8 |
9 |
10 |
11 | **Instructors: Alex Aklson & Polong Lin**
12 |
13 | Course Link : [What is Data Science?](https://www.coursera.org/learn/what-is-datascience)
14 |
15 | ## Course Syllabus
16 |
17 | ### Defining Data Science and What Data Scientists Do
18 | - Defining Data Science
19 | - What is Data Science?
20 | - Fundamentals of Data Science
21 | - The Many Paths to Data Science
22 | - Advice for New Data Scientists
23 | - Data Science: The Sexiest Job in the 21st Century
24 |
25 | ### What Do Data Scientists Do?
26 | - A day in the Life of a Data Scientist
27 | - Old problems, new problems, Data Science solutions
28 | - Data Science Topics and Algorithms
29 | - What is the cloud?
30 | - What Makes Someone a Data Scientist?
31 |
32 | ### Data Science Topics
33 | - Foundations of Big Data
34 | - How Big Data is Driving Digital Transformation
35 | - What is Hadoop?
36 | - Data Science Skills & Big Data
37 | - Data Scientists at New York University
38 | - Data Mining
39 | - Quiz: Data Mining
40 |
41 | ### Deep Learning and Machine Learning
42 | - What's the difference?
43 | - Neural Networks and Deep Learning
44 | - Applications of Machine Learning
45 | - Regression
46 | - Quiz: Regression
47 |
48 | ### Data Science in Business
49 | - Applications of Data Science
50 | - How Data Science is Saving Lives
51 | - How Should Companies Get Started in Data Science?
52 | - Applications of Data Science
53 | - The Final Deliverable
54 | - Quiz: The Final Deliverable
55 |
56 | ### Careers and Recruiting in Data Science
57 | - How Can Someone Become a Data Scientist?
58 | - Recruiting for Data Science
59 | - Careers in Data Science
60 | - High School Students and Data Science Careers
61 |
62 | ### The Report Structure
63 | - The Report Structure
64 | - Quiz: The Report Structure
65 | - Final Assignment
66 |
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1 | # Defining Data Science and What Data Scientists Do
2 |
3 | In this module, you will view the course syllabus to learn what will be taught in this course. You will hear from data science professionals to discover what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will complete a reading assignment to find out why data science is considered the sexiest job in the 21st century.
4 |
5 | ## Key Concepts
6 | - Go over the course syllabus to learn what will be taught in this course.
7 | - Hear from data science professionals to learn what data science is.
8 | - Learn about the many paths to data science.
9 | - Hear from data science professionals as they give advice to anyone who is passionate about data science.
10 | - Learn some statistics about the field of data science, the demand for data scientists, and some of the qualities of excelling data scientists.
11 | - Hear from data scientists as they share with you what a typical day in the life of a data scientist looks like.
12 | - Hear from data scientists as they share with you what tools, algorithms, and technologies they use on a daily basis.
13 | - Hear from data scientists as they try to explain the term "cloud".
14 | - Learn why data science is considered the sexiest job in the 21st century.
15 | - Learn about data science, data scientists, and how each is defined.
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1 | # Data Science Topics
2 |
3 | In this module, you will hear from Norman White, the Faculty Director of the Stern Centre for Research Computing at New York University, as he talks about data science and the skills required for anyone interested in pursuing a career in this field. He also advises those looking to start a career in data science. Finally, you will complete reading assignments to learn about the process of mining a given dataset and about regression analysis.
4 |
5 |
6 | ## Key Concepts
7 | - Hear from Norman White, the Faculty Director of the Stern Centre for Research Computing, at New York University.
8 | - Hear from Norman White as he talks about data science and what skills are required for anyone interested in pursuing a career in this field.
9 | - Hear from Norman White as he explains some of the popular data science tools and algorithms.
10 | - Hear from Norman White as he gives advice to high schools students, in particular, and anyone, in general, who are looking to start a career in data science.
11 | - Learn about data mining, and the steps the comprise the process of mining a given dataset.
12 | - Learn about regression and what questions can be put to regression analysis.
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1 | # Final Assignment about Data Science and Reports
2 |
3 | Q1. Based on the videos and the reading material, how would you define a data scientist and data science? **(3 marks)**
4 |
5 | **Data Science:**
6 | - Data science is something that data scientist do.
7 | - Data science is a way of extracting insights from large volumes of disparate data.
8 | - Data science involves drawing patterns from seemingly random structured and unstructured type of data.
9 |
10 | **Data scientists:**
11 | - Data scientists are curious and analytical thinkers who use a variety of math skills not limited to Mathematics, Statistics and Probability to solve a problem.
12 | - They apply different available methods and algorithms to draw insights and conclusions from various kinds of data.
13 | - After applying data science methodologies, they are effective communicators and story tellers who can present their findings often to present new findings or confirm what was initially suspected.
