - **Filtering** (Data > Filter): 11 | - Add filter arrows to headers. 12 | - Filter by values, text, or conditions (e.g., >100). 13 | - **Clear Filters**: Data > Clear to show all data. 14 | 15 | ## Step-by-Step Example 16 | **Scenario**: Sort and filter a sales table (columns: Region, Sales, Date). 17 | 1. **Sort by Sales**: 18 | - Select table (A1:C100). 19 | - Data > Sort > Sort by “Sales” > Largest to Smallest. 20 | 2. **Filter by Region**: 21 | - Data > Filter. 22 | - Click Region filter arrow, select “North”. 23 | 3. **Clear Filters**: 24 | - Data > Clear to reset. 25 | 26 | ## Practical Scenario 27 | You have a dataset with employee names, departments, and salaries. Sort by salary (high to low) and filter for the IT department. 28 | 29 | ## Practice Tasks 30 | 1. Sort a dataset by product price (low to high). 31 | 2. Filter sales data to show values >$500. 32 | 3. Apply a custom sort by department, then by hire date. 33 | 4. Filter dates to show only 2025 entries. 34 | 5. Combine sort and filter to show top 10 sales in a region. 35 | 36 | ## Interview Tip 37 | Interviewers may ask you to extract specific data. Practice filtering quickly and explaining your steps. -------------------------------------------------------------------------------- /Excel Fundamentals/02 Formulas and Functions/Logical_Functions.md: -------------------------------------------------------------------------------- 1 | # Logical Functions in Excel 2 | 3 | ## Overview 4 | Logical functions enable decision-making in Excel. This guide covers IF, AND, and OR functions for data analysis. 5 | 6 | ## Key Functions 7 | - **IF**: `=IF(condition, value_if_true, value_if_false)` – Returns value based on condition. 8 | - **AND**: `=AND(condition1, condition2)` – True if all conditions are met. 9 | - **OR**: `=OR(condition1, condition2)` – True if any condition is met. 10 | 11 | ## Step-by-Step Example 12 | **Scenario**: Categorize sales performance. 13 | 1. **IF for Sales Threshold**: 14 | - Data: Sales in A2:A10. 15 | - In B2, enter `=IF(A2>1000, "High", "Low")`. 16 | - Drag down to B10. 17 | 2. **AND for Multiple Conditions**: 18 | - In C2, enter `=AND(A2>500, A2<1000)` to check moderate sales. 19 | 3. **OR for Flexible Criteria**: 20 | - In D2, enter `=OR(A2>1000, A2<200)` for extreme sales. 21 | 22 | ## Practical Scenario 23 | You have employee data with salaries and years of service. Flag employees with salary >$50,000 and service >5 years using `AND` within `IF`. 24 | 25 | ## Practice Tasks 26 | 1. Use `IF` to flag orders >$2000 as “Priority”. 27 | 2. Combine `AND` with `IF` to mark sales between 100 and 500. 28 | 3. Use `OR` to identify extreme values (<50 or >1000). 29 | 4. Nest `IF` statements to categorize sales as High, Medium, Low. 30 | 5. Create a formula to flag invalid data (e.g., negative values). 31 | 32 | ## Interview Tip 33 | Interviewers may ask for conditional logic. Practice nesting functions and explaining your logic clearly. -------------------------------------------------------------------------------- /Excel Fundamentals/03 Data Management/Data_Entry_and_Validation.md: -------------------------------------------------------------------------------- 1 | # Data Entry and Validation in Excel 2 | 3 | ## Overview 4 | Data validation ensures accurate input, critical for clean datasets. This guide covers setting validation rules and error alerts. 5 | 6 | ## Key Concepts 7 | - **Data Validation** (Data > Data Validation): 8 | - Restrict to lists, numbers, dates. 9 | - Example: Dropdown for “Yes/No”. 10 | - Set ranges (e.g., 1-100). 11 | - **Input Messages**: Show instructions when cell is selected. 12 | - **Error Alerts**: Display messages for invalid entries (Stop, Warning, Information). 13 | 14 | ## Step-by-Step Example 15 | **Scenario**: Restrict a column to specific categories. 16 | 1. **Create Dropdown**: 17 | - Select B2:B10. 18 | - Data > Data Validation > List > Source: “High,Medium,Low”. 19 | 2. **Set Number Range**: 20 | - Select C2:C10. 21 | - Data > Data Validation > Whole Number > Minimum: 1, Maximum: 100. 22 | 3. **Add Error Alert**: 23 | - In Data Validation, set Error Alert to “Stop” with message: “Enter 1-100 only”. 24 | 25 | ## Practical Scenario 26 | You’re managing a survey. Restrict a column to “Male/Female” and another to ages 18-65, with error messages for invalid entries. 27 | 28 | ## Practice Tasks 29 | 1. Create a dropdown for product categories. 30 | 2. Restrict a column to dates in 2025. 31 | 3. Set a number range (0-500) with an error alert. 32 | 4. Add an input message to guide users. 33 | 5. Test validation by entering invalid data. 34 | 35 | ## Interview Tip 36 | Interviewers may ask for data integrity. Practice setting up validation and explaining rules. -------------------------------------------------------------------------------- /Excel Fundamentals/01 Basic Operations/Charts.md: -------------------------------------------------------------------------------- 1 | # Charts in Excel 2 | 3 | ## Overview 4 | Charts visualize data for insights and presentations. This guide covers creating and customizing bar, line, and pie charts. 5 | 6 | ## Key Concepts 7 | - **Chart Types**: 8 | - **Bar**: Compare categories (e.g., sales by region). 9 | - **Line**: Show trends (e.g., revenue over time). 10 | - **Pie**: Display proportions (e.g., market share). 11 | - **Inserting Charts**: Select data, Insert > Chart Type. 12 | - **Customization**: 13 | - Add titles (Chart Title). 14 | - Adjust labels (Data Labels). 15 | - Change colors (Chart Styles). 16 | 17 | ## Step-by-Step Example 18 | **Scenario**: Create a bar chart for sales by region. 19 | 1. **Prepare Data**: 20 | - Columns: Region (A2:A5), Sales (B2:B5). 21 | 2. **Insert Chart**: 22 | - Select A1:B5. 23 | - Insert > Bar Chart > Clustered Bar. 24 | 3. **Customize**: 25 | - Add title: “Sales by Region”. 26 | - Enable Data Labels (Chart Elements > Data Labels). 27 | - Change bar color (Format > Shape Fill). 28 | 29 | ## Practical Scenario 30 | You have monthly revenue data for 2024. Create a line chart to show trends and a pie chart for product category shares. 31 | 32 | ## Practice Tasks 33 | 1. Create a bar chart for sales by product. 34 | 2. Build a line chart for weekly website traffic. 35 | 3. Make a pie chart for department budget allocation. 36 | 4. Customize a chart with title, labels, and colors. 37 | 5. Create a chart with two data series (e.g., sales and costs). 38 | 39 | ## Interview Tip 40 | Interviewers may ask for quick visualizations. Practice creating charts and explaining data insights. -------------------------------------------------------------------------------- /Advanced Excel Skills/Power Query/Transforming_Data.md: -------------------------------------------------------------------------------- 1 | # Transforming Data in Power Query 2 | 3 | ## Overview 4 | Power Query’s transformation tools clean and reshape data, essential for preparing datasets for analysis and interviews. 5 | 6 | ## Key Concepts 7 | - **Transformations**: 8 | - Filter Rows: Remove unwanted data. 9 | - Remove Columns: Keep only relevant fields. 10 | - Merge Queries: Combine datasets. 11 | - Group By: Aggregate data (e.g., sum, average). 12 | - **Query Editor**: Apply transformations visually. 13 | - **Applied Steps**: Track and modify transformation steps. 14 | 15 | ## Step-by-Step Example 16 | **Scenario**: Clean and merge sales data. 17 | 1. **Import Data**: 18 | - Load two CSV files: `sales.csv` (Date, Product, Sales) and `products.csv` (Product, Category). 19 | 2. **Filter Rows**: 20 | - In Query Editor, filter `sales.csv` to exclude Sales <100. 21 | 3. **Merge Queries**: 22 | - Home > Merge Queries > Join `sales` with `products` on Product column. 23 | 4. **Group By**: 24 | - Group `sales` by Category, calculate total Sales. 25 | 26 | ## Practical Scenario 27 | You have a dataset with duplicate rows and missing values. Use Power Query to remove duplicates, fill missing data, and merge with a category table. 28 | 29 | ## Practice Tasks 30 | 1. Filter a dataset to show only 2025 sales. 31 | 2. Remove unnecessary columns from a query. 32 | 3. Merge two datasets on a common key (e.g., Product ID). 33 | 4. Group sales data by region and calculate averages. 34 | 5. Replace null values with a default (e.g., 0). 35 | 36 | ## Interview Tip 37 | Interviewers may test data cleaning. Practice transformations and explain each step clearly. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Conditional Formatting/Custom_Rules_with_Formulas.md: -------------------------------------------------------------------------------- 1 | # Custom Rules with Formulas in Excel 2 | 3 | ## Overview 4 | Custom conditional formatting rules use formulas to highlight cells based on complex conditions, a key intermediate skill for interviews. 5 | 6 | ## Key Concepts 7 | - **Custom Rule**: Home > Conditional Formatting > New Rule > Use a formula. 8 | - **Formula Logic**: Must return TRUE/FALSE for each cell. 9 | - **Examples**: 10 | - `=A1>100`: Highlights cells >100. 11 | - `=AND(A1>50, B1<200)`: Combines conditions. 12 | - **Relative References**: Adjust automatically for each cell. 13 | 14 | ## Step-by-Step Example 15 | **Scenario**: Highlight rows where sales >1000 and region is “North”. 16 | 1. **Prepare Data**: 17 | - Columns: Region (A2:A20), Sales (B2:B20). 18 | 2. **Create Rule**: 19 | - Select A2:B20. 20 | - Home > Conditional Formatting > New Rule > Use a formula. 21 | - Enter `=AND(A2="North", B2>1000)`. 22 | - Set format to Yellow Fill. 23 | 3. **Apply**: Click OK; rows meeting both conditions are highlighted. 24 | 25 | ## Practical Scenario 26 | You have a dataset with products, prices, and quantities. Highlight rows where price >$50 and quantity <10. 27 | 28 | ## Practice Tasks 29 | 1. Highlight cells where sales are above average (`=B2>AVERAGE(B:B)`). 30 | 2. Highlight rows where category is “Electronics” and sales >500. 31 | 3. Use a formula to highlight every other row (`=MOD(ROW(),2)=0`). 32 | 4. Highlight cells with negative values. 33 | 5. Combine `AND` and `OR` in a custom rule. 34 | 35 | ## Interview Tip 36 | Interviewers may test formula-based formatting. Practice writing and explaining custom formulas. -------------------------------------------------------------------------------- /Excel Fundamentals/01 Basic Operations/Basic_Formulas.md: -------------------------------------------------------------------------------- 1 | # Basic Formulas in Excel 2 | 3 | ## Overview 4 | Basic formulas are essential for quick calculations in Excel. This guide covers SUM, AVERAGE, MIN, MAX, and COUNT with examples. 5 | 6 | ## Key Formulas 7 | - **SUM**: `=SUM(A1:A10)` – Adds values in a range. 8 | - **AVERAGE**: `=AVERAGE(A1:A10)` – Calculates mean of a range. 9 | - **MIN**: `=MIN(A1:A10)` – Finds smallest value. 10 | - **MAX**: `=MAX(A1:A10)` – Finds largest value. 11 | - **COUNT**: `=COUNT(A1:A10)` – Counts cells with numbers. 12 | 13 | ## Step-by-Step Example 14 | **Scenario**: Analyze monthly sales data in cells A2:A13. 15 | 1. **Total Sales**: 16 | - In cell A14, enter `=SUM(A2:A13)` to sum sales. 17 | 2. **Average Sales**: 18 | - In cell A15, enter `=AVERAGE(A2:A13)` to find the mean. 19 | 3. **Min and Max**: 20 | - In A16, enter `=MIN(A2:A13)` for lowest sales. 21 | - In A17, enter `=MAX(A2:A13)` for highest sales. 22 | 4. **Count Months**: 23 | - In A18, enter `=COUNT(A2:A13)` to count entries. 24 | 25 | ## Practical Scenario 26 | You have a dataset with daily expenses in B2:B31. Calculate total expenses, average daily spend, min, max, and count of days with expenses. 27 | 28 | ## Practice Tasks 29 | 1. Sum sales for Q1 (3 months) in a dataset. 30 | 2. Find the average score of 20 students. 31 | 3. Identify the lowest and highest temperatures in a week’s data. 32 | 4. Count non-empty cells in a column with mixed data. 33 | 5. Combine formulas (e.g., `=SUM(A1:A10)/COUNT(A1:A10)` for manual average). 34 | 35 | ## Interview Tip 36 | Interviewers often test basic formula accuracy. Practice entering formulas without errors and explaining their purpose. -------------------------------------------------------------------------------- /Excel Fundamentals/02 Formulas and Functions/Arithmetic_Operators.md: -------------------------------------------------------------------------------- 1 | # Arithmetic Operators in Excel 2 | 3 | ## Overview 4 | Arithmetic operators perform basic calculations, a foundational skill for Excel interviews. This guide covers addition, subtraction, multiplication, and division. 5 | 6 | ## Key Concepts 7 | - **Operators**: 8 | - `+`: Addition (e.g., `=A1+B1`). 9 | - `-`: Subtraction (e.g., `=A1-B1`). 10 | - `*`: Multiplication (e.g., `=A1*B1`). 11 | - `/`: Division (e.g., `=A1/B1`). 12 | - **Order of Operations**: PEMDAS (Parentheses first, then Exponents, Multiplication/Division, Addition/Subtraction). 13 | - **Cell References**: Use relative (A1) or absolute ($A$1) references for dynamic calculations. 14 | 15 | ## Step-by-Step Example 16 | **Scenario**: Calculate order totals for a store. 17 | 1. **Setup Data**: 18 | - Column A: Quantity (A2:A5). 19 | - Column B: Price per unit (B2:B5). 20 | 2. **Total Cost**: 21 | - In C2, enter `=A2*B2` to multiply quantity by price. 22 | - Drag down to C5. 23 | 3. **Discounted Total**: 24 | - In D2, enter `=C2-C2*0.1` for 10% discount. 25 | 4. **Total Revenue**: 26 | - In D6, enter `=SUM(D2:D5)`. 27 | 28 | ## Practical Scenario 29 | You have a dataset with units sold and unit price. Calculate total revenue, apply a 5% tax, and subtract shipping costs ($10 per order). 30 | 31 | ## Practice Tasks 32 | 1. Multiply quantity and price for 10 orders. 33 | 2. Calculate profit (revenue - cost) for a dataset. 34 | 3. Divide a budget of $5000 across 12 months. 35 | 4. Use parentheses to prioritize calculations (e.g., `=(A1+B1)*C1`). 36 | 5. Combine operators to calculate net income after tax and expenses. 37 | 38 | ## Interview Tip 39 | Interviewers may test formula accuracy. Practice using cell references and explaining calculations clearly. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Conditional Formatting/Data_Bars_Color_Scales_Icon_Sets.md: -------------------------------------------------------------------------------- 1 | # Data Bars, Color Scales, and Icon Sets in Excel 2 | 3 | ## Overview 4 | Data Bars, Color Scales, and Icon Sets provide visual cues to compare data quickly, enhancing analysis in reports and interviews. 5 | 6 | ## Key Concepts 7 | - **Data Bars**: Show bars proportional to cell values. 8 | - **Color Scales**: Apply color gradients based on value ranges. 9 | - **Icon Sets**: Display icons (e.g., arrows, stars) based on thresholds. 10 | - **Access**: Home > Conditional Formatting > Data Bars/Color Scales/Icon Sets. 11 | 12 | ## Step-by-Step Example 13 | **Scenario**: Visualize monthly sales data. 14 | 1. **Data Bars**: 15 | - Select B2:B12 (sales values). 16 | - Home > Conditional Formatting > Data Bars > Blue Gradient. 17 | - Result: Bars show relative sales magnitude. 18 | 2. **Color Scales**: 19 | - Select B2:B12. 20 | - Home > Conditional Formatting > Color Scales > Green-Yellow-Red. 21 | - Result: Colors indicate low to high values. 22 | 3. **Icon Sets**: 23 | - Select B2:B12. 24 | - Home > Conditional Formatting > Icon Sets > 3 Arrows. 25 | - Result: Arrows show high, medium, low sales. 26 | 27 | ## Practical Scenario 28 | You have a dataset of student grades. Use Data Bars for relative comparison, Color Scales to show grade ranges, and Icon Sets to flag pass/fail thresholds. 29 | 30 | ## Practice Tasks 31 | 1. Apply Data Bars to a column of expenses. 32 | 2. Use a Green-White-Red Color Scale for sales data. 33 | 3. Add 3-star Icon Sets to performance scores. 34 | 4. Customize Data Bar colors for a professional look. 35 | 5. Combine Color Scales with Highlight Rules. 36 | 37 | ## Interview Tip 38 | Interviewers may ask for visual analysis. Practice applying these tools to highlight trends clearly. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Lookup and Reference/README.md: -------------------------------------------------------------------------------- 1 | # 🔗 Lookup and Reference 2 | 3 |
4 | Excel Logo 5 |
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Retrieve data efficiently with lookup functions

