├── Adult Census Income ├── Dataset_Details.docx └── adult.xlsx ├── Amazon Top 50 Bestselling Books 2009 - 2019 ├── Dataset_Details.docx └── bestsellers-with-categories.xlsx ├── Avacado Prices ├── Dataset_Details.docx └── avocado.xlsx ├── Bank Marketing ├── Dataset_Details.docx └── bank-additional-full.xlsx ├── Boston Housing ├── Dataset_Details.docx └── boston.xlsx ├── Breast Cancer Wisconsin Dataset ├── Breast.xlsx └── Dataset_Details.docx ├── FIFA World Cup ├── Dataset_Details.docx ├── WorldCupMatches.xlsx ├── WorldCupPlayers.xlsx └── WorldCups.xlsx ├── Iris ├── Dataset_Details.docx └── iris.xlsx ├── LICENSE ├── New York City Airbnb Open Data ├── AB_NYC_2019.xlsx ├── Dataset_Details.docx └── New_York_City_.png ├── Online Shoppers Intention ├── Dataset_Details.docx └── online_shoppers_intention.xlsx ├── Readme.md ├── Stock Price ├── Amazon.xlsx ├── Apple.xlsx ├── Dataset_Details.docx ├── Facebook.xlsx ├── Google.xlsx └── Netflix.xlsx ├── Super Store Sales ├── Dataset_Details.docx └── SuperStoreUS-2015.xlsx ├── Titanic ├── Dataset_Details.docx └── Titanic.xlsx ├── Wine Quality ├── Dataset_Details.docx ├── winequality-red.xlsx ├── winequality-white.xlsx └── winequality.names └── World Happiness Report ├── 2015.xlsx ├── 2016.xlsx ├── 2017.xlsx ├── 2018.xlsx ├── 2019.xlsx └── Dataset_Details.docx /Adult Census Income/Dataset_Details.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/Adult Census Income/Dataset_Details.docx -------------------------------------------------------------------------------- /Adult Census Income/adult.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/Adult Census Income/adult.xlsx 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-------------------------------------------------------------------------------- /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 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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. -------------------------------------------------------------------------------- /New York City Airbnb Open Data/AB_NYC_2019.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/New York City Airbnb Open Data/AB_NYC_2019.xlsx -------------------------------------------------------------------------------- /New York City Airbnb Open Data/Dataset_Details.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/New York City Airbnb Open Data/Dataset_Details.docx -------------------------------------------------------------------------------- /New York City Airbnb Open Data/New_York_City_.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/New York City Airbnb Open Data/New_York_City_.png -------------------------------------------------------------------------------- /Online Shoppers Intention/Dataset_Details.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/Online Shoppers Intention/Dataset_Details.docx -------------------------------------------------------------------------------- /Online Shoppers Intention/online_shoppers_intention.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rohanmistry231/Practice-Datasets-for-Excel/03c8c297b29437967fbf0ee7f1fda760eb204f88/Online Shoppers Intention/online_shoppers_intention.xlsx -------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 | # Excel Datasets for Data Analytics Beginners 2 | 3 | Welcome to the **Excel Datasets for Data Analytics Beginners** repository! This repository contains 15 different datasets stored in Excel format, each designed to help beginners practice and improve their data analytics skills. These datasets cover various topics, including financial analysis, market analysis, and time series analysis. 4 | 5 | ## Table of Contents 6 | 7 | 1. [What are Excel Datasets?](#what-are-excel-datasets) 8 | 2. [List of Excel Datasets](#list-of-excel-datasets) 9 | 3. [Dataset Descriptions](#dataset-descriptions) 10 | 1. [Superstore Sales](#superstore-sales) 11 | 2. [Iris](#iris) 12 | 3. [Titanic](#titanic) 13 | 4. [Wine Quality](#wine-quality) 14 | 5. [Adult Census Income](#adult-census-income) 15 | 6. [Boston Housing](#boston-housing) 16 | 7. [Breast Cancer Wisconsin Dataset](#breast-cancer-wisconsin-dataset) 17 | 8. [Online Shoppers Purchasing Intention](#online-shoppers-purchasing-intention) 18 | 9. [Bank Marketing](#bank-marketing) 19 | 10. [Avocado Prices](#avocado-prices) 20 | 11. [Amazon Top 50 Bestselling Books 2009 – 2019](#amazon-top-50-bestselling-books-2009-2019) 21 | 12. [FIFA World Cup](#fifa-world-cup) 22 | 13. [New York City Airbnb Open Data](#new-york-city-airbnb-open-data) 23 | 14. [World Happiness Report](#world-happiness-report) 24 | 15. [Stock Price](#stock-price) 25 | 4. [Common Practice Questions](#common-practice-questions) 26 | 5. [Final Thoughts](#final-thoughts) 27 | 28 | ## What are Excel Datasets? 