14 |
15 | Q2. As discussed in the videos and the reading material, data science can be applied to problems across different industries. What industry are you passionate about and would like to pursue a data science career in? **(1 mark)**
16 |
17 | I am passionate about pursuing a data science career in the field of Healthcare with the main focus being improving quality of care provided and making healthcare affordable. I would like to create models to predict diseases very early on by looking at various parameters of a person not limited to genetics, family history, lifestyle, and diet.
18 |
19 |
20 | Q3. Based on the videos and the reading material, what are the ten main components of a report that would be delivered at the end of a data science project? **(5 marks)**
21 |
22 | The 10 main components of a data science project report would be:
23 |
24 | 1. **Cover Page** with Author's name, contacts, affiliations if any and publication date
25 | 2. **Table of Contents** containing main headings, list of contents and figures
26 | 3. **Abstract / Executive summary** to explain gist of the report
27 | 4. **Introduction** to explain the topic to new readers
28 | 5. **Literature Review** including citations of authors and data sources
29 | 6. **Methodology** section to explain the research methods and data sources used for analysis
30 | 7. Detailed _Explanations_ including **Results** and **discussion** sections
31 | 8. **Conclusions** which generalize findings and identify possible future outcomes.
32 | 9. **References**
33 | 10. **Acknowledgement** and **Appendices** (if Needed)
34 |
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1 | # Data Science in Business
2 |
3 | In this module, you will learn about the approaches companies can take to start working with data science. You will learn about some of the qualities that differentiate data scientists from other professionals. You will also learn about analytics, story-telling, and the pivotal role data scientists play in creating an effective final deliverable. Finally, you will apply what you learned about data science by answering open-ended questions.
4 |
5 | ## Key Concepts
6 | - Learn about what companies need to do in order to start with data science.
7 | - Learn about some of the qualities that differentiate data scientists from other professionals.
8 | - Learn about some applications of data science.
9 | - Learn about analytics and what important role data scientists play in this process.
10 | - Learn about story-telling and the importance of an effective final deliverable.
11 | - Learn about the main components of an effective final deliverable.
12 | - Apply what you learned about data science to answer open-ended questions.
13 | - Demonstrate your understanding of the readings to define what data science and data scientist mean.
14 | - Demonstrate your understanding of the readings to answer a question about the final deliverable of data science project.
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1 | # Tools for Data Science
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | **Instructors: Romeo Kienzler, Svetlana Levitan**
12 |
13 | **Course link:** [IBM Tools for Data Science](https://www.coursera.org/learn/open-source-tools-for-data-science)
14 |
15 | ## Key Concepts
16 |
17 | - Data Scientist's Toolkit
18 | - Open Source Tools
19 | - IBM Tools for Data Science
20 |
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1 | # Data Scientist's Toolkit
2 |
3 | This week, You will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by data scientists.
4 |
5 | ## Key Concepts
6 |
7 | - Explore the languages, tools, and data used by data scientists.
8 | - Give examples of popular tools used by data scientists.
9 | - Discover IBM tools focused on data science.
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1 | # Open Source Tools
2 |
3 | This week, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE. You will become familiar with the features of each tool, and what makes these tools so popular among data scientists today.
4 |
5 | ## Key Concepts
6 | - Explain how to use GitHub to create and manage source code for data science projects.
7 | - Describe the features of Jupyter Notebook that make it popular for data science projects.
8 | - Describe the features of RStudio IDE that make it popular for data science projects.
9 |
10 |
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/2.Tools_for_Data_Science/Week 3 - IBM Tools for Data Science/Readme.md:
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1 | # IBM Tools for Data Science
2 |
3 | ## Key Concepts
4 | - Explain how IBM Watson Studio can be used by data scientists.
5 | - Describe other IBM data science tools.
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/2.Tools_for_Data_Science/Week 4 - Final Assignment/Final_Assignment.ipynb:
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1 | {"cells": [{"metadata": {"collapsed": true}, "cell_type": "markdown", "source": "# My Jupyter Notebook on IBM Watson Studio"}, {"metadata": {}, "cell_type": "markdown", "source": "**Thomas George Thomas**\n\nData Scientist"}, {"metadata": {}, "cell_type": "markdown", "source": "_I am interested in Data Science because I would love to contribute towards affordable and quality healthcare in the future_"}, {"metadata": {}, "cell_type": "markdown", "source": "### The below should print 'Hello World'"}, {"metadata": {}, "cell_type": "code", "source": "print(\"Hello World!\")", "execution_count": 1, "outputs": [{"output_type": "stream", "text": "Hello World!\n", "name": "stdout"}]}, {"metadata": {}, "cell_type": "markdown", "source": "> You miss 100% of the shots you don't take\n>\n - Wayne Gretzky\n - Micheal Scott\n\n\n\nMade with <3 [My Github](https://github.com/Thomas-George-T)\n"}], "metadata": {"kernelspec": {"name": "python3", "display_name": "Python 3.6", "language": "python"}, "language_info": {"name": "python", "version": "3.6.9", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}, "nbformat": 4, "nbformat_minor": 1}
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/2.Tools_for_Data_Science/Week 4 - Final Assignment/Readme.md:
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1 | # Final Assignment: Create and Share Your Jupyter Notebook
2 |
3 | ## Key Concepts
4 | - Create a Jupyter Notebook.