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Lookup and Reference** subfolder! This section covers functions to retrieve data from tables, including VLOOKUP, HLOOKUP, INDEX, MATCH, OFFSET, and INDIRECT, essential for intermediate Excel users and interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **VLOOKUP and HLOOKUP**: Basic lookup functions. 18 | - **INDEX and MATCH**: Flexible lookup alternatives. 19 | - **OFFSET and INDIRECT**: Dynamic range and reference functions. 20 | 21 | ## 🗂️ Files 22 | - `VLOOKUP_and_HLOOKUP.md`: Standard lookup functions. 23 | - `INDEX_and_MATCH.md`: Advanced lookup techniques. 24 | - `OFFSET_and_INDIRECT.md`: Dynamic referencing. 25 | 26 | ## 💡 Why Learn Lookup and Reference? 27 | - Retrieve data from large datasets accurately. 28 | - Create dynamic reports for analysis. 29 | - Prepare for complex interview lookup tasks. 30 | 31 | ## 📆 Study Plan 32 | - **Day 1**: VLOOKUP and HLOOKUP 33 | - **Day 2**: INDEX and MATCH 34 | - **Day 3**: OFFSET and INDIRECT 35 | 36 | ## 🤝 Contributions 37 | Want to contribute? 38 | 1. Fork the repository. 39 | 2. Create a feature branch (`git checkout -b feature/lookup-update`). 40 | 3. Commit changes (`git commit -m 'Add lookup content'`). 41 | 4. Push to branch (`git push origin feature/lookup-update`). 42 | 5. Open a Pull Request. 43 | 44 | --- 45 | 46 |
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Master data retrieval here! ✨

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-------------------------------------------------------------------------------- /Advanced Excel Skills/Array Formulas/README.md: -------------------------------------------------------------------------------- 1 | # 🧮 Array Formulas 2 | 3 |
4 | Excel Logo 5 |
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Perform complex calculations with array formulas

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Array Formulas** subfolder! This section covers advanced array formulas, including dynamic arrays (FILTER, SORT, UNIQUE) and legacy arrays, essential for complex calculations and interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Dynamic Array Formulas**: Use FILTER, SORT, UNIQUE. 18 | - **Legacy Array Formulas**: Ctrl+Shift+Enter formulas. 19 | - **Multi-Cell Calculations**: Perform array operations. 20 | 21 | ## 🗂️ Files 22 | - `Dynamic_Array_Formulas.md`: FILTER, SORT, UNIQUE. 23 | - `Legacy_Array_Formulas.md`: Ctrl+Shift+Enter arrays. 24 | - `Multi_Cell_Calculations.md`: Complex array operations. 25 | 26 | ## 💡 Why Learn Array Formulas? 27 | - Handle complex calculations efficiently. 28 | - Automate data filtering and sorting. 29 | - Prepare for advanced interview formula tasks. 30 | 31 | ## 📆 Study Plan 32 | - **Day 1**: Dynamic Array Formulas 33 | - **Day 2**: Legacy Array Formulas 34 | - **Day 3**: Multi-Cell Calculations 35 | 36 | ## 🤝 Contributions 37 | Want to contribute? 38 | 1. Fork the repository. 39 | 2. Create a feature branch (`git checkout -b feature/array-formulas-update`). 40 | 3. Commit changes (`git commit -m 'Add array formulas content'`). 41 | 4. Push to branch (`git push origin feature/array-formulas-update`). 42 | 5. Open a Pull Request. 43 | 44 | --- 45 | 46 |
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Master complex calculations here! ✨

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-------------------------------------------------------------------------------- /Intermediate Excel Skills/Conditional Formatting/Highlight_Rules.md: -------------------------------------------------------------------------------- 1 | # Highlight Rules in Excel 2 | 3 | ## Overview 4 | Highlight Rules in Conditional Formatting allow you to emphasize cells based on specific conditions, such as values being greater than a threshold or within the top 10%. This is essential for quick data analysis and interview tasks. 5 | 6 | ## Key Concepts 7 | - **Accessing Conditional Formatting**: Home > Conditional Formatting. 8 | - **Common Rules**: 9 | - **Greater Than/Less Than**: Highlight values above/below a number. 10 | - **Between**: Highlight values within a range. 11 | - **Top/Bottom Rules**: Highlight top/bottom N% or items. 12 | - **Text Contains**: Highlight cells with specific text. 13 | - **Formatting Options**: Choose colors, bold, or other styles. 14 | 15 | ## Step-by-Step Example 16 | **Scenario**: Highlight sales above $1000 in a dataset. 17 | 1. **Prepare Data**: 18 | - Column A: Product, Column B: Sales (B2:B20). 19 | 2. **Apply Greater Than Rule**: 20 | - Select B2:B20. 21 | - Home > Conditional Formatting > Highlight Cells Rules > Greater Than. 22 | - Enter `1000`, choose Red Fill. 23 | 3. **Apply Top 10% Rule**: 24 | - Select B2:B20. 25 | - Home > Conditional Formatting > Top/Bottom Rules > Top 10%. 26 | - Choose Green Fill. 27 | 28 | ## Practical Scenario 29 | You have a dataset of employee performance scores (1-100). Highlight scores >80 in blue and the top 10% in yellow. 30 | 31 | ## Practice Tasks 32 | 1. Highlight sales <500 in red. 33 | 2. Highlight the top 5 sales values in green. 34 | 3. Highlight cells containing “Urgent” in a status column. 35 | 4. Apply a rule to highlight values between 200 and 800. 36 | 5. Combine two rules (e.g., >1000 and top 10%). 37 | 38 | ## Interview Tip 39 | Interviewers may ask you to highlight key data. Practice applying multiple rules and explaining their impact. -------------------------------------------------------------------------------- /Intermediate Excel Skills/Pivot Tables/README.md: -------------------------------------------------------------------------------- 1 | # 📊 Pivot Tables 2 | 3 |
4 | Excel Logo 5 |
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Summarize and analyze data with Pivot Tables

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Pivot Tables** subfolder! This section covers creating, grouping, and enhancing Pivot Tables to summarize large datasets, a critical skill for data analysis interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Creating Pivot Tables**: Build summaries from raw data. 18 | - **Grouping Data**: Group by dates or categories. 19 | - **Calculated Fields**: Add custom calculations. 20 | - **Slicers and Timelines**: Filter data interactively. 21 | 22 | ## 🗂️ Files 23 | - `Creating_Pivot_Tables.md`: Build and configure Pivot Tables. 24 | - `Grouping_Data.md`: Group by dates or categories. 25 | - `Calculated_Fields.md`: Add custom calculations. 26 | - `Slicers_and_Timelines.md`: Enhance interactivity. 27 | 28 | ## 💡 Why Learn Pivot Tables? 29 | - Summarize large datasets quickly. 30 | - Create dynamic reports for presentations. 31 | - Prepare for advanced interview analysis tasks. 32 | 33 | ## 📆 Study Plan 34 | - **Day 1**: Creating Pivot Tables 35 | - **Day 2**: Grouping Data 36 | - **Day 3**: Calculated Fields, Slicers, and Timelines 37 | 38 | ## 🤝 Contributions 39 | Want to contribute? 40 | 1. Fork the repository. 41 | 2. Create a feature branch (`git checkout -b feature/pivot-tables-update`). 42 | 3. Commit changes (`git commit -m 'Add pivot table content'`). 43 | 4. Push to branch (`git push origin feature/pivot-tables-update`). 44 | 5. Open a Pull Request. 45 | 46 | --- 47 | 48 |
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Master data summarization here! ✨

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-------------------------------------------------------------------------------- /Advanced Excel Skills/Data Analysis Tools/README.md: -------------------------------------------------------------------------------- 1 | # 🔮 Data Analysis Tools 2 | 3 |
4 | Excel Logo 5 |
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Perform advanced analysis with Excel tools

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Data Analysis Tools** subfolder! This section covers advanced Excel tools like What-If Analysis, Solver, Data Tables, and Advanced Filters for sophisticated data analysis and interview preparation. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **What-If Analysis**: Use Goal Seek and Scenario Manager. 18 | - **Solver**: Optimize solutions. 19 | - **Data Tables**: Perform sensitivity analysis. 20 | - **Advanced Filters**: Filter complex datasets. 21 | 22 | ## 🗂️ Files 23 | - `What_If_Analysis.md`: Goal Seek and Scenario Manager. 24 | - `Solver_for_Optimization.md`: Solve optimization problems. 25 | - `Data_Tables_for_Sensitivity_Analysis.md`: Analyze variable impacts. 26 | - `Advanced_Filters.md`: Filter with complex criteria. 27 | 28 | ## 💡 Why Learn Data Analysis Tools? 29 | - Solve complex business problems. 30 | - Perform predictive and optimization analysis. 31 | - Prepare for advanced interview scenarios. 32 | 33 | ## 📆 Study Plan 34 | - **Day 1**: What-If Analysis 35 | - **Day 2**: Solver and Data Tables 36 | - **Day 3**: Advanced Filters 37 | 38 | ## 🤝 Contributions 39 | Want to contribute? 40 | 1. Fork the repository. 41 | 2. Create a feature branch (`git checkout -b feature/data-analysis-update`). 42 | 3. Commit changes (`git commit -m 'Add data analysis content'`). 43 | 4. Push to branch (`git push origin feature/data-analysis-update`). 44 | 5. Open a Pull Request. 45 | 46 | --- 47 | 48 |
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Master advanced analysis here! ✨