29 | 30 | Excel datasets are collections of data stored in an Excel spreadsheet, a commonly used software for creating, manipulating, and analyzing data in a structured format. These datasets can come in two main formats: Excel (.xlsx) and Comma Separated Values (CSV). 31 | 32 | - **Excel (.xlsx):** Provides advanced features for organizing and analyzing complex data, including formulas and visualizations. 33 | - **CSV:** Offers a simpler format compatible with various software applications, making data sharing easier. 34 | 35 | ## List of Excel Datasets 36 | 37 | 1. Superstore Sales 38 | 2. Iris 39 | 3. Titanic 40 | 4. Wine Quality 41 | 5. Adult Census Income 42 | 6. Boston Housing 43 | 7. Breast Cancer Wisconsin Dataset 44 | 8. Online Shoppers Purchasing Intention 45 | 9. Bank Marketing 46 | 10. Avocado Prices 47 | 11. Amazon Top 50 Bestselling Books 2009 – 2019 48 | 12. FIFA World Cup 49 | 13. New York City Airbnb Open Data 50 | 14. World Happiness Report 51 | 15. Stock Price 52 | 53 | ## Dataset Descriptions 54 | 55 | ### 1. Superstore Sales 56 | Provides sales data for a fictional retail company, including information on products, orders, and customers. Useful for practicing data analytics techniques. 57 | 58 | Variables: 59 | - Order ID, Customer ID, Order Date, Ship Date, Ship Mode, Segment, Region, Category, Sub-Category, Product Name, Sales, Quantity, Discount, Profit 60 | 61 | ### 2. Iris 62 | Includes measurements of iris flowers, belonging to 3 different species: setosa, versicolor, and virginica. 63 | 64 | Variables: 65 | - Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species 66 | 67 | ### 3. Titanic 68 | Offers information on passengers onboard the Titanic, useful for data cleaning, preprocessing, descriptive statistics, data visualization, and predictive modeling. 69 | 70 | Variables: 71 | - PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked 72 | 73 | ### 4. Wine Quality 74 | Contains information on red and white wine samples, aiming to classify wine quality based on chemical properties. 75 | 76 | Variables: 77 | - Fixed Acidity, Volatile Acidity, Citric Acid, Residual Sugar, Chlorides, Free Sulfur Dioxide, Total Sulfur Dioxide, Density, pH, Sulphates, Alcohol, Quality 78 | 79 | ### 5. Adult Census Income 80 | Collection of demographic, social, and economic attributes about individuals from the 1994 Census database. 81 | 82 | Variables: 83 | - age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, income 84 | 85 | ### 6. Boston Housing 86 | Information on housing in Boston, Massachusetts, used to analyze the relationship between house prices and other features. 87 | 88 | Variables: 89 | - CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT, MEDV 90 | 91 | ### 7. Breast Cancer Wisconsin Dataset 92 | Information about breast cancer tumors, used for classifying tumors as malignant or benign. 93 | 94 | Variables: 95 | - ID number, Diagnosis, Radius, Texture, Perimeter, Area, Smoothness, Compactness, Concavity, Concave points, Symmetry, Fractal dimension 96 | 97 | ### 8. Online Shoppers Purchasing Intention 98 | Data related to purchase patterns and consumer behavior in online shopping. 99 | 100 | Variables: 101 | - Administrative, Administrative_Duration, Informational, Informational_Duration, ProductRelated, ProductRelated_Duration, BounceRates, ExitRates, PageValues, SpecialDay 102 | 103 | ### 9. Bank Marketing 104 | Information about marketing campaigns for a Portuguese banking institution. 105 | 106 | Variables: 107 | - Age, Job, Marital, Education, Default, Balance, Housing, Loan, Contact, Day, Month, Duration, Campaign, Pdays, Previous, Poutcome, y 108 | 109 | ### 10. Avocado Prices 110 | Data related to avocado prices in the United States, collected from various sources. 111 | 112 | Variables: 113 | - Date, AveragePrice, Total Volume, PLU, Type, Region, Year 114 | 115 | ### 11. Amazon Top 50 Bestselling Books 2009 – 2019 116 | Data related to the top 50 best-selling books on Amazon for each year from 2009 to 2019. 117 | 118 | Variables: 119 | - Name, Author, User Rating, Reviews, Price, Year, Genre 120 | 121 | ### 12. FIFA World Cup 122 | Data related to the FIFA World Cup from 1930 to 2014. 123 | 124 | Variables: 125 | - Year, Country, Winner, Runners-Up, Third, Fourth, GoalsScored, QualifiedTeams, MatchesPlayed, Attendance 126 | 127 | ### 13. New York City Airbnb Open Data 128 | Public information about Airbnb listings in New York City. 129 | 130 | Variables: 131 | - Id, Name, Host_id, Host_name, Neighbourhood_group, Neighbourhood, Latitude, Longitude, Room_type, Price, Minimum_nights, Number_of_reviews, Last_review, Reviews_per_month, Calculated_host_listings_count, Availability_365 132 | 133 | ### 14. World Happiness Report 134 | Information on happiness levels of over 150 countries, including economic, social, and health factors. 