5 | - Configure content in the Notebook.
6 | - Share the Notebook for peer review.
7 |
8 | ## Assignment Topic:
9 |
10 | You will create a Jupyter Notebook using IBM Watson Studio. You can choose any language you want (Python, R, or Scala). You will need to include a combination of markdown and code cells. You will likely need to use the Markdown cheatsheet to help you determine the appropriate syntax to style your markdown.
11 |
12 | ### Guidelines for the submission:
13 |
14 | Create your Jupyter Notebook on IBM Watson Studio.
15 |
16 | ### Include at least 6 cells:
17 |
18 | - Cell 1 (rendered as Markdown): The title should be "My Jupyter Notebook on IBM Watson Studio", in H1 header styling. The title does not need to be centered.
19 | - Cell 2 (rendered as Markdown): Include your name, in bold characters. In the line below your name, write your current or desired occupation in regular font.
20 | - Cell 3 (rendered as Markdown): In italic formatting, write one or two sentences about why you are interested in data science. For example, you can start your first sentence with "I am interested in data science because ...".
21 | - Cell 4 (rendered as Markdown): In H3 header styling, explain in a short sentence what your code is supposed to do in Cell 5.
22 | - Cell 5 (code cell): Your code, as described in Cell 4. It must be executed and must display an output. Try to keep the code simple (it can even be "1 + 1").
23 | - Cell 6 (rendered as Markdown): Using Markdown or HTML, this cell must include at least 3 of the following: horizontal rule, bulleted list, numbered list, tables, hyperlinks, images, code/syntax highlighting, blocked quotes, strikethrough.
24 |
25 | **Submit:**
26 |
27 | Submit the URL of your publicly shared notebook on IBM Watson Studio.
28 |
29 |
30 | The **main grading criteria** will be:
31 |
32 | - Is the notebook publicly viewable?
33 | - Are there, or does there appear to be, at least 5 Markdown cells and 1 code cell?
34 | - Is the criteria for each cell fulfilled, as described in the "Guidelines for Submission"?
35 |
36 | You **will not be judged** on:
37 |
38 | - Your English language, including spelling or grammatical mistakes.
39 | - The content of any text or image(s) or where a link is hyperlinked to.
40 |
41 |
42 | **Link to my Notebook**:https://eu-gb.dataplatform.cloud.ibm.com/analytics/notebooks/v2/ffd4f8a0-9c2c-4300-817e-9a8707b13ba1/view?access_token=7ce23622e8e2931d92a524f95965ad20c1b15f20056bd9517d640170e6bf5f7f
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/3.Data_Science_Methodology/Readme.md:
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1 | # Data Science Methodology
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | **Instructors: Alex Aklson & Polong Lin**
12 |
13 | Course link: [Data Science Methodology](https://www.coursera.org/learn/data-science-methodology)
14 |
15 | ## Syllabus
16 |
17 | ### Module 1: From Problem to Approach and from Requirements to Collection
18 | - Business Understanding
19 | - Analytic Approach
20 | - Data Requirements
21 | - Data Collection
22 | - Lab: From Problem to Approach
23 | - Lab: From Requirement to Collection
24 | - Quiz: From Problem to Approach
25 | - Quiz: From Requirement to Collection
26 |
27 | ### Module 2: From Understanding to Preparation and from Modeling to Evaluation
28 | - Data Understanding
29 | - Data Preparation
30 | - Modeling
31 | - Evaluation
32 | - Lab: From Understanding to Preparation
33 | - Lab: From Modeling to Evaluation
34 | - Quiz: From Understanding to Preparation
35 | - Quiz: From Modeling to Evaluation
36 |
37 | ### Module 3: From Deployment to Feedback
38 | - Deployment
39 | - Feedback
40 | - Quiz: From Deployment to Feedback
41 | - Peer-review Assignment
42 |
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/3.Data_Science_Methodology/Week 1 - From Problem to Approach and From Requirements to Collection/Readme.md:
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1 | # From Problem to Approach and From Requirements to Collection
2 |
3 | In this module, you will learn about why we are interested in data science, what a methodology is, and why data scientists need a methodology. You will also learn about the data science methodology and its flowchart. You will learn about the first two stages of the data science methodology, namely Business Understanding and Analytic Approach. Finally, through a lab session, you will also obtain how to complete the Business Understanding and the Analytic Approach stages and the Data Requirements and Data Collection stages pertaining to any data science problem.