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-------------------------------------------------------------------------------- /Intermediate Excel Skills/Charts and Visualization/README.md: -------------------------------------------------------------------------------- 1 | # 📉 Charts and Visualization 2 | 3 |
4 | Excel Logo 5 |
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Create advanced visualizations for data insights

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Charts and Visualization** subfolder! This section covers intermediate charting techniques to create compelling visuals, including combo charts, sparklines, trendlines, and custom formatting, essential for interview presentations. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Combo Charts**: Combine multiple chart types. 18 | - **Sparklines**: Create mini-charts in cells. 19 | - **Trendlines**: Analyze data trends. 20 | - **Custom Chart Formatting**: Enhance chart appearance. 21 | 22 | ## 🗂️ Files 23 | - `Combo_Charts.md`: Combine chart types for insights. 24 | - `Sparklines.md`: Create compact visualizations. 25 | - `Trendlines.md`: Add trend analysis to charts. 26 | - `Custom_Chart_Formatting.md`: Customize chart appearance. 27 | 28 | ## 💡 Why Learn Charts and Visualization? 29 | - Present data insights clearly. 30 | - Create professional reports for stakeholders. 31 | - Prepare for interview visualization tasks. 32 | 33 | ## 📆 Study Plan 34 | - **Day 1**: Combo Charts 35 | - **Day 2**: Sparklines and Trendlines 36 | - **Day 3**: Custom Chart Formatting 37 | 38 | ## 🤝 Contributions 39 | Want to contribute? 40 | 1. Fork the repository. 41 | 2. Create a feature branch (`git checkout -b feature/charts-update`). 42 | 3. Commit changes (`git commit -m 'Add charts content'`). 43 | 4. Push to branch (`git push origin feature/charts-update`). 44 | 5. Open a Pull Request. 45 | 46 | --- 47 | 48 |
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Master data visualization here! ✨

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-------------------------------------------------------------------------------- /Advanced Excel Skills/Dashboards/README.md: -------------------------------------------------------------------------------- 1 | # 📊 Dashboards 2 | 3 |
4 | Excel Logo 5 |
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Create interactive dashboards for data insights

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Dashboards** subfolder! This section covers building interactive dashboards using Pivot Charts, Slicers, and dynamic ranges, essential for advanced Excel users and interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Building Interactive Dashboards**: Combine charts and filters. 18 | - **Using Slicers and Pivot Charts**: Add interactivity. 19 | - **Dynamic Ranges with OFFSET**: Create flexible data sources. 20 | - **Visual Best Practices**: Design professional dashboards. 21 | 22 | ## 🗂️ Files 23 | - `Building_Interactive_Dashboards.md`: Create dashboards. 24 | - `Using_Slicers_and_Pivot_Charts.md`: Add interactivity. 25 | - `Dynamic_Ranges_with_OFFSET.md`: Use dynamic ranges. 26 | - `Visual_Best_Practices.md`: Design effective dashboards. 27 | 28 | ## 💡 Why Learn Dashboards? 29 | - Present data insights interactively. 30 | - Create professional reports for stakeholders. 31 | - Prepare for advanced visualization interview tasks. 32 | 33 | ## 📆 Study Plan 34 | - **Day 1**: Building Dashboards 35 | - **Day 2**: Slicers and Pivot Charts 36 | - **Day 3**: Dynamic Ranges and Best Practices 37 | 38 | ## 🤝 Contributions 39 | Want to contribute? 40 | 1. Fork the repository. 41 | 2. Create a feature branch (`git checkout -b feature/dashboards-update`). 42 | 3. Commit changes (`git commit -m 'Add dashboards content'`). 43 | 4. Push to branch (`git push origin feature/dashboards-update`). 44 | 5. Open a Pull Request. 45 | 46 | --- 47 | 48 |
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Master dashboard creation here! ✨

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-------------------------------------------------------------------------------- /Advanced Excel Skills/Power Query/README.md: -------------------------------------------------------------------------------- 1 | # ⚙️ Power Query 2 | 3 |
4 | Excel Logo 5 |
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Transform and automate data workflows with Power Query

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Power Query** subfolder! This section covers advanced data import, transformation, and automation using Power Query, a powerful tool for data analysts preparing for technical interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Importing Data**: Load data from CSV, databases, and other sources. 18 | - **Transforming Data**: Filter, merge, and clean datasets. 19 | - **Automating Data Refresh**: Set up queries for automatic updates. 20 | - **Creating Custom Columns**: Add calculated columns with Power Query. 21 | 22 | ## 🗂️ Files 23 | - `Importing_Data.md`: Load data from various sources. 24 | - `Transforming_Data.md`: Clean and reshape data. 25 | - `Automating_Data_Refresh.md`: Set up automatic updates. 26 | - `Creating_Custom_Columns.md`: Add custom calculations. 27 | 28 | ## 💡 Why Learn Power Query? 29 | - Streamline data preparation for large datasets. 30 | - Automate repetitive data tasks. 31 | - Prepare for advanced data analysis interview questions. 32 | 33 | ## 📆 Study Plan 34 | - **Day 1**: Importing Data 35 | - **Day 2**: Transforming Data 36 | - **Day 3**: Automating Data Refresh and Custom Columns 37 | 38 | ## 🤝 Contributions 39 | Want to contribute? 40 | 1. Fork the repository. 41 | 2. Create a feature branch (`git checkout -b feature/power-query-update`). 42 | 3. Commit changes (`git commit -m 'Add Power Query content'`). 43 | 4. Push to branch (`git push origin feature/power-query-update`). 44 | 5. Open a Pull Request. 45 | 46 | --- 47 | 48 |
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Master data transformation here! ✨

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-------------------------------------------------------------------------------- /Excel Fundamentals/03 Data Management/README.md: -------------------------------------------------------------------------------- 1 | # 📈 Data Management 2 | 3 |
4 | Excel Logo 5 |
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Organize and clean data like a pro

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Data Management** subfolder! This section focuses on organizing, validating, and cleaning data in Excel, key skills for data analysis and interview preparation. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Data Entry and Validation**: Ensure accurate data input. 18 | - **Freeze Panes**: Keep headers visible. 19 | - **Splitting Text to Columns**: Separate data easily. 20 | - **Removing Duplicates**: Clean datasets. 21 | - **Flash Fill**: Automate data formatting. 22 | 23 | ## 🗂️ Files 24 | - `Data_Entry_and_Validation.md`: Restrict and validate inputs. 25 | - `Freeze_Panes.md`: Lock rows/columns for easy viewing. 26 | - `Splitting_Text_to_Columns.md`: Split data into columns. 27 | - `Removing_Duplicates.md`: Eliminate redundant data. 28 | - `Flash_Fill.md`: Automate pattern-based formatting. 29 | 30 | ## 💡 Why Learn Data Management? 31 | - Ensure data accuracy and consistency. 32 | - Streamline large dataset handling. 33 | - Prepare for data-cleaning interview tasks. 34 | 35 | ## 📆 Study Plan 36 | - **Day 1**: Data Entry and Validation, Freeze Panes 37 | - **Day 2**: Splitting Text, Removing Duplicates 38 | - **Day 3**: Flash Fill 39 | 40 | ## 🤝 Contributions 41 | Want to contribute? 42 | 1. Fork the repository. 43 | 2. Create a feature branch (`git checkout -b feature/data-management-update`). 44 | 3. Commit changes (`git commit -m 'Add data management content'`). 45 | 4. Push to branch (`git push origin feature/data-management-update`). 46 | 5. Open a Pull Request. 47 | 48 | --- 49 | 50 |
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Master data organization here! ✨

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-------------------------------------------------------------------------------- /Advanced Excel Skills/VBA and Macros/README.md: -------------------------------------------------------------------------------- 1 | # 🖥️ VBA and Macros 2 | 3 |
4 | Excel Logo 5 |
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Automate tasks with VBA and Macros

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **VBA and Macros** subfolder! This section covers automating Excel tasks using macros and VBA scripts, a critical skill for advanced users and technical interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Recording Macros**: Automate repetitive tasks. 18 | - **Writing VBA Scripts**: Code custom solutions. 19 | - **Automating Repetitive Tasks**: Streamline workflows. 20 | - **Creating User-Defined Functions**: Build custom formulas. 21 | - **Working with Loops and Conditions**: Add logic to scripts. 22 | 23 | ## 🗂️ Files 24 | - `Recording_Macros.md`: Record and edit macros. 25 | - `Writing_VBA_Scripts.md`: Write custom VBA code. 26 | - `Automating_Repetitive_Tasks.md`: Apply VBA for efficiency. 27 | - `Creating_User_Defined_Functions.md`: Build custom functions. 28 | - `Working_with_Loops_and_Conditions.md`: Use loops and logic. 29 | 30 | ## 💡 Why Learn VBA and Macros? 31 | - Automate complex and repetitive tasks. 32 | - Create custom solutions for data analysis. 33 | - Prepare for advanced interview automation tasks. 34 | 35 | ## 📆 Study Plan 36 | - **Day 1**: Recording Macros 37 | - **Day 2**: Writing VBA Scripts, Automating Tasks 38 | - **Day 3**: User-Defined Functions, Loops, and Conditions 39 | 40 | ## 🤝 Contributions 41 | Want to contribute? 42 | 1. Fork the repository. 43 | 2. Create a feature branch (`git checkout -b feature/vba-update`). 44 | 3. Commit changes (`git commit -m 'Add VBA content'`). 45 | 4. Push to branch (`git push origin feature/vba-update`). 46 | 5. Open a Pull Request. 47 | 48 | --- 49 | 50 |
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Master Excel automation here! ✨

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-------------------------------------------------------------------------------- /Intermediate Excel Skills/Conditional Formatting/README.md: -------------------------------------------------------------------------------- 1 | # 🔍 Conditional Formatting 2 | 3 |
4 | Excel Logo 5 |
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Enhance data visualization with conditional formatting

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Conditional Formatting** subfolder! This section covers techniques to highlight data based on rules, making it easier to analyze trends and patterns. Perfect for intermediate Excel users preparing for data analysis interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Highlight Rules**: Highlight cells based on conditions (e.g., Greater Than, Top 10%). 18 | - **Data Bars, Color Scales, Icon Sets**: Visualize data with graphical elements. 19 | - **Custom Rules with Formulas**: Create advanced conditional formatting rules. 20 | 21 | ## 🗂️ Files 22 | - `Highlight_Rules.md`: Apply basic conditional formatting rules. 23 | - `Data_Bars_Color_Scales_Icon_Sets.md`: Use graphical formatting tools. 24 | - `Custom_Rules_with_Formulas.md`: Create formula-based formatting. 25 | 26 | ## 💡 Why Learn Conditional Formatting? 27 | - Highlight key data insights quickly. 28 | - Improve data presentation for reports. 29 | - Prepare for interview tasks requiring data analysis. 30 | 31 | ## 📆 Study Plan 32 | - **Day 1**: Highlight Rules 33 | - **Day 2**: Data Bars, Color Scales, Icon Sets 34 | - **Day 3**: Custom Rules with Formulas 35 | 36 | ## 🤝 Contributions 37 | Want to contribute? 38 | 1. Fork the repository. 39 | 2. Create a feature branch (`git checkout -b feature/conditional-formatting-update`). 40 | 3. Commit changes (`git commit -m 'Add conditional formatting content'`). 41 | 4. Push to branch (`git push origin feature/conditional-formatting-update`). 42 | 5. Open a Pull Request. 43 | 44 | --- 45 | 46 |
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Master data visualization here! ✨

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-------------------------------------------------------------------------------- /Excel Fundamentals/01 Basic Operations/Cell_Formatting.md: -------------------------------------------------------------------------------- 1 | # Cell Formatting in Excel 2 | 3 | ## Overview 4 | Proper cell formatting enhances data readability and professionalism, a key skill for interviews. This guide covers formatting fonts, borders, numbers, and more. 5 | 6 | ## Key Concepts 7 | - **Font Formatting**: Adjust font type, size, bold, italic, or underline (Home > Font group). 8 | - **Borders**: Add outlines or grids to cells (Home > Borders). 9 | - **Number Formats**: 10 | - General: Default, no specific format. 11 | - Currency: `$1,234.56` 12 | - Percentage: `75%` 13 | - Date: `12/31/2025` 14 | - Custom: e.g., `0000` for 4-digit numbers. 15 | - **Alignment**: Center, left, right, or wrap text (Home > Alignment). 16 | - **Fill Color**: Highlight cells for emphasis (Home > Fill Color). 17 | 18 | ## Step-by-Step Example 19 | **Scenario**: Format a sales table for a report. 20 | 1. **Font Formatting**: 21 | - Select header row (e.g., A1:D1). 22 | - Go to Home > Font > Bold, set font to Calibri, size 12. 23 | 2. **Borders**: 24 | - Select table (e.g., A1:D10). 25 | - Home > Borders > All Borders. 26 | 3. **Number Formats**: 27 | - Select sales column (e.g., B2:B10). 28 | - Home > Number Format > Currency. 29 | 4. **Alignment**: 30 | - Center-align headers (Home > Center). 31 | 5. **Fill Color**: 32 | - Highlight totals row with light green (Home > Fill Color). 33 | 34 | ## Practical Scenario 35 | You’re given a dataset with sales, dates, and quantities. Format sales as Currency, dates as `MM/DD/YYYY`, and apply bold borders to headers. 36 | 37 | ## Practice Tasks 38 | 1. Format a column of prices as Currency with 2 decimal places. 39 | 2. Apply bold, size 14 font, and blue fill to a table header. 40 | 3. Format a date column as `DD-MMM-YYYY` (e.g., 24-Jul-2025). 41 | 4. Add thick outside borders to a 5x5 data range. 42 | 5. Use wrap text for a column with long descriptions. 43 | 44 | ## Interview Tip 45 | Interviewers may ask you to format a dataset for clarity. Practice applying consistent number formats and borders to impress. -------------------------------------------------------------------------------- /Excel Fundamentals/02 Formulas and Functions/README.md: -------------------------------------------------------------------------------- 1 | # 🔢 Formulas and Functions 2 | 3 |
4 | Excel Logo 5 |
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Master essential Excel formulas and functions