135 | 136 | Variables: 137 | - Country name, Year, Life Ladder, Log GDP per capita, Social support, Healthy life expectancy, Freedom to make life choices, Generosity, Perceptions of corruption, Positive affect, Negative affect 138 | 139 | ### 15. Stock Price 140 | Daily stock prices of various companies. 141 | 142 | Variables: 143 | - Date, Open, High, Low, Close, Adj Close, Volume 144 | 145 | ## Common Practice Questions 146 | 147 | Here are some common practice questions for each dataset to help you get started: 148 | 149 | ### Superstore Sales 150 | - What is the total revenue generated by the store? 151 | - Which category of products contributes the most to sales? 152 | - How has the sales trend been for the past year? 153 | - Which region has the highest sales and which one has the lowest? 154 | - What is the average profit margin of the store? 155 | 156 | ### Iris 157 | - What is the distribution of each species of iris in the dataset? 158 | - What is the correlation between petal length and petal width? 159 | - What is the average sepal length for each species of iris? 160 | - Which species of iris has the largest petal area? 161 | - How many observations are there for each species of iris? 162 | 163 | ### Titanic 164 | - What is the survival rate of the passengers? 165 | - What is the average age of the passengers? 166 | - What is the proportion of male and female passengers? 167 | - Which class of passengers had the highest survival rate? 168 | - What is the distribution of the fare paid by the passengers? 169 | 170 | ### Wine Quality 171 | - What is the correlation between pH and alcohol content? 172 | - Which type of wine (red or white) has a higher median quality rating? 173 | - What is the median volatile acidity for each type of wine? 174 | - What is the proportion of each wine type in the dataset? 175 | - What is the distribution of citric acid for each wine type? 176 | 177 | ### Adult Census Income 178 | - What is the proportion of people who earn more than $50K? 179 | - What is the average age of people who earn more than $50K? 180 | - What is the correlation between age and education level? 181 | - What is the proportion of men and women who earn more than $50K? 182 | - What is the median hours worked per week for people who earn more than $50K? 183 | 184 | ### Boston Housing 185 | - What is the correlation between the number of rooms and the median value of owner-occupied homes? 186 | - Which variable has the highest correlation with the median value of owner-occupied homes? 187 | - What is the average age of the homes? 188 | - What is the distribution of the pupil-teacher ratio by town? 189 | - Which town has the highest median value of owner-occupied homes? 190 | 191 | ### Breast Cancer Wisconsin Dataset 192 | - What is the proportion of benign and malignant tumors? 193 | - What is the correlation between tumor radius and perimeter? 194 | - What is the average smoothness of the tumors? 195 | - What is the distribution of the concavity of the tumors? 196 | - What is 197 | 198 | the median area of the tumors? 199 | 200 | ### Online Shoppers Purchasing Intention 201 | - What is the proportion of visitors who make a purchase? 202 | - What is the average duration of visits? 203 | - What is the correlation between bounce rate and exit rate? 204 | - What is the proportion of visits on special days? 205 | - What is the distribution of page values? 206 | 207 | ### Bank Marketing 208 | - What is the proportion of people who subscribed to a term deposit? 209 | - What is the average age of people who subscribed to a term deposit? 210 | - What is the correlation between age and job? 211 | - What is the proportion of people who have a housing loan? 212 | - What is the distribution of education levels? 213 | 214 | ### Avocado Prices 215 | - What is the average price of avocados? 216 | - What is the correlation between total volume and average price? 217 | - What is the distribution of avocado types? 218 | - What is the trend of avocado prices over the years? 219 | - Which region has the highest average price of avocados? 220 | 221 | ### Amazon Top 50 Bestselling Books 2009 – 2019 222 | - What is the average user rating of the books? 223 | - What is the correlation between price and user rating? 224 | - What is the proportion of books by genre? 225 | - Which author has the most bestsellers? 