4 |
5 | ## Key Concepts
6 | - Learn about why we are interested in data science.
7 | - Learn what a methodology is, and why data scientists need a methodology.
8 | - Learn about the data science methodology and its flowchart.
9 | - Learn about the Business Understaning, the Analytic Approach, the Data Requirements, and the Data Understanding stages of the data science methodology.
10 | - Learn about what occurs during data collection.
11 | - Learn how to complete the Business Understanding and the Analytic Approach stages pertaining to any data science problem.
12 | - Learn how to complete the Data Requirements and the Data Collection stages pertaining to any data science problem.
13 |
14 |
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/3.Data_Science_Methodology/Week 2 - From Understanding to Preparation and From Modeling to Evaluation/Readme.md:
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1 | # From Understanding to Preparation and From Modeling to Evaluation
2 |
3 | In this module, you will learn what it means to understand data, and prepare or clean data. You will also learn about the purpose of data modeling and some characteristics of the modeling process. Finally, through a lab session, you will learn how to complete the Data Understanding and the Data Preparation stages, as well as the Modeling and the Model Evaluation stages pertaining to any data science problem.
4 |
5 | ## Key Concepts
6 | - Learn what it means to understand data.
7 | - Learn what it means to prepare or clean data.
8 | - Learn about ways in which data is prepared.
9 | - Learn what the purpose of data modeling is.
10 | - Learn about some characteristics of the modeling process.
11 | - Learn about what it means to evaluate a model and ways in which a model is evaluated.
12 | - Learn how to complete the Data Understanding and the Data Preparation stages pertaining to any data science problem.
13 | - Learn how to complete the Modeling and the Model Evaluation stages pertaining to any data science problem.
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/3.Data_Science_Methodology/Week 3 - From Understanding to Preparation and From Modeling to Evaluation/2. Final Assignment/Readme.md:
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1 | # Data Science Methodology Final Assignment: Emails
2 |
3 | ### Q1. Which topic did you choose to apply the data science methodology to? 2 marks)
4 |
5 | **Ans:**
6 | The topic that I have chosen to apply data science methodology to is **Emails**. I believe by automatically classifying emails, productivity can be increased drastically.
7 |
8 |
9 | Next, you will play the role of the client and the data scientist.
10 |
11 | ### Q2. Using the topic that you selected, complete the Business Understanding stage by coming up with a problem that you would like to solve and phrasing it in the form of a question that you will use data to answer. **(3 marks)**
12 |
13 | You are required to:
14 |
15 | Describe the problem, related to the topic you selected.
16 | Phrase the problem as a question to be answered using data.
17 | For example, using the food recipes use case discussed in the labs, the question that we defined was, "Can we automatically determine the cuisine of a given dish based on its ingredients?".
18 |
19 | **Ans:**
20 | Daily, we receive 100's of emails every day and it may not be possible to look at all of them. We can determine which emails are worth taking a second look by organizing them into various categories like Promotions, Updates, Social, Order Receipts, Important/Not Important, Spam etc.
21 |
22 | Our Question would be: "Is it possible to automatically determine the type/category of email based on the content of the email?"
23 |
24 | ### Q3. Briefly explain how you would complete each of the following stages for the problem that you described in the Business Understanding stage, so that you are ultimately able to answer the question that you came up with. **(5 marks):**
25 |
26 | 1. Analytic Approach
27 | 2. Data Requirements
28 | 3. Data Collection
29 | 4. Data Understanding and Preparation
30 | 5. Modeling and Evaluation
31 |
32 | You can always refer to the labs as a reference with describing how you would complete each stage for your problem.
33 |
34 | **Ans:**
35 |
36 | 1. **Analytic Approach:**
37 |
38 | A Yes/No answer can be applied to this problem so we can use a classification model.
39 |
40 | 2. **Data Requirements:**
41 |
42 | To create the model, we will require information regarding the sender including email address, domain, subject, language ,if the email has an attachment or not, and body of the email to see if it contains a list (presence of a list could help classify the email as an order).
43 |
44 | 3. **Data Collection:**
45 |
46 | We can gather all these data from email accounts from various email inboxes (Gmail, Hotmail, yahoo, outlook etc.). We can further merge the emails from the various inboxes to create a good dataset. Descriptive statistics & visualizations can be applied to the data set to assess the content quality and if we have the required information.
47 |
48 | 4. **Data Understanding and Preparation:**
49 |
50 | We should remove the redundant data from our dataset. This could be two copies of the same email sent to different inboxes. Since we are working with text, we need to perform text analysis. We should ensure proper groupings to help classify the emails properly. These groupings should be done based on certain keywords present in the subject or content of the email.