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Formulas and Functions** subfolder! This section dives into Excel’s core formulas and functions, from arithmetic operations to lookup functions, equipping you for data analysis and interview tasks. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Arithmetic Operators**: Basic calculations (+, -, *, /). 18 | - **Logical Functions**: IF, AND, OR for decision-making. 19 | - **Text Functions**: Manipulate text with LEFT, RIGHT, CONCATENATE. 20 | - **Date Functions**: Work with dates using TODAY, NOW, DATEDIF. 21 | - **Lookup Functions**: VLOOKUP and HLOOKUP for data retrieval. 22 | 23 | ## 🗂️ Files 24 | - `Arithmetic_Operators.md`: Basic math operations. 25 | - `Logical_Functions.md`: IF, AND, OR functions. 26 | - `Text_Functions.md`: Text manipulation tools. 27 | - `Date_Functions.md`: Handling dates and times. 28 | - `Lookup_Functions.md`: VLOOKUP and HLOOKUP. 29 | 30 | ## 💡 Why Learn Formulas and Functions? 31 | - Perform quick and accurate calculations. 32 | - Handle complex data analysis tasks. 33 | - Prepare for technical interview questions. 34 | 35 | ## 📆 Study Plan 36 | - **Day 1**: Arithmetic Operators and Logical Functions 37 | - **Day 2**: Text and Date Functions 38 | - **Day 3**: Lookup Functions 39 | 40 | ## 🤝 Contributions 41 | Want to contribute? 42 | 1. Fork the repository. 43 | 2. Create a feature branch (`git checkout -b feature/formulas-update`). 44 | 3. Commit changes (`git commit -m 'Add formulas content'`). 45 | 4. Push to branch (`git push origin feature/formulas-update`). 46 | 5. Open a Pull Request. 47 | 48 | --- 49 | 50 |
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Level up your Excel calculations here! ✨

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-------------------------------------------------------------------------------- /Excel Fundamentals/01 Basic Operations/README.md: -------------------------------------------------------------------------------- 1 | # 📊 Basic Operations 2 | 3 |
4 | Excel Logo 5 |
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Learn the essentials of navigating and formatting in Excel

7 | 8 | --- 9 | 10 | ## 📖 Introduction 11 | 12 | Welcome to the **Basic Operations** subfolder! This section covers the foundational skills for using Microsoft Excel, including navigation, cell formatting, basic formulas, sorting, filtering, and chart creation. Perfect for beginners or those preparing for Excel interviews. 13 | 14 | ## 🌟 What’s Inside? 15 | 16 | This subfolder contains `.md` files with detailed guides on: 17 | - **Navigation**: Using the ribbon and keyboard shortcuts. 18 | - **Cell Formatting**: Adjusting fonts, borders, and number formats. 19 | - **Basic Formulas**: SUM, AVERAGE, MIN, MAX, COUNT. 20 | - **Sorting and Filtering**: Organizing data efficiently. 21 | - **Charts**: Creating bar, line, and pie charts. 22 | 23 | ## 🗂️ Files 24 | - `Navigation.md`: Guide to Excel’s interface and shortcuts. 25 | - `Cell_Formatting.md`: Formatting cells for clarity and professionalism. 26 | - `Basic_Formulas.md`: Using core formulas for calculations. 27 | - `Sorting_and_Filtering.md`: Sorting and filtering data. 28 | - `Charts.md`: Creating and customizing basic charts. 29 | 30 | ## 💡 Why Learn Basic Operations? 31 | - Build confidence in Excel’s interface. 32 | - Master shortcuts to boost efficiency. 33 | - Prepare for entry-level interview tasks. 34 | 35 | ## 📆 Study Plan 36 | - **Day 1**: Navigation and Cell Formatting 37 | - **Day 2**: Basic Formulas and Sorting/Filtering 38 | - **Day 3**: Charts 39 | 40 | ## 🤝 Contributions 41 | Want to contribute? 42 | 1. Fork the repository. 43 | 2. Create a feature branch (`git checkout -b feature/basic-operations-update`). 44 | 3. Commit changes (`git commit -m 'Add basic operations content'`). 45 | 4. Push to branch (`git push origin feature/basic-operations-update`). 46 | 5. Open a Pull Request. 47 | 48 | --- 49 | 50 |
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Start mastering Excel basics here! ✨

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-------------------------------------------------------------------------------- /Excel Fundamentals/01 Basic Operations/Navigation.md: -------------------------------------------------------------------------------- 1 | # Navigation in Excel 2 | 3 | ## Overview 4 | Mastering navigation in Excel is crucial for efficient workflow and interview tasks. This guide covers Excel’s interface, ribbon navigation, and keyboard shortcuts to help you move quickly through worksheets. 5 | 6 | ## Key Concepts 7 | - **Ribbon**: The toolbar at the top with tabs like Home, Insert, Data, and Formulas. 8 | - **Quick Access Toolbar**: Customizable toolbar for frequent commands (e.g., Save, Undo). 9 | - **Keyboard Shortcuts**: 10 | - `Ctrl+C`: Copy selected cells. 11 | - `Ctrl+V`: Paste copied cells. 12 | - `Ctrl+Z`: Undo last action. 13 | - `Ctrl+S`: Save workbook. 14 | - `Ctrl+Arrow`: Jump to the edge of a data region. 15 | - `Ctrl+Page Up/Down`: Switch between worksheets. 16 | - **Name Box**: Located left of the formula bar, shows or sets cell references (e.g., A1). 17 | - **Formula Bar**: Displays the content or formula of the selected cell. 18 | - **Sheet Tabs**: Bottom of the workbook to switch between worksheets. 19 | 20 | ## Step-by-Step Example 21 | **Scenario**: You’re analyzing a sales dataset and need to navigate a large worksheet. 22 | 1. **Open Ribbon Tabs**: 23 | - Go to `Home` tab to access formatting tools. 24 | - Switch to `Data` tab to sort or filter data. 25 | 2. **Use Shortcuts**: 26 | - Select cell A1, press `Ctrl+Down Arrow` to jump to the last row of data. 27 | - Press `Ctrl+Right Arrow` to move to the last column. 28 | 3. **Switch Sheets**: 29 | - Use `Ctrl+Page Down` to move to the next worksheet. 30 | 4. **Name Box**: 31 | - Type `B50` in the Name Box and press Enter to jump to cell B50. 32 | 33 | ## Practical Scenario 34 | You’re given a workbook with 10 sheets of monthly sales data. Use `Ctrl+Page Down` to navigate to the “June” sheet, then use `Ctrl+Down Arrow` to find the last row of sales data. 35 | 36 | ## Practice Tasks 37 | 1. Open a new Excel workbook and explore all ribbon tabs (Home, Insert, View, etc.). 38 | 2. Use `Ctrl+Arrow` keys to navigate a dataset with 100 rows and 10 columns. 39 | 3. Switch between 3 worksheets using `Ctrl+Page Up/Down`. 40 | 4. Use the Name Box to jump to cell `Z100`. 41 | 5. Add `Save` and `Undo` to the Quick Access Toolbar. 42 | 43 | ## Interview Tip 44 | Interviewers may ask you to quickly locate data in a large dataset. Practice `Ctrl+Arrow` and Name Box navigation to demonstrate efficiency. -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Statistics/Introduction to Basic and Conditional Probability/Random Variables.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "92d99a75", 6 | "metadata": {}, 7 | "source": [ 8 | "## Random Variables\n", 9 | "\n", 10 | "In probability theory, a random variable is a mathematical concept that assigns a numerical value to each possible outcome of a random event or experiment. It represents the uncertain outcome of a random process and serves as a way to quantify and analyze the probabilities associated with different outcomes." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "id": "4f0e3531", 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "name": "stdout", 21 | "output_type": "stream", 22 | "text": [ 23 | "Rolled value: 4\n" 24 | ] 25 | } 26 | ], 27 | "source": [ 28 | "import random\n", 29 | "\n", 30 | "# Example of a discrete random variable - rolling a fair six-sided die\n", 31 | "outcomes = [1, 2, 3, 4, 5, 6]\n", 32 | "rolled_value = random.choice(outcomes)\n", 33 | "print(\"Rolled value:\", rolled_value)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 2, 39 | "id": "a8de28f3", 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "Random value: 0.9891548815190968\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "import random\n", 52 | "\n", 53 | "# Example of a continuous random variable - generating a random number between 0 and 1\n", 54 | "random_value = random.random()\n", 55 | "print(\"Random value:\", random_value)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "id": "3ea4c78f", 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [] 65 | } 66 | ], 67 | "metadata": { 68 | "kernelspec": { 69 | "display_name": "Python 3 (ipykernel)", 70 | "language": "python", 71 | "name": "python3" 72 | }, 73 | "language_info": { 74 | "codemirror_mode": { 75 | "name": "ipython", 76 | "version": 3 77 | }, 78 | "file_extension": ".py", 79 | "mimetype": "text/x-python", 80 | "name": "python", 81 | "nbconvert_exporter": "python", 82 | "pygments_lexer": "ipython3", 83 | "version": "3.10.9" 84 | } 85 | }, 86 | "nbformat": 4, 87 | "nbformat_minor": 5 88 | } 89 | -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Hypothesis/Introduction to Chi Squared Tests.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "9515f3fa", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "\n", 13 | "from scipy.stats import ttest_1samp # one t test\n", 14 | "from scipy.stats import ttest_ind # two t test\n", 15 | "from scipy.stats import ttest_rel # paired t test" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 2, 21 | "id": "cfcdba97", 22 | "metadata": {}, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/plain": [ 27 | "(1460, 81)" 28 | ] 29 | }, 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "output_type": "execute_result" 33 | } 34 | ], 35 | "source": [ 36 | "# lets read the data\n", 37 | "data = pd.read_csv('train.csv')\n", 38 | "\n", 39 | "# lets check the shape of the dataset\n", 40 | "data.shape" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "id": "fc7c3bc3", 46 | "metadata": {}, 47 | "source": [ 48 | "## Chi-Squared Test" 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "id": "103e2918", 54 | "metadata": {}, 55 | "source": [ 56 | "### Goodness of Fit test" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 3, 62 | "id": "38136b52", 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "name": "stdout", 67 | "output_type": "stream", 68 | "text": [ 69 | "National\n", 70 | "col_0 count\n", 71 | "0 \n", 72 | "black 50000\n", 73 | "hispanic 60000\n", 74 | "other 35000\n", 75 | "white 100000\n", 76 | "Minnesota\n", 77 | "col_0 count\n", 78 | "0 \n", 79 | "black 250\n", 80 | "hispanic 300\n", 81 | "other 150\n", 82 | "white 600\n" 83 | ] 84 | } 85 | ], 86 | "source": [ 87 | "# creating two dataframes called national and state.\n", 88 | "\n", 89 | "national = pd.DataFrame([\"white\"]*100000 + [\"hispanic\"]*60000 +\\\n", 90 | " [\"black\"]*50000 + [\"other\"]*35000)\n", 91 | "\n", 92 | "state = pd.DataFrame([\"white\"]*600 + [\"hispanic\"]*300 + \\\n", 93 | " [\"black\"]*250 + [\"other\"]*150)\n", 94 | "\n", 95 | "# Perform cross-tabulation to create frequency tables\n", 96 | "national_table = pd.crosstab(index=national[0], columns=\"count\")\n", 97 | "state_table = pd.crosstab(index=state[0], columns=\"count\")\n", 98 | "\n", 99 | "# Print the frequency tables\n", 100 | "print( \"National\")\n", 101 | "print(national_table)\n", 102 | "print( \"Minnesota\")\n", 103 | "print(state_table)" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": null, 109 | "id": "b8c43c33", 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [] 113 | } 114 | ], 115 | "metadata": { 116 | "kernelspec": { 117 | "display_name": "Python 3 (ipykernel)", 118 | "language": "python", 119 | "name": "python3" 120 | }, 121 | "language_info": { 122 | "codemirror_mode": { 123 | "name": "ipython", 124 | "version": 3 125 | }, 126 | "file_extension": ".py", 127 | "mimetype": "text/x-python", 128 | "name": "python", 129 | "nbconvert_exporter": "python", 130 | "pygments_lexer": "ipython3", 131 | "version": "3.10.9" 132 | } 133 | }, 134 | "nbformat": 4, 135 | "nbformat_minor": 5 136 | } 137 | -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Hypothesis/Introduction to T Tests.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 19, 6 | "id": "52005e23", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "\n", 13 | "from scipy.stats import ttest_1samp # one t test\n", 14 | "from scipy.stats import ttest_ind # two t test\n", 15 | "from scipy.stats import ttest_rel # paired t test" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 20, 21 | "id": "5cd6d05a", 22 | "metadata": {}, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/plain": [ 27 | "(1460, 81)" 28 | ] 29 | }, 30 | "execution_count": 20, 31 | "metadata": {}, 32 | "output_type": "execute_result" 33 | } 34 | ], 35 | "source": [ 36 | "# lets read the data\n", 37 | "data = pd.read_csv('train.csv')\n", 38 | "\n", 39 | "# lets check the shape of the dataset\n", 40 | "data.shape" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "id": "d564ff18", 46 | "metadata": {}, 47 | "source": [ 48 | "# Chi-Squared Test" 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "id": "211b52db", 54 | "metadata": {}, 55 | "source": [ 56 | "## Goodness of Fit test" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 21, 62 | "id": "a63c22bb", 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "name": "stdout", 67 | "output_type": "stream", 68 | "text": [ 69 | "National\n", 70 | "col_0 count\n", 71 | "0 \n", 72 | "black 50000\n", 73 | "hispanic 60000\n", 74 | "other 35000\n", 75 | "white 100000\n", 76 | "Minnesota\n", 77 | "col_0 count\n", 78 | "0 \n", 79 | "black 250\n", 80 | "hispanic 300\n", 81 | "other 150\n", 82 | "white 600\n" 83 | ] 84 | } 85 | ], 86 | "source": [ 87 | "# creating two dataframes called national and state.\n", 88 | "\n", 89 | "national = pd.DataFrame([\"white\"]*100000 + [\"hispanic\"]*60000 +\\\n", 90 | " [\"black\"]*50000 + [\"other\"]*35000)\n", 91 | "\n", 92 | "state = pd.DataFrame([\"white\"]*600 + [\"hispanic\"]*300 + \\\n", 93 | " [\"black\"]*250 + [\"other\"]*150)\n", 94 | "\n", 95 | "# Perform cross-tabulation to create frequency tables\n", 96 | "national_table = pd.crosstab(index=national[0], columns=\"count\")\n", 97 | "state_table = pd.crosstab(index=state[0], columns=\"count\")\n", 98 | "\n", 99 | "# Print the frequency tables\n", 100 | "print( \"National\")\n", 101 | "print(national_table)\n", 102 | "print( \"Minnesota\")\n", 103 | "print(state_table)" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": null, 109 | "id": "7cc6383f", 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [ 113 | "# conducting a chisquared test\n", 114 | "observed = state_table\n", 115 | "\n", 116 | "# Get population ratios\n", 117 | "national_ratios = national_table/len(national)\n", 118 | "\n", 119 | "# Get expected counts\n", 120 | "expected = national_ratios * len(state)\n", 121 | "\n", 122 | "# chisquared test\n", 123 | "chi_squared_stat = (((observed-expected)**2)/expected).sum()\n", 124 | "print(chi_squared_stat)" 125 | ] 126 | } 127 | ], 128 | "metadata": { 129 | "kernelspec": { 130 | "display_name": "Python 3 (ipykernel)", 131 | "language": "python", 132 | "name": "python3" 133 | }, 134 | "language_info": { 135 | "codemirror_mode": { 136 | "name": "ipython", 137 | "version": 3 138 | }, 139 | "file_extension": ".py", 140 | "mimetype": "text/x-python", 141 | "name": "python", 142 | "nbconvert_exporter": "python", 143 | "pygments_lexer": "ipython3", 144 | "version": "3.10.9" 145 | } 146 | }, 147 | "nbformat": 4, 148 | "nbformat_minor": 5 149 | } 150 | -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Hypothesis/Introduction to Z Tests.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "3861999a", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "\n", 13 | "from scipy.stats import ttest_1samp # one t test\n", 14 | "from scipy.stats import ttest_ind # two t test\n", 15 | "from scipy.stats import ttest_rel # paired t test" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "id": "496dad6e", 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "# lets read the data\n", 26 | "data = pd.read_csv('train.csv')\n", 27 | "\n", 28 | "# lets check the shape of the dataset\n", 29 | "data.shape" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "id": "1dff2506", 35 | "metadata": {}, 36 | "source": [ 37 | "## Z test" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": null, 43 | "id": "08adf03a", 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [ 47 | "from statsmodels.stats import weightstats as stests\n", 48 | "\n", 49 | "# Use the help() function to display the documentation for the ztest() function in the statsmodels.stats.weightstats module.\n", 50 | "help(stests.ztest)" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "id": "f2089d45", 56 | "metadata": {}, 57 | "source": [ 58 | "### One Sample Z Test\n", 59 | "\n", 60 | "The one-sample z-test is used to test whether the mean of a population is greater than, less than, or not equal to a specific value. Because the standard normal distribution is used to calculate critical values for the test, this test is often called the one-sample z-test." 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "id": "4b3f65a4", 66 | "metadata": {}, 67 | "source": [ 68 | "## We are testing whether the mean of house prices is 180000 or not" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": null, 74 | "id": "adbd1adb", 75 | "metadata": {}, 76 | "outputs": [], 77 | "source": [ 78 | "from scipy import stats\n", 79 | "\n", 80 | "# Perform a one-sample z-test using the stests.ztest() function from the statsmodels.stats.weightstats module.\n", 81 | "# The ztest() function compares the sample mean (x1) to a specified value (value) and returns the z-test statistic and p-value.\n", 82 | "ztest ,pval = stests.ztest(x1 = data['SalePrice'], x2=None, value=180000)\n", 83 | "\n", 84 | "# Print the obtained p-value.\n", 85 | "print(\"P-value :\",float(pval))\n", 86 | "\n", 87 | "# Based on the p-value, determine whether to reject or fail to reject the null hypothesis.\n", 88 | "if pval<0.05:\n", 89 | " print(\" We reject the null hypothesis\")\n", 90 | "else:\n", 91 | " print(\"We fail to reject the null hypothesis\")" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "id": "0fc65157", 97 | "metadata": {}, 98 | "source": [ 99 | "### Two sample Z Test\n", 100 | "\n", 101 | "In two sample z-test , similar to t-test here we are checking two independent data groups and deciding whether sample mean of two group is equal or not." 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "id": "c7303520", 108 | "metadata": {}, 109 | "outputs": [], 110 | "source": [ 111 | "# Two Sample Z test\n", 112 | "ztest, pval = stests.ztest(x1 = data['1stFlrSF'], x2 = data['2ndFlrSF'], value = 0, alternative = 'smaller')\n", 113 | "\n", 114 | "# Print the obtained p-value.\n", 115 | "print(\"p-value\", pval)\n", 116 | "\n", 117 | "# Based on the p-value, determine whether to reject or fail to reject the null hypothesis.\n", 118 | "if pval <0.05:\n", 119 | " print(\"we reject null hypothesis\")\n", 120 | "else:\n", 121 | " print(\"we accept null hypothesis\")" 122 | ] 123 | } 124 | ], 125 | "metadata": { 126 | "kernelspec": { 127 | "display_name": "Python 3 (ipykernel)", 128 | "language": "python", 129 | "name": "python3" 130 | }, 131 | "language_info": { 132 | "codemirror_mode": { 133 | "name": "ipython", 134 | "version": 3 135 | }, 136 | "file_extension": ".py", 137 | "mimetype": "text/x-python", 138 | "name": "python", 139 | "nbconvert_exporter": "python", 140 | "pygments_lexer": "ipython3", 141 | "version": "3.10.9" 142 | } 143 | }, 144 | "nbformat": 4, 145 | "nbformat_minor": 5 146 | } 147 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 📊 Excel Interview Preparation 2 | 3 |
4 | Excel Logo 5 | VBA 6 | Power Query 7 | Pivot Tables 8 |
9 |