226 | - What is the distribution of reviews for the books? 227 | 228 | ### FIFA World Cup 229 | - What is the average number of goals scored per World Cup? 230 | - Which country has won the most World Cups? 231 | - What is the distribution of attendance per World Cup? 232 | - What is the correlation between the number of goals scored and the number of matches played? 233 | - What is the trend of qualified teams over the years? 234 | 235 | ### New York City Airbnb Open Data 236 | - What is the average price of listings? 237 | - What is the correlation between number of reviews and price? 238 | - What is the proportion of room types? 239 | - Which neighborhood group has the highest number of listings? 240 | - What is the distribution of availability for the listings? 241 | 242 | ### World Happiness Report 243 | - What is the average life ladder score for the countries? 244 | - What is the correlation between GDP per capita and life ladder score? 245 | - What is the distribution of social support scores? 246 | - Which country has the highest healthy life expectancy? 247 | - What is the trend of positive affect scores over the years? 248 | 249 | ### Stock Price 250 | - What is the average closing price of the stocks? 251 | - What is the correlation between opening and closing prices? 252 | - What is the distribution of trading volume? 253 | - What is the trend of stock prices over the years? 254 | - Which company has the highest average closing price? 255 | 256 | ## Final Thoughts 257 | 258 | We hope you find these datasets helpful for practicing your data analytics skills. Feel free to reach out if you have any questions or suggestions for additional datasets. 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The details are described in [Cortez et al., 2009]. 3 | Please include this citation if you plan to use this database: 4 | 5 | P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 6 | Modeling wine preferences by data mining from physicochemical properties. 7 | In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236. 8 | 9 | Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 10 | [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf 11 | [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib 12 | 13 | 1. Title: Wine Quality 14 | 15 | 2. Sources 16 | Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009 17 | 18 | 3. Past Usage: 19 | 20 | P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 21 | Modeling wine preferences by data mining from physicochemical properties. 22 | In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236. 23 | 24 | In the above reference, two datasets were created, using red and white wine samples. 25 | The inputs include objective tests (e.g. PH values) and the output is based on sensory data 26 | (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality 27 | between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model 28 | these datasets under a regression approach. The support vector machine model achieved the 29 | best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), 30 | etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity 31 | analysis procedure). 32 | 33 | 4. Relevant Information: 34 | 35 | The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. 36 | For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. 37 | Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables 38 | are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). 39 | 40 | These datasets can be viewed as classification or regression tasks. 41 | The classes are ordered and not balanced (e.g. there are munch more normal wines than 42 | excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent 43 | or poor wines. Also, we are not sure if all input variables are relevant. So 44 | it could be interesting to test feature selection methods. 45 | 46 | 5. Number of Instances: red wine - 1599; white wine - 4898. 47 | 48 | 6. Number of Attributes: 11 + output attribute 49 | 50 | Note: several of the attributes may be correlated, thus it makes sense to apply some sort of 51 | feature selection. 52 | 53 | 7. Attribute information: 54 | 55 | For more information, read [Cortez et al., 2009]. 56 | 57 | Input variables (based on physicochemical tests): 58 | 1 - fixed acidity 59 | 2 - volatile acidity 60 | 3 - citric acid 61 | 4 - residual sugar 62 | 5 - chlorides 63 | 6 - free sulfur dioxide 64 | 7 - total sulfur dioxide 65 | 8 - density 66 | 9 - pH 67 | 10 - sulphates 68 | 11 - alcohol 69 | Output variable (based on sensory data): 70 | 12 - quality (score between 0 and 10) 71 | 72 | 8. 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