51 |
52 | 5. **Modeling and Evaluation:**
53 |
54 | We create the classification model. We evaluate the results of the model and see how much is classified correctly or incorrectly. Using this feedback we can tweak the model to add parameters and perform necessary changes to ensure that we're getting the intended results.
55 |
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/3.Data_Science_Methodology/Week 3 - From Understanding to Preparation and From Modeling to Evaluation/Readme.md:
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1 | # From Deployment to Feedback
2 |
3 | In this module, you will learn about what happens when a model is deployed and why model feedback is important. Also, by completing a peer-reviewed assignment, you will demonstrate your understanding of the data science methodology by applying it to a problem that you define.
4 |
5 | ## Key Concepts
6 | - Learn about what happens when a model is deployed.
7 | - Learn about why model feedback is important.
8 | - Demonstrate your understanding of the data science methodology by applying it to a problem that you define.
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/4.Python_for_Data_Science_and_AI/Readme.md:
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1 | # Python for Data Science and AI
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | **Instructors: Joseph Santarcangelo**
12 |
13 | Course Link: [Python for Data Science and AI](https://www.coursera.org/learn/python-for-applied-data-science-ai)
14 |
15 | ## About this Course
16 |
17 | This introduction to Python will kickstart your learning of **Python for data science**, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.
18 |
19 | Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you. This course will not teach you everything about Python, but it should give you the tools to work as a data scientist and enough knowledge to continue to expand your Python learning. In the final project, you will load data and learn a new Python Library on your own.
20 |
21 | ### Module 1 - Python Basics
22 |
23 | 1. Your first program
24 | 2. Types
25 | 3. Expressions and Variables
26 | 4. String Operations
27 |
28 | ### Module 2 - Python Data Structures
29 |
30 | 1. Lists and Tuples
31 | 2. Sets
32 | 3. Dictionaries
33 |
34 | ### Module 3 - Python Programming Fundamentals
35 |
36 | 1. Conditions and Branching
37 | 2. Loops
38 | 3. Functions
39 | 4. Objects and Classes
40 |
41 | ### Module 4 - Working with Data in Python
42 |
43 | 1. Reading files with open
44 | 2. Writing files with open
45 | 3. Loading data with Pandas
46 | 4. Working with and Saving data with Pandas
47 | 5. Numpy
48 |
49 | ### Module 5 - Final Project
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/4.Python_for_Data_Science_and_AI/Week 1 - Python Basics/Readme.md:
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1 | # Python Basics
2 |
3 | ## Key Concepts
4 | - Demonstrate an understanding of types in python by converting/casting data types: strings, floats, integers.
5 | - Interpret variables and solve expressions by applying mathematical operations.
6 | - Describe how to manipulate strings by using a variety of methods and operations.
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/4.Python_for_Data_Science_and_AI/Week 2 - Python Data Structures/Readme.md:
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1 | # Python Data Structures
2 |
3 | ## Key Concepts
4 | 1. Understand tuples and lists by describing and manipulating tuple combinations and list data structures.
5 | 2. Demonstrate understanding of dictionaries by writing structures with correct keys and values.
6 | 3. Understand the differences between sets, tuples, and lists by creating sets.
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/4.Python_for_Data_Science_and_AI/Week 3 - Python Programming Fundamentals/Readme.md:
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1 | # Python Programming Fundamentals
2 |
3 | ## Key Concepts
4 | - Classify conditions and branching by identifying structured scenarios with outputs.
5 | - Understand loops by using visual examples and comparing them to tuples and lists.
6 | - Understand functions by building a function using inputs and outputs.
7 | - Explain objects and classes by identifying data types and creating a class.
8 |
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/4.Python_for_Data_Science_and_AI/Week 4 - Working with Data in Python/Readme.md:
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1 | # Working with Data in Python
2 |
3 | ## Key Concepts
4 | - Demonstrate an open function to create and identify a file object.
5 | - Understand how to use pandas for library and data analysis by using commands.
6 | - Demonstrate how to create a text file by using the open function.
7 | - Demonstrate how to use NumPy to create multi-dimensional arrays.
8 |
9 |
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/4.Python_for_Data_Science_and_AI/Week 5 - Analyzing US Economic Data and Building a Dashboard/Readme.md:
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1 | # Analyzing US Economic Data and Building a Dashboard
2 |
3 | ## Key Concepts
4 | - Create a dashboard that shows key economic indicators from a specific data set.