Your comprehensive guide to mastering Excel for data analysis and interviews

10 | 11 | --- 12 | 13 | ## 📖 Introduction 14 | 15 | Welcome to the Excel Interview Preparation repository! 🚀 This is your go-to resource for mastering Microsoft Excel, a critical tool for data analysis, reporting, and business intelligence. From beginner basics to advanced techniques, this repo is designed to help you excel in technical interviews and data-driven roles with confidence. 16 | 17 | ## 🌟 What’s Inside? 18 | 19 | - **Core Excel Skills**: Master formulas, formatting, and data organization. 20 | - **Intermediate Techniques**: Dive into VLOOKUP, Pivot Tables, and conditional formatting. 21 | - **Advanced Features**: Explore Power Query, VBA, and complex data analysis. 22 | - **Hands-on Practice**: Solve real-world problems with step-by-step solutions. 23 | - **Interview Question Bank**: Ace common Excel interview questions. 24 | - **Efficiency Tips**: Learn shortcuts and best practices for professional workflows. 25 | 26 | ## 🔍 Who Is This For? 27 | 28 | - Data Analysts preparing for technical interviews. 29 | - Business Professionals enhancing Excel proficiency. 30 | - Financial Analysts mastering data modeling. 31 | - Job Seekers aiming for roles requiring Excel expertise. 32 | - Anyone looking to boost data analysis skills with Excel. 33 | 34 | ## 🗺️ Comprehensive Learning Roadmap 35 | 36 | --- 37 | 38 | ### 🏗️ Excel Fundamentals 39 | 40 | #### 📊 Basic Operations 41 | - Navigation (Ribbon, Shortcuts: Ctrl+C, Ctrl+V, Ctrl+Z) 42 | - Cell Formatting (Fonts, Borders, Number Formats) 43 | - Basic Formulas (SUM, AVERAGE, MIN, MAX, COUNT) 44 | - Sorting and Filtering 45 | - Charts (Bar, Line, Pie) 46 | 47 | #### 🔢 Formulas and Functions 48 | - Arithmetic Operators (+, -, *, /) 49 | - Logical Functions (IF, AND, OR) 50 | - Text Functions (LEFT, RIGHT, CONCATENATE) 51 | - Date Functions (TODAY, NOW, DATEDIF) 52 | - Lookup Functions (VLOOKUP, HLOOKUP) 53 | 54 | #### 📈 Data Management 55 | - Data Entry and Validation 56 | - Freeze Panes 57 | - Splitting Text to Columns 58 | - Removing Duplicates 59 | - Flash Fill 60 | 61 | --- 62 | 63 | ### 🛠️ Intermediate Excel Skills 64 | 65 | #### 🔍 Conditional Formatting 66 | - Highlight Rules (Greater Than, Top 10%) 67 | - Data Bars, Color Scales, Icon Sets 68 | - Custom Rules with Formulas 69 | 70 | #### 📊 Pivot Tables 71 | - Creating Pivot Tables 72 | - Grouping Data (by Date, Category) 73 | - Calculated Fields 74 | - Slicers and Timelines 75 | 76 | #### 🔗 Lookup and Reference 77 | - VLOOKUP and HLOOKUP 78 | - INDEX and MATCH 79 | - OFFSET and INDIRECT 80 | 81 | #### 📉 Charts and Visualization 82 | - Combo Charts 83 | - Sparklines 84 | - Trendlines 85 | - Custom Chart Formatting 86 | 87 | --- 88 | 89 | ### 🚀 Advanced Excel Skills 90 | 91 | #### ⚙️ Power Query 92 | - Importing Data (CSV, Databases) 93 | - Transforming Data (Filtering, Merging) 94 | - Automating Data Refresh 95 | - Creating Custom Columns 96 | 97 | #### 🖥️ VBA and Macros 98 | - Recording Macros 99 | - Writing VBA Scripts 100 | - Automating Repetitive Tasks 101 | - Creating User-Defined Functions 102 | - Working with Loops and Conditions 103 | 104 | #### 🔮 Data Analysis Tools 105 | - What-If Analysis (Goal Seek, Scenario Manager) 106 | - Solver for Optimization 107 | - Data Tables for Sensitivity Analysis 108 | - Advanced Filters 109 | 110 | #### 📊 Dashboards 111 | - Building Interactive Dashboards 112 | - Using Slicers and Pivot Charts 113 | - Dynamic Ranges with OFFSET 114 | - Visual Best Practices 115 | 116 | #### 🧮 Array Formulas 117 | - Dynamic Array Formulas (FILTER, SORT, UNIQUE) 118 | - Legacy Array Formulas (Ctrl+Shift+Enter) 119 | - Multi-Cell Calculations 120 | 121 | --- 122 | 123 | ## 💡 Why Master Excel for Interviews? 124 | 125 | Excel is a cornerstone for data analysis and business roles. Here’s why it’s essential: 126 | 1. **Versatility**: Handles data cleaning, analysis, and visualization. 127 | 2. **Industry Standard**: Used across finance, marketing, and operations. 128 | 3. **Efficiency**: Automates tasks with VBA and Power Query. 129 | 4. **High Demand**: A must-have skill for 6 LPA+ data roles. 130 | 5. **Accessibility**: Intuitive interface with powerful capabilities. 131 | 132 | This repo is your roadmap to mastering Excel for technical interviews and data careers—let’s get started! 133 | 134 | ## 📆 Study Plan 135 | 136 | - **Week 1-2**: Excel Fundamentals and Basic Formulas 137 | - **Week 3-4**: Intermediate Skills (Pivot Tables, VLOOKUP, Charts) 138 | - **Week 5-6**: Advanced Techniques (Power Query, VBA, Dashboards) 139 | 140 | ## 🤝 Contributions 141 | 142 | Want to contribute? Here’s how! 🌟 143 | 1. Fork the repository. 144 | 2. Create a feature branch (`git checkout -b feature/amazing-addition`). 145 | 3. Commit your changes (`git commit -m 'Add some amazing content'`). 146 | 4. Push to the branch (`git push origin feature/amazing-addition`). 147 | 5. Open a Pull Request. 148 | 149 | --- 150 | 151 |
152 |

Happy Learning and Good Luck with Your Interviews! ✨

153 |
-------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Statistics/Introduction to Basic and Conditional Probability/Basic and Conditional Probability.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "3269a76c", 6 | "metadata": {}, 7 | "source": [ 8 | "## Introduction to Basic and Conditional Probability" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "df1e0683", 14 | "metadata": {}, 15 | "source": [ 16 | "## Basic Probability\n", 17 | "\n", 18 | "To perform basic probability calculations on IPL (Indian Premier League) data using Python, you can use the pandas library to load and analyze the data. Here's an example of how you can calculate probabilities based on IPL data:" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 4, 24 | "id": "eece1b94", 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "# importing the libraries\n", 29 | "import pandas as pd\n", 30 | "import numpy as np" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 5, 36 | "id": "5eed5bbc", 37 | "metadata": {}, 38 | "outputs": [ 39 | { 40 | "data": { 41 | "text/html": [ 42 | "
\n", 43 | "\n", 56 | "\n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | "
idSeasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2umpire3
01IPL-2017Hyderabad2017-05-04 00:00:00Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ LlongNaN
12IPL-2017Pune2017-06-04 00:00:00Mumbai IndiansRising Pune SupergiantRising Pune Supergiantfieldnormal0Rising Pune Supergiant07SPD SmithMaharashtra Cricket Association StadiumA Nand KishoreS RaviNaN
23IPL-2017Rajkot2017-07-04 00:00:00Gujarat LionsKolkata Knight RidersKolkata Knight Ridersfieldnormal0Kolkata Knight Riders010CA LynnSaurashtra Cricket Association StadiumNitin MenonCK NandanNaN
34IPL-2017Indore2017-08-04 00:00:00Rising Pune SupergiantKings XI PunjabKings XI Punjabfieldnormal0Kings XI Punjab06GJ MaxwellHolkar Cricket StadiumAK ChaudharyC ShamshuddinNaN
45IPL-2017Bangalore2017-08-04 00:00:00Royal Challengers BangaloreDelhi DaredevilsRoyal Challengers Bangalorebatnormal0Royal Challengers Bangalore150KM JadhavM Chinnaswamy StadiumNaNNaNNaN
\n", 188 | "
" 189 | ], 190 | "text/plain": [ 191 | " id Season city date team1 \\\n", 192 | "0 1 IPL-2017 Hyderabad 2017-05-04 00:00:00 Sunrisers Hyderabad \n", 193 | "1 2 IPL-2017 Pune 2017-06-04 00:00:00 Mumbai Indians \n", 194 | "2 3 IPL-2017 Rajkot 2017-07-04 00:00:00 Gujarat Lions \n", 195 | "3 4 IPL-2017 Indore 2017-08-04 00:00:00 Rising Pune Supergiant \n", 196 | "4 5 IPL-2017 Bangalore 2017-08-04 00:00:00 Royal Challengers Bangalore \n", 197 | "\n", 198 | " team2 toss_winner toss_decision \\\n", 199 | "0 Royal Challengers Bangalore Royal Challengers Bangalore field \n", 200 | "1 Rising Pune Supergiant Rising Pune Supergiant field \n", 201 | "2 Kolkata Knight Riders Kolkata Knight Riders field \n", 202 | "3 Kings XI Punjab Kings XI Punjab field \n", 203 | "4 Delhi Daredevils Royal Challengers Bangalore bat \n", 204 | "\n", 205 | " result dl_applied winner win_by_runs \\\n", 206 | "0 normal 0 Sunrisers Hyderabad 35 \n", 207 | "1 normal 0 Rising Pune Supergiant 0 \n", 208 | "2 normal 0 Kolkata Knight Riders 0 \n", 209 | "3 normal 0 Kings XI Punjab 0 \n", 210 | "4 normal 0 Royal Challengers Bangalore 15 \n", 211 | "\n", 212 | " win_by_wickets player_of_match venue \\\n", 213 | "0 0 Yuvraj Singh Rajiv Gandhi International Stadium, Uppal \n", 214 | "1 7 SPD Smith Maharashtra Cricket Association Stadium \n", 215 | "2 10 CA Lynn Saurashtra Cricket Association Stadium \n", 216 | "3 6 GJ Maxwell Holkar Cricket Stadium \n", 217 | "4 0 KM Jadhav M Chinnaswamy Stadium \n", 218 | "\n", 219 | " umpire1 umpire2 umpire3 \n", 220 | "0 AY Dandekar NJ Llong NaN \n", 221 | "1 A Nand Kishore S Ravi NaN \n", 222 | "2 Nitin Menon CK Nandan NaN \n", 223 | "3 AK Chaudhary C Shamshuddin NaN \n", 224 | "4 NaN NaN NaN " 225 | ] 226 | }, 227 | "execution_count": 5, 228 | "metadata": {}, 229 | "output_type": "execute_result" 230 | } 231 | ], 232 | "source": [ 233 | "# reading the dataset\n", 234 | "df=pd.read_excel(\"matches.xlsx\")\n", 235 | "\n", 236 | "# checking the first five rows\n", 237 | "df.head()" 238 | ] 239 | }, 240 | { 241 | "cell_type": "code", 242 | "execution_count": 6, 243 | "id": "abaa880b", 244 | "metadata": {}, 245 | "outputs": [ 246 | { 247 | "data": { 248 | "text/plain": [ 249 | "Index(['id', 'Season', 'city', 'date', 'team1', 'team2', 'toss_winner',\n", 250 | " 'toss_decision', 'result', 'dl_applied', 'winner', 'win_by_runs',\n", 251 | " 'win_by_wickets', 'player_of_match', 'venue', 'umpire1', 'umpire2',\n", 252 | " 'umpire3'],\n", 253 | " dtype='object')" 254 | ] 255 | }, 256 | "execution_count": 6, 257 | "metadata": {}, 258 | "output_type": "execute_result" 259 | } 260 | ], 261 | "source": [ 262 | "# checking the columns present in the data\n", 263 | "df.columns" 264 | ] 265 | }, 266 | { 267 | "cell_type": "markdown", 268 | "id": "fbccf24c", 269 | "metadata": {}, 270 | "source": [ 271 | "**Calculating the probability of a team winning a match:**" 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 7, 277 | "id": "0ae9619f", 278 | "metadata": {}, 279 | "outputs": [ 280 | { 281 | "name": "stdout", 282 | "output_type": "stream", 283 | "text": [ 284 | "Probability of Mumbai Indians winning a match:14.42%\n" 285 | ] 286 | } 287 | ], 288 | "source": [ 289 | "# Total number of matches\n", 290 | "total_matches = len(df)\n", 291 | "\n", 292 | "# Number of matches won by Mumbai Indians\n", 293 | "team_wins = len(df[df['winner'] == 'Mumbai Indians'])\n", 294 | "\n", 295 | "probability = team_wins / total_matches\n", 296 | "print(\"Probability of Mumbai Indians winning a match:{0:.2f}%\".format(probability*100))" 297 | ] 298 | }, 299 | { 300 | "cell_type": "markdown", 301 | "id": "6ccec882", 302 | "metadata": {}, 303 | "source": [ 304 | "**Calculating the probability distribution of toss results:**" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": 8, 310 | "id": "04424510", 311 | "metadata": {}, 312 | "outputs": [ 313 | { 314 | "name": "stdout", 315 | "output_type": "stream", 316 | "text": [ 317 | "Probability distribution of toss results:\n", 318 | "Mumbai Indians 12.96\n", 319 | "Kolkata Knight Riders 12.17\n", 320 | "Chennai Super Kings 11.77\n", 321 | "Royal Challengers Bangalore 10.71\n", 322 | "Kings XI Punjab 10.71\n", 323 | "Delhi Daredevils 10.58\n", 324 | "Rajasthan Royals 10.58\n", 325 | "Sunrisers Hyderabad 6.08\n", 326 | "Deccan Chargers 5.69\n", 327 | "Pune Warriors 2.65\n", 328 | "Gujarat Lions 1.98\n", 329 | "Delhi Capitals 1.32\n", 330 | "Kochi Tuskers Kerala 1.06\n", 331 | "Rising Pune Supergiants 0.93\n", 332 | "Rising Pune Supergiant 0.79\n", 333 | "Name: toss_winner, dtype: float64\n" 334 | ] 335 | } 336 | ], 337 | "source": [ 338 | "# Count of each team winning the toss\n", 339 | "toss_counts = df['toss_winner'].value_counts()\n", 340 | "\n", 341 | "# Total number of matches\n", 342 | "total_matches = len(df)\n", 343 | "\n", 344 | "toss_probability = (toss_counts / total_matches)*100\n", 345 | "toss_probability = round(toss_probability, 2)\n", 346 | "print(\"Probability distribution of toss results:\")\n", 347 | "print(toss_probability)" 348 | ] 349 | }, 350 | { 351 | "cell_type": "markdown", 352 | "id": "f3aa15f7", 353 | "metadata": {}, 354 | "source": [ 355 | "**Probability of a specific outcome in the toss (e.g., winning the toss and choosing to bat):**" 356 | ] 357 | }, 358 | { 359 | "cell_type": "code", 360 | "execution_count": 9, 361 | "id": "e96ab7e6", 362 | "metadata": {}, 363 | "outputs": [ 364 | { 365 | "name": "stdout", 366 | "output_type": "stream", 367 | "text": [ 368 | "Probability of Chennai Super Kings choosing to bat after winning the toss:6.35%\n" 369 | ] 370 | } 371 | ], 372 | "source": [ 373 | "# Total number of tosses\n", 374 | "total_tosses = len(df)\n", 375 | "batting_choice = len(df[(df['toss_winner'] == 'Chennai Super Kings') & (df['toss_decision'] == 'bat')])\n", 376 | "\n", 377 | "probability = batting_choice / total_tosses\n", 378 | "print(\"Probability of Chennai Super Kings choosing to bat after winning the toss:{0:.2f}%\".format(probability*100))" 379 | ] 380 | }, 381 | { 382 | "cell_type": "markdown", 383 | "id": "9f2c4761", 384 | "metadata": {}, 385 | "source": [ 386 | "**Probability of a team winning after winning the toss and choosing to field:**" 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "execution_count": 10, 392 | "id": "98ccbf31", 393 | "metadata": {}, 394 | "outputs": [ 395 | { 396 | "name": "stdout", 397 | "output_type": "stream", 398 | "text": [ 399 | "Probability of a team winning after winning the toss and choosing to field:34.26%\n" 400 | ] 401 | } 402 | ], 403 | "source": [ 404 | "# Total number of matches\n", 405 | "total_matches = len(df)\n", 406 | "toss_field = len(df[(df['toss_decision'] == 'field') & (df['winner'] == df['toss_winner'])])\n", 407 | "\n", 408 | "probability = toss_field / total_matches\n", 409 | "print(\"Probability of a team winning after winning the toss and choosing to field:{0:.2f}%\".format(probability*100))" 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "id": "553e7196", 415 | "metadata": {}, 416 | "source": [ 417 | "**Probability of a specific event occurring in a match (e.g., a player scoring a century):**" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": 11, 423 | "id": "922fa394", 424 | "metadata": {}, 425 | "outputs": [ 426 | { 427 | "name": "stdout", 428 | "output_type": "stream", 429 | "text": [ 430 | "Probability of MS Dhoni being the player of the match and scoring a century:2.25%\n" 431 | ] 432 | } 433 | ], 434 | "source": [ 435 | "# Total number of matches\n", 436 | "total_matches = len(df)\n", 437 | "century_matches = len(df[df['player_of_match'] == 'MS Dhoni'])\n", 438 | "\n", 439 | "probability = century_matches / total_matches\n", 440 | "print(\"Probability of MS Dhoni being the player of the match and scoring a century:{0:.2f}%\".format(probability*100))" 441 | ] 442 | }, 443 | { 444 | "cell_type": "markdown", 445 | "id": "6907cf2b", 446 | "metadata": {}, 447 | "source": [ 448 | "## Set Theory" 449 | ] 450 | }, 451 | { 452 | "cell_type": "markdown", 453 | "id": "bb5fe22c", 454 | "metadata": {}, 455 | "source": [ 456 | "To perform set theory operations on two umpire columns in IPL (Indian Premier League) data using Python, you can apply the set operations individually on each column or consider both columns together. Here's an example of how you can apply set operations on two umpire columns:" 457 | ] 458 | }, 459 | { 460 | "cell_type": "code", 461 | "execution_count": 12, 462 | "id": "76d2a45e", 463 | "metadata": {}, 464 | "outputs": [ 465 | { 466 | "name": "stdout", 467 | "output_type": "stream", 468 | "text": [ 469 | "Union Set:\n", 470 | "{'GA Pratapkumar', 'Subroto Das', 'Rod Tucker', 'BG Jerling', 'K Ananthapadmanabhan', 'Ulhas Gandhe', 'K Hariharan', 'RK Illingworth', 'A Deshmukh', 'TH Wijewardene', 'C Shamshuddin', 'AV Jayaprakash', 'PG Pathak', 'Aleem Dar', 'Nand Kishore', 'K Srinath', 'Bruce Oxenford', 'Vineet Kulkarni', 'SL Shastri', 'Anil Dandekar', 'S Das', 'Anil Chaudhary', 'Sundaram Ravi', 'MR Benson', 'S Asnani', 'Marais Erasmus', 'Yeshwant Barde', 'RJ Tucker', 'Nigel Llong', 'AY Dandekar', 'HDPK Dharmasena', 'SJ Davis', 'VK Sharma', 'SD Fry', 'GAV Baxter', 'JD Cloete', 'AL Hill', 'I Shivram', 'AK Chaudhary', 'IL Howell', 'M Erasmus', 'SJA Taufel', 'YC Barde', 'KN Ananthapadmanabhan', 'Nitin Menon', 'Asad Rauf', 'S Ravi', 'Chris Gaffaney', 'Nanda Kishore', 'O Nandan', 'NJ Llong', 'PR Reiffel', nan, 'Virender Kumar Sharma', 'SD Ranade', 'CB Gaffaney', 'VA Kulkarni', 'SK Tarapore', 'KN Anantapadmanabhan', 'Ian Gould', 'SS Hazare', 'DJ Harper', 'BR Doctrove', 'AM Saheba', 'K Bharatan', 'RM Deshpande', 'A Nanda Kishore', 'K