5 |
6 | __1. Create a dataframe that contains the GDP data and display using the method head() and take a screen shot__
7 |
8 | 
9 |
10 | __2.Create a dataframe that contains the unemployment data. Display the first five rows of the dataframe using the method head() and take a screen shot.__
11 |
12 | 
13 |
14 | __3.Display a dataframe where unemployment was greater than 8.5% . Take a screenshot__
15 |
16 | 
17 |
18 | __4.Use the function make_dashboard to make a dashboard, then take a screen shot.__
19 |
20 | 
21 |
22 | __5.input the link for your notebook generated from Watson Studio .__
23 |
24 | Link to my notebook: https://eu-gb.dataplatform.cloud.ibm.com/analytics/notebooks/v2/7d9c82e3-0c74-4b9c-a43e-68e0ddcc39ba/view?access_token=dad1a6d34f5a266b4ece6b5bb12f51ccd78e88e86426aef4c5496a9c40384fee
25 |
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/5.Databases_and_SQL_for_Data_Science/Readme.md:
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1 | # Databases and SQL for Data Science
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | **Instructors:Rav Ahuja**
12 |
13 | Course link: [Databases and SQL for Data Science](https://www.coursera.org/learn/sql-data-science)
14 |
15 | ## Program Overview
16 | - Week 1: Introduction to Databases and Basic SQL
17 | - Week 2: Advanced SQL
18 | - Week 3: Accessing Databases using Python
19 | - Week 4: Course Assignment
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/5.Databases_and_SQL_for_Data_Science/Week 1 - Introduction to Databases and Basic SQL/Lab 1.sql:
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1 | -- DROP THE TABLE INSTRUCTOR FROM THE DATABASE IN CASE IT ALREADY EXISTS.
2 | DROP TABLE INSTRUCTOR;
3 |
4 |
5 | -- CREATE THE INSTRUCTOR TABLE AS DEFINED ABOVE. HAVE THE INS_ID BE THE PRIMARY KEY, AND ENSURE THE LASTNAME AND FIRSTNAME ARE NOT NULL.
6 | -- (HINT: INS_ID IS OF TYPE INTEGER, COUNTRY OF TYPE CHAR(2), AND REST OF THE FIELDS VARCHAR)
7 | CREATE TABLE INSTRUCTOR (
8 | INS_ID INT NOT NULL PRIMARY KEY,
9 | LASTNAME VARCHAR(30) NOT NULL,
10 | FIRSTNAME VARCHAR(30) NOT NULL,
11 | CITY VARCHAR(30),
12 | COUNTRY CHAR(2)
13 | );
14 |
15 |
16 | -- INSERT ONE ROW INTO THE INSTRUCTOR TABLE FOR THE THE INSTRUCTOR RAV AHUJA.
17 | -- (HINT: VALUES FOR THE CHARACTER FIELDS REQUIRE A SINGE QUOTATION MARK (') BEFORE AND AFTER EACH VALUE)
18 | INSERT INTO INSTRUCTOR VALUES (1, 'AHUJA', 'RAV','TORONTO','CA');
19 |
20 |
21 | -- INSERT TWO ROWS AT ONCE IN THE INSTRUCTOR TABLE FOR INSTRUCTORS RAUL CHONG AND HIMA VASUDEVAN.
22 | -- (HINT: LIST THE VALUES FOR THE SECOND ROW AFTER THE FIRST ROW)
23 | INSERT INTO INSTRUCTOR VALUES
24 | (2, 'CHONG', 'RAUL','TORONTO','CA'),
25 | (3,'VASUDEVAN','HIMA','CHICAGO','US');
26 |
27 |
28 | -- SELECT ALL ROWS FROM THE INSTRUCTOR TABLE.
29 | SELECT * FROM INSTRUCTOR;
30 |
31 |
32 | -- SELECT THE FIRSTNAME, LASTNAME AND COUNTRY WHERE THE CITY IS TORONTO
33 | SELECT FIRSTNAME,LASTNAME,COUNTRY FROM INSTRUCTOR WHERE CITY = 'TORONTO' ;
34 |
35 |
36 | -- UPDATE THE ROW FOR RAV AHUJA AND CHANGE HIS CITY TO MARKHAM.
37 | UPDATE INSTRUCTOR SET CITY='MARKHAM' WHERE INS_ID='1';
38 |
39 |
40 | -- DELETE THE ROW FOR RAUL CHONG FROM THE TABLE.
41 | DELETE FROM INSTRUCTOR WHERE INS_ID='2';
42 |
43 |
44 | -- RETRIEVE ALL ROWS IN THE INSTRUCTOR TABLE.
45 | SELECT * FROM INSTRUCTOR;
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/5.Databases_and_SQL_for_Data_Science/Week 1 - Introduction to Databases and Basic SQL/Readme.md:
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1 | # Week 1 - Introduction to Databases and Basic SQL
2 |
3 | ## Key Concepts
4 | - Explain SQL and Relational Databases
5 | - Create a database instance on the Cloud
6 | - Learn how to write basic SQL statements
7 | - Practice basic SQL statements hands-on on a live database
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/Quiz - String Patterns, Ranges, Sorting and Grouping.docx:
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/Week2Lab1v5.pdf:
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q1.sql:
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1 | -- RETRIEVE ALL EMPLOYEES WHOSE ADDRESS IS IN ELGIN,IL
2 | SELECT * FROM EMPLOYEES
3 | WHERE ADDRESS LIKE '%ELGIN,IL%';
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q2.sql:
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1 | -- RETRIEVE ALL EMPLOYEES WHO WERE BORN DURING THE 1970'S.
2 | SELECT * FROM EMPLOYEES
3 | WHERE B_DATE LIKE '197%';
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q3.sql:
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1 | -- RETRIEVE ALL EMPLOYEES IN DEPARTMENT 5 WHOSE SALARY IS BETWEEN
2 | -- 60000 AND 70000
3 | SELECT * FROM EMPLOYEES
4 | WHERE SALARY BETWEEN 60000 AND 70000;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q4a.sql:
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1 | -- RETRIEVE A LIST OF EMPLOYEES ORDERED BY DEPARTMENT ID.
2 | SELECT * FROM EMPLOYEES
3 | ORDER BY DEP_ID;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q4b.sql:
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1 | -- RETRIEVE A LIST OF EMPLOYEES ORDERED IN DESCENDING ORDER BY
2 | -- DEPARTMENT ID AND WITHIN EACH DEPARTMENT ORDERED ALPHABETICALLY IN
3 | -- DESCENDING ORDER BY LAST NAME.
4 | SELECT * FROM EMPLOYEES
5 | ORDER BY DEP_ID DESC, L_NAME DESC;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q5a.sql:
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1 | -- FOR EACH DEPARTMENT ID RETRIEVE THE NUMBER OF EMPLOYEES IN THE
2 | -- DEPARTMENT.
3 | SELECT DEP_ID, COUNT(*) AS NUM_EMP FROM EMPLOYEES
4 | GROUP BY DEP_ID;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q5b.sql:
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1 | -- For each department retrieve the number of employees in the
2 | -- department, and the average employees salary in the department.
3 |
4 | select DEP_ID, count(*) as num_emp, avg(SALARY) as avg_salary
5 | from employees
6 | group by DEP_ID;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q5c.sql:
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1 | -- Label the computed columns in the result set of Query 5B as
2 | -- “NUM_EMPLOYEES” and “AVG_SALARY”.
3 |
4 | select DEP_ID, count(*) as NUM_EMPLOYEES, avg(SALARY) as AVG_SALARY
5 | from employees
6 | group by DEP_ID;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q5d.sql:
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1 | -- IN QUERY 5C ORDER THE RESULT SET BY AVERAGE SALARY.
2 | SELECT DEP_ID, COUNT(*) AS NUM_EMPLOYEES, AVG(SALARY) AS AVG_SALARY
3 | FROM EMPLOYEES
4 | GROUP BY DEP_ID
5 | ORDER BY AVG_SALARY;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q5e.sql:
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1 | --IN QUERY 5D LIMIT THE RESULT TO DEPARTMENTS WITH FEWER THAN 4 EMPLOYEES.
2 | SELECT DEP_ID, COUNT(*) AS NUM_EMPLOYEES, AVG(SALARY) AS AVG_SALARY
3 | FROM EMPLOYEES
4 | GROUP BY DEP_ID
5 | HAVING COUNT(*) < 4
6 | ORDER BY AVG_SALARY;
7 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/1. String Patterns, Ranges, Sorting, and Grouping/wk2q6.sql:
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1 | -- RETRIEVE A LIST OF EMPLOYEES ORDERED BY DEPARTMENT NAME, AND WITHIN
2 | -- EACH DEPARTMENT ORDERED ALPHABETICALLY IN DESCENDING ORDER BY LAST NAME.