Srinivasan', 'RE Koertzen', 'BNJ Oxenford', 'CK Nandan', 'A Nand Kishore', 'BF Bowden', 'A.D Deshmukh', 'RB Tiffin', 'Kumar Dharmasena'}\n" 471 | ] 472 | } 473 | ], 474 | "source": [ 475 | "# Extract unique umpires from the first umpire column\n", 476 | "umpire1_set = set(df['umpire1'])\n", 477 | "\n", 478 | "# Extract unique umpires from the second umpire column\n", 479 | "umpire2_set = set(df['umpire2'])\n", 480 | "\n", 481 | "# Perform union of umpires from both columns\n", 482 | "union_set = umpire1_set.union(umpire2_set)\n", 483 | "\n", 484 | "print(\"Union Set:\")\n", 485 | "print(union_set)" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 13, 491 | "id": "6ff73023", 492 | "metadata": {}, 493 | "outputs": [ 494 | { 495 | "name": "stdout", 496 | "output_type": "stream", 497 | "text": [ 498 | "Intersection Set:\n", 499 | "{'Anil Chaudhary', 'S Das', 'CB Gaffaney', 'Rod Tucker', 'BG Jerling', 'VA Kulkarni', 'K Ananthapadmanabhan', 'MR Benson', 'Ulhas Gandhe', 'SK Tarapore', 'K Hariharan', 'RK Illingworth', 'A Deshmukh', 'S Asnani', 'Yeshwant Barde', 'AK Chaudhary', 'C Shamshuddin', 'IL Howell', 'SS Hazare', 'DJ Harper', 'Nigel Llong', 'Ian Gould', 'BR Doctrove', 'HDPK Dharmasena', 'AM Saheba', 'AV Jayaprakash', 'M Erasmus', 'SJA Taufel', 'A Nanda Kishore', 'KN Ananthapadmanabhan', 'SJ Davis', 'Nitin Menon', 'PG Pathak', 'S Ravi', 'Chris Gaffaney', 'RE Koertzen', 'BNJ Oxenford', 'CK Nandan', 'O Nandan', 'Nanda Kishore', 'NJ Llong', 'A Nand Kishore', 'SD Fry', 'PR Reiffel', 'K Srinath', nan, 'JD Cloete', 'Bruce Oxenford', 'Vineet Kulkarni', 'Kumar Dharmasena', 'SL Shastri', 'Anil Dandekar'}\n" 500 | ] 501 | } 502 | ], 503 | "source": [ 504 | "# Perform intersection of umpires from both columns\n", 505 | "intersection_set = umpire1_set.intersection(umpire2_set)\n", 506 | "\n", 507 | "print(\"Intersection Set:\")\n", 508 | "print(intersection_set)" 509 | ] 510 | }, 511 | { 512 | "cell_type": "code", 513 | "execution_count": 14, 514 | "id": "b2ff27ee", 515 | "metadata": {}, 516 | "outputs": [ 517 | { 518 | "name": "stdout", 519 | "output_type": "stream", 520 | "text": [ 521 | "Difference (Umpire1 - Umpire2) Set:\n", 522 | "{'K Bharatan', 'RM Deshpande', 'Marais Erasmus', 'YC Barde', 'BF Bowden', 'Sundaram Ravi', 'Aleem Dar', 'Asad Rauf', 'GAV Baxter', 'AY Dandekar'}\n" 523 | ] 524 | } 525 | ], 526 | "source": [ 527 | "# Perform difference between umpires of the first column and second column\n", 528 | "difference1_set = umpire1_set.difference(umpire2_set)\n", 529 | "\n", 530 | "print(\"Difference (Umpire1 - Umpire2) Set:\")\n", 531 | "print(difference1_set)" 532 | ] 533 | }, 534 | { 535 | "cell_type": "markdown", 536 | "id": "db4c47af", 537 | "metadata": {}, 538 | "source": [ 539 | "## Conditional Probability\n", 540 | "Conditional probability in IPL data involves calculating the probability of an event occurring given that another event has already occurred. Here's an example of calculating conditional probability in IPL data using Python:\n", 541 | "\n", 542 | "**Suppose you want to calculate the probability of a team winning the match given that they have won the toss. You can use the following code:**" 543 | ] 544 | }, 545 | { 546 | "cell_type": "code", 547 | "execution_count": 15, 548 | "id": "4a0eaf10", 549 | "metadata": {}, 550 | "outputs": [ 551 | { 552 | "name": "stdout", 553 | "output_type": "stream", 554 | "text": [ 555 | "Conditional Probability of Mumbai Indians winning the match given they won the toss: 57.14%\n" 556 | ] 557 | } 558 | ], 559 | "source": [ 560 | "# Total number of matches\n", 561 | "total_matches = len(df)\n", 562 | "\n", 563 | " # Number of matches won by Mumbai Indians in the toss\n", 564 | "toss_wins = len(df[df['toss_winner'] == 'Mumbai Indians'])\n", 565 | "match_wins_given_toss = len(df[(df['toss_winner'] == 'Mumbai Indians') & (df['winner'] == 'Mumbai Indians')])\n", 566 | "\n", 567 | "conditional_probability = match_wins_given_toss / toss_wins\n", 568 | "print(\"Conditional Probability of Mumbai Indians winning the match given they won the toss: {0:.2f}%\".format(conditional_probability*100))" 569 | ] 570 | }, 571 | { 572 | "cell_type": "markdown", 573 | "id": "6fdd5738", 574 | "metadata": {}, 575 | "source": [ 576 | "In this code, toss_wins represents the number of matches where Mumbai Indians won the toss, and match_wins_given_toss represents the number of matches Mumbai Indians won after winning the toss. The conditional probability is then calculated by dividing match_wins_given_toss by toss_wins." 577 | ] 578 | }, 579 | { 580 | "cell_type": "markdown", 581 | "id": "3c1af5be", 582 | "metadata": {}, 583 | "source": [ 584 | "**Suppose you want to calculate the probability of a team winning the match given that they have chosen to field after winning the toss. You can use the following code:**" 585 | ] 586 | }, 587 | { 588 | "cell_type": "code", 589 | "execution_count": 16, 590 | "id": "e02e07f1", 591 | "metadata": {}, 592 | "outputs": [ 593 | { 594 | "name": "stdout", 595 | "output_type": "stream", 596 | "text": [ 597 | "Probability of Mumbai Indians winning the match while fielding : 13.82%\n" 598 | ] 599 | } 600 | ], 601 | "source": [ 602 | "# Total number of matches\n", 603 | "total_matches = len(df)\n", 604 | "\n", 605 | "# Number of matches where the toss decision was to field\n", 606 | "toss_field = len(df[(df['toss_decision'] == 'field')])\n", 607 | "match_wins_given_field = len(df[(df['toss_decision'] == 'field') & (df['winner'] == 'Mumbai Indians')])\n", 608 | "\n", 609 | "conditional_probability = match_wins_given_field / toss_field\n", 610 | "print(\"Probability of Mumbai Indians winning the match while fielding : {0:.2f}%\".format(conditional_probability*100))" 611 | ] 612 | }, 613 | { 614 | "cell_type": "markdown", 615 | "id": "3f5c15d7", 616 | "metadata": {}, 617 | "source": [ 618 | "In this code, toss_field represents the number of matches where the toss decision was to field, and match_wins_given_field represents the number of matches won by Mumbai Indians when they chose to field. The conditional probability is then calculated by dividing match_wins_given_field by toss_field." 619 | ] 620 | }, 621 | { 622 | "cell_type": "markdown", 623 | "id": "723673c2", 624 | "metadata": {}, 625 | "source": [ 626 | "## Bayes Theorem\n", 627 | "\n", 628 | "Bayes' theorem is a fundamental concept in probability theory that allows us to update the probability of an event based on new evidence. Here's an example of applying Bayes' theorem in IPL data using Python:\n", 629 | "\n", 630 | "**Suppose you want to calculate the probability of a team winning the match given that they have won the toss. You also have prior knowledge about the overall win rate of teams in the IPL. Here's how you can use Bayes' theorem to update the probability:**" 631 | ] 632 | }, 633 | { 634 | "cell_type": "code", 635 | "execution_count": null, 636 | "id": "5b764b1c", 637 | "metadata": {}, 638 | "outputs": [], 639 | "source": [ 640 | "# Total number of matches\n", 641 | "total_matches = len(df)\n", 642 | "\n", 643 | "# Number of matches won by Mumbai Indians in the toss\n", 644 | "toss_wins = len(df[df['toss_winner'] == 'Mumbai Indians'])\n", 645 | "\n", 646 | "# Number of matches won by Mumbai Indians in both toss and match\n", 647 | "match_wins_and_toss = len(df[(df['winner'] == 'Mumbai Indians') & (df['toss_winner'] == 'Mumbai Indians')])\n", 648 | "\n", 649 | "# Prior Probability: Probability of Mumbai Indians winning the match\n", 650 | "prior_probability = match_wins_and_toss / total_matches\n", 651 | "\n", 652 | "# Likelihood: Probability of winning the toss and the match\n", 653 | "likelihood = match_wins_and_toss / toss_wins\n", 654 | "\n", 655 | "# Evidence: Probability of Mumbai Indians winning the toss\n", 656 | "evidence = toss_wins / total_matches\n", 657 | "\n", 658 | "# Applying Bayes' theorem\n", 659 | "posterior_probability = (likelihood * prior_probability) / evidence\n", 660 | "\n", 661 | "print(\"Posterior Probability of Mumbai Indians winning the match given they won the toss: {0:.2f}%\".format(posterior_probability*100))" 662 | ] 663 | }, 664 | { 665 | "cell_type": "markdown", 666 | "id": "c06cfdd9", 667 | "metadata": {}, 668 | "source": [ 669 | "In this code, prior_probability represents the probability of Mumbai Indians winning the match before considering the toss outcome. likelihood represents the probability of Mumbai Indians winning the toss given that they won the match. evidence represents the probability of Mumbai Indians winning the toss based on the total matches. Bayes' theorem is then applied to calculate the posterior_probability of Mumbai Indians winning the match given they won the toss." 670 | ] 671 | }, 672 | { 673 | "cell_type": "code", 674 | "execution_count": null, 675 | "id": "020660cb", 676 | "metadata": {}, 677 | "outputs": [], 678 | "source": [] 679 | } 680 | ], 681 | "metadata": { 682 | "kernelspec": { 683 | "display_name": "Python 3 (ipykernel)", 684 | "language": "python", 685 | "name": "python3" 686 | }, 687 | "language_info": { 688 | "codemirror_mode": { 689 | "name": "ipython", 690 | "version": 3 691 | }, 692 | "file_extension": ".py", 693 | "mimetype": "text/x-python", 694 | "name": "python", 695 | "nbconvert_exporter": "python", 696 | "pygments_lexer": "ipython3", 697 | "version": "3.10.9" 698 | } 699 | }, 700 | "nbformat": 4, 701 | "nbformat_minor": 5 702 | } 703 | -------------------------------------------------------------------------------- /Data Analytics & Visualization Course Materials/Statistics/Introduction to Descriptive Statistics/Types of Data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "db4b4ff3", 6 | "metadata": {}, 7 | "source": [ 8 | "# Descriptive Statistics" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "cfa9958b", 14 | "metadata": {}, 15 | "source": [ 16 | "## Types of Data\n", 17 | "\n", 18 | "In descriptive statistics, there are two main types of data: numerical and categorical. In Python, you can use various libraries such as NumPy and pandas to analyze and summarize both types of data." 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "id": "1dfbf668", 24 | "metadata": {}, 25 | "source": [ 26 | "* Example : Employee Data" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "id": "7092e869", 32 | "metadata": {}, 33 | "source": [ 34 | "The \"import pandas as pd\" statement is used in Python to import the pandas library and assign it the alias \"pd\".\n", 35 | "\n", 36 | "Pandas is a popular data manipulation and analysis library in Python. It provides powerful data structures, such as DataFrames, and a wide range of functions and methods for data manipulation, cleaning, analysis, and visualization. By importing pandas using the \"import pandas as pd\" statement, you can access all the functionality provided by the pandas library throughout your code by using the \"pd\" alias." 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 2, 42 | "id": "22cb2e97", 43 | "metadata": {}, 44 | "outputs": [], 45 | "source": [ 46 | "import pandas as pd" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 3, 52 | "id": "59c8f5a9", 53 | "metadata": {}, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/html": [ 58 | "
\n", 59 | "\n", 72 | "\n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | "
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumber...RelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041YesTravel_Rarely1102Sales12Life Sciences11...18008016405
149NoTravel_Frequently279Research & Development81Life Sciences12...4801103310717
237YesTravel_Rarely1373Research & Development22Other14...28007330000
333NoTravel_Frequently1392Research & Development34Life Sciences15...38008338730
427NoTravel_Rarely591Research & Development21Medical17...48016332222
\n", 222 | "

5 rows × 35 columns

\n", 223 | "
" 224 | ], 225 | "text/plain": [ 226 | " Age Attrition BusinessTravel DailyRate Department \\\n", 227 | "0 41 Yes Travel_Rarely 1102 Sales \n", 228 | "1 49 No Travel_Frequently 279 Research & Development \n", 229 | "2 37 Yes Travel_Rarely 1373 Research & Development \n", 230 | "3 33 No Travel_Frequently 1392 Research & Development \n", 231 | "4 27 No Travel_Rarely 591 Research & Development \n", 232 | "\n", 233 | " DistanceFromHome Education EducationField EmployeeCount EmployeeNumber \\\n", 234 | "0 1 2 Life Sciences 1 1 \n", 235 | "1 8 1 Life Sciences 1 2 \n", 236 | "2 2 2 Other 1 4 \n", 237 | "3 3 4 Life Sciences 1 5 \n", 238 | "4 2 1 Medical 1 7 \n", 239 | "\n", 240 | " ... RelationshipSatisfaction StandardHours StockOptionLevel \\\n", 241 | "0 ... 1 80 0 \n", 242 | "1 ... 4 80 1 \n", 243 | "2 ... 2 80 0 \n", 244 | "3 ... 3 80 0 \n", 245 | "4 ... 4 80 1 \n", 246 | "\n", 247 | " TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany \\\n", 248 | "0 8 0 1 6 \n", 249 | "1 10 3 3 10 \n", 250 | "2 7 3 3 0 \n", 251 | "3 8 3 3 8 \n", 252 | "4 6 3 3 2 \n", 253 | "\n", 254 | " YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager \n", 255 | "0 4 0 5 \n", 256 | "1 7 1 7 \n", 257 | "2 0 0 0 \n", 258 | "3 7 3 0 \n", 259 | "4 2 2 2 \n", 260 | "\n", 261 | "[5 rows x 35 columns]" 262 | ] 263 | }, 264 | "execution_count": 3, 265 | "metadata": {}, 266 | "output_type": "execute_result" 267 | } 268 | ], 269 | "source": [ 270 | "# Reading the dataset\n", 271 | "df = pd.read_csv(\"employee.csv\")\n", 272 | "\n", 273 | "# checking the head\n", 274 | "df.head()" 275 | ] 276 | }, 277 | { 278 | "cell_type": "code", 279 | "execution_count": 4, 280 | "id": "a30f6181", 281 | "metadata": {}, 282 | "outputs": [ 283 | { 284 | "data": { 285 | "text/plain": [ 286 | "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n", 287 | " 'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n", 288 | " 'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n", 289 | " 'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n", 290 | " 'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n", 291 | " 'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n", 292 | " 'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n", 293 | " 'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n", 294 | " 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n", 295 | " 'YearsWithCurrManager'],\n", 296 | " dtype='object')" 297 | ] 298 | }, 299 | "execution_count": 4, 300 | "metadata": {}, 301 | "output_type": "execute_result" 302 | } 303 | ], 304 | "source": [ 305 | "df.columns" 306 | ] 307 | }, 308 | { 309 | "cell_type": "markdown", 310 | "id": "df7a3cd9", 311 | "metadata": {}, 312 | "source": [ 313 | "# Numerical Data" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 5, 319 | "id": "c692ff8b", 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "data": { 324 | "text/html": [ 325 | "
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AgeDailyRateDistanceFromHomeEducationEmployeeCountEmployeeNumberEnvironmentSatisfactionHourlyRateJobInvolvementJobLevel...RelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
0411102121129432...18008016405
149279811236122...4801103310717
2371373221449221...28007330000
3331392341545631...38008338730
427591211714031...48016332222
..................................................................
1465368842321206134142...380117335203
146639613611206244223...18019537717
146727155431206428742...28016036203
1468491023231206546322...480017329608
146934628831206828242...18006344312
\n", 633 | "

1470 rows × 26 columns

\n", 634 | "
" 635 | ], 636 | "text/plain": [ 637 | " Age DailyRate DistanceFromHome Education EmployeeCount \\\n", 638 | "0 41 1102 1 2 1 \n", 639 | "1 49 279 8 1 1 \n", 640 | "2 37 1373 2 2 1 \n", 641 | "3 33 1392 3 4 1 \n", 642 | "4 27 591 2 1 1 \n", 643 | "... ... ... ... ... ... \n", 644 | "1465 36 884 23 2 1 \n", 645 | "1466 39 613 6 1 1 \n", 646 | "1467 27 155 4 3 1 \n", 647 | "1468 49 1023 2 3 1 \n", 648 | "1469 34 628 8 3 1 \n", 649 | "\n", 650 | " EmployeeNumber EnvironmentSatisfaction HourlyRate JobInvolvement \\\n", 651 | "0 1 2 94 3 \n", 652 | "1 2 3 61 2 \n", 653 | "2 4 4 92 2 \n", 654 | "3 5 4 56 3 \n", 655 | "4 7 1 40 3 \n", 656 | "... ... ... ... ... \n", 657 | "1465 2061 3 41 4 \n", 658 | "1466 2062 4 42 2 \n", 659 | "1467 2064 2 87 4 \n", 660 | "1468 2065 4 63 2 \n", 661 | "1469 2068 2 82 4 \n", 662 | "\n", 663 | " JobLevel ... RelationshipSatisfaction StandardHours \\\n", 664 | "0 2 ... 1 80 \n", 665 | "1 2 ... 4 80 \n", 666 | "2 1 ... 2 80 \n", 667 | "3 1 ... 3 80 \n", 668 | "4 1 ... 4 80 \n", 669 | "... ... ... ... ... \n", 670 | "1465 2 ... 3 80 \n", 671 | "1466 3 ... 1 80 \n", 672 | "1467 2 ... 2 80 \n", 673 | "1468 2 ... 4 80 \n", 674 | "1469 2 ... 1 80 \n", 675 | "\n", 676 | " StockOptionLevel TotalWorkingYears TrainingTimesLastYear \\\n", 677 | "0 0 8 0 \n", 678 | "1 1 10 3 \n", 679 | "2 0 7 3 \n", 680 | "3 0 8 3 \n", 681 | "4 1 6 3 \n", 682 | "... ... ... ... \n", 683 | "1465 1 17 3 \n", 684 | "1466 1 9 5 \n", 685 | "1467 1 6 0 \n", 686 | "1468 0 17 3 \n", 687 | "1469 0 6 3 \n", 688 | "\n", 689 | " WorkLifeBalance YearsAtCompany YearsInCurrentRole \\\n", 690 | "0 1 6 4 \n", 691 | "1 3 10 7 \n", 692 | "2 3 0 0 \n", 693 | "3 3 8 7 \n", 694 | "4 3 2 2 \n", 695 | "... ... ... ... \n", 696 | "1465 3 5 2 \n", 697 | "1466 3 7 7 \n", 698 | "1467 3 6 2 \n", 699 | "1468 2 9 6 \n", 700 | "1469 4 4 3 \n", 701 | "\n", 702 | " YearsSinceLastPromotion YearsWithCurrManager \n", 703 | "0 0 5 \n", 704 | "1 1 7 \n", 705 | "2 0 0 \n", 706 | "3 3 0 \n", 707 | "4 2 2 \n", 708 | "... ... ... \n", 709 | "1465 0 3 \n", 710 | "1466 1 7 \n", 711 | "1467 0 3 \n", 712 | "1468 0 8 \n", 713 | "1469 1 2 \n", 714 | "\n", 715 | "[1470 rows x 26 columns]" 716 | ] 717 | }, 718 | "execution_count": 5, 719 | "metadata": {}, 720 | "output_type": "execute_result" 721 | } 722 | ], 723 | "source": [ 724 | "# Selecting columns with integer data type from the DataFrame\n", 725 | "df.select_dtypes(\"int\")" 726 | ] 727 | }, 728 | { 729 | "cell_type": "markdown", 730 | "id": "8d69f8e1", 731 | "metadata": {}, 732 | "source": [ 733 | "# Categorical Data" 734 | ] 735 | }, 736 | { 737 | "cell_type": "code", 738 | "execution_count": 6, 739 | "id": "af7aee90", 740 | "metadata": {}, 741 | "outputs": [ 742 | { 743 | "data": { 744 | "text/html": [ 745 | "
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AttritionBusinessTravelDepartmentEducationFieldGenderJobRoleMaritalStatusOver18OverTime
0YesTravel_RarelySalesLife SciencesFemaleSales ExecutiveSingleYYes
1NoTravel_FrequentlyResearch & DevelopmentLife SciencesMaleResearch ScientistMarriedYNo
2YesTravel_RarelyResearch & DevelopmentOtherMaleLaboratory TechnicianSingleYYes
3NoTravel_FrequentlyResearch & DevelopmentLife SciencesFemaleResearch ScientistMarriedYYes
4NoTravel_RarelyResearch & DevelopmentMedicalMaleLaboratory TechnicianMarriedYNo
..............................
1465NoTravel_FrequentlyResearch & DevelopmentMedicalMaleLaboratory TechnicianMarriedYNo
1466NoTravel_RarelyResearch & DevelopmentMedicalMaleHealthcare RepresentativeMarriedYNo
1467NoTravel_RarelyResearch & DevelopmentLife SciencesMaleManufacturing DirectorMarriedYYes
1468NoTravel_FrequentlySalesMedicalMaleSales ExecutiveMarriedYNo
1469NoTravel_RarelyResearch & DevelopmentMedicalMaleLaboratory TechnicianMarriedYNo
\n", 909 | "

1470 rows × 9 columns

\n", 910 | "
" 911 | ], 912 | "text/plain": [ 913 | " Attrition BusinessTravel Department EducationField \\\n", 914 | "0 Yes Travel_Rarely Sales Life Sciences \n", 915 | "1 No Travel_Frequently Research & Development Life Sciences \n", 916 | "2 Yes Travel_Rarely Research & Development Other \n", 917 | "3 No Travel_Frequently Research & Development Life Sciences \n", 918 | "4 No Travel_Rarely Research & Development Medical \n", 919 | "... ... ... ... ... \n", 920 | "1465 No Travel_Frequently Research & Development Medical \n", 921 | "1466 No Travel_Rarely Research & Development Medical \n", 922 | "1467 No Travel_Rarely Research & Development Life Sciences \n", 923 | "1468 No Travel_Frequently Sales Medical \n", 924 | "1469 No Travel_Rarely Research & Development Medical \n", 925 | "\n", 926 | " Gender JobRole MaritalStatus Over18 OverTime \n", 927 | "0 Female Sales Executive Single Y Yes \n", 928 | "1 Male Research Scientist Married Y No \n", 929 | "2 Male Laboratory Technician Single Y Yes \n", 930 | "3 Female Research Scientist Married Y Yes \n", 931 | "4 Male Laboratory Technician Married Y No \n", 932 | "... ... ... ... ... ... \n", 933 | "1465 Male Laboratory Technician Married Y No \n", 934 | "1466 Male Healthcare Representative Married Y No \n", 935 | "1467 Male Manufacturing Director Married Y Yes \n", 936 | "1468 Male Sales Executive Married Y No \n", 937 | "1469 Male Laboratory Technician Married Y No \n", 938 | "\n", 939 | "[1470 rows x 9 columns]" 940 | ] 941 | }, 942 | "execution_count": 6, 943 | "metadata": {}, 944 | "output_type": "execute_result" 945 | } 946 | ], 947 | "source": [ 948 | "df.select_dtypes(\"object\")" 949 | ] 950 | }, 951 | { 952 | "cell_type": "code", 953 | "execution_count": null, 954 | "id": "f61901bf", 955 | "metadata": {}, 956 | "outputs": [], 957 | "source": [] 958 | } 959 | ], 960 | "metadata": { 961 | "kernelspec": { 962 | "display_name": "Python 3 (ipykernel)", 963 | "language": "python", 964 | "name": "python3" 965 | }, 966 | "language_info": { 967 | "codemirror_mode": { 968 | "name": "ipython", 969 | "version": 3 970 | }, 971 | "file_extension": ".py", 972 | "mimetype": "text/x-python", 973 | "name": "python", 974 | "nbconvert_exporter": "python", 975 | "pygments_lexer": "ipython3", 976 | "version": "3.10.9" 977 | } 978 | }, 979 | "nbformat": 4, 980 | "nbformat_minor": 5 981 | } 982 | --------------------------------------------------------------------------------