3 |
4 | SELECT *
5 | FROM EMPLOYEES
6 | JOIN DEPARTMENTS
7 | ON DEP_ID = DEPT_ID_DEP
8 | ORDER BY DEP_NAME, L_NAME DESC;
9 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/2. Functions, Sub-Queries, Multiple Tables/PETSALE-CREATE.sql:
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1 | -- Drop the PETSALE table in case it exists
2 | drop table PETSALE;
3 | -- Create the PETSALE table
4 | create table PETSALE (
5 | ID INTEGER PRIMARY KEY NOT NULL,
6 | ANIMAL VARCHAR(20),
7 | QUANTITY INTEGER,
8 | SALEPRICE DECIMAL(6,2),
9 | SALEDATE DATE
10 | );
11 | -- Insert saple data into PETSALE table
12 | insert into PETSALE values
13 | (1,'Cat',9,450.09,'2018-05-29'),
14 | (2,'Dog',3,666.66,'2018-06-01'),
15 | (3,'Dog',1,100.00,'2018-06-04'),
16 | (4,'Parrot',2,50.00,'2018-06-04'),
17 | (5,'Dog',1,75.75,'2018-06-10'),
18 | (6,'Hamster',6,60.60,'2018-06-11'),
19 | (7,'Cat',1,44.44,'2018-06-11'),
20 | (8,'Goldfish',24,48.48,'2018-06-14'),
21 | (9,'Dog',2,222.22,'2018-06-15')
22 |
23 | ;
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/2. Functions, Sub-Queries, Multiple Tables/Quiz - Functions, Sub-Queries, Multiple Tables.docx:
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Readme.md:
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1 | # Week 2 - Advanced SQL
2 |
3 | By the end of this module, you will learn the following: (1) Learn how to use string patterns and ranges to search data and how to sort and group data in result sets. (2) Learn how to work with multiple tables in a relational database using join operations.
4 |
5 | ## Key Concepts
6 | - Explain how to use string patterns and ranges in SQL queries
7 | - Demonstrate how to sort and order result sets
8 | - Practice use of grouping data in result sets
9 | - Employ Built-in functions in Queries
10 | - Demonstrate how to write sub-queries and nested selects
11 | - Build queries to access multiple tables
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Table Data/Departments.csv:
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1 | 2,Architect Group,30001,L0001
2 | 5,Software Group,30002,L0002
3 | 7,Design Team,30003,L0003
4 | 5,Software Group,30004,L0004
5 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Table Data/Employees.csv:
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1 | E1001,John,Thomas,123456,01/09/1976,M,"5631 Rice, OakPark,IL",100,100000,30001,2
2 | E1002,Alice,James,123457,07/31/1972,F,"980 Berry ln, Elgin,IL",200,80000,30002,5
3 | E1003,Steve,Wells,123458,08/10/1980,M,"291 Springs, Gary,IL",300,50000,30002,5
4 | E1004,Santosh,Kumar,123459,07/20/1985,M,"511 Aurora Av, Aurora,IL",400,60000,30004,5
5 | E1005,Ahmed,Hussain,123410,01/04/1981,M,"216 Oak Tree, Geneva,IL",500,70000,30001,2
6 | E1006,Nancy,Allen,123411,02/06/1978,F,"111 Green Pl, Elgin,IL",600,90000,30001,2
7 | E1007,Mary,Thomas,123412,05/05/1975,F,"100 Rose Pl, Gary,IL",650,65000,30003,7
8 | E1008,Bharath,Gupta,123413,05/06/1985,M,"145 Berry Ln, Naperville,IL",660,65000,30003,7
9 | E1009,Andrea,Jones,123414,07/09/1990,F,"120 Fall Creek, Gary,IL",234,70000,30003,7
10 | E1010,Ann,Jacob,123415,03/30/1982,F,"111 Britany Springs,Elgin,IL",220,70000,30004,5
11 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Table Data/Jobs.csv:
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1 | 100,Sr. Architect,60000,100000
2 | 200,Sr. Software Developer,60000,80000
3 | 300,Jr.Software Developer,40000,60000
4 | 400,Jr.Software Developer,40000,60000
5 | 500,Jr. Architect,50000,70000
6 | 600,Lead Architect,70000,100000
7 | 650,Jr. Designer,60000,70000
8 | 660,Jr. Designer,60000,70000
9 | 234,Sr. Designer,70000,90000
10 | 220,Sr. Designer,70000,90000
11 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Table Data/JobsHistory.csv:
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1 | E1001,08/01/2000,100,2
2 | E1002,08/01/2001,200,5
3 | E1003,08/16/2001,300,5
4 | E1004,08/16/2000,400,5
5 | E1005,05/30/2000,500,2
6 | E1006,08/16/2001,600,2
7 | E1007,05/30/2002,650,7
8 | E1008,05/06/2010,660,7
9 | E1009,08/16/2016,234,7
10 | E1010,08/16/2016,220,5
11 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Table Data/Locations.csv:
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1 | L0001,2
2 | L0002,5
3 | L0003,7
4 |
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/5.Databases_and_SQL_for_Data_Science/Week 2 - Advanced SQL/Table Data/Script_Create_Tables.sql:
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1 | ------------------------------------------
2 | --DDL statement for table 'HR' database--
3 | --------------------------------------------
4 |
5 | CREATE TABLE EMPLOYEES (
6 | EMP_ID CHAR(9) NOT NULL,
7 | F_NAME VARCHAR(15) NOT NULL,
8 | L_NAME VARCHAR(15) NOT NULL,
9 | SSN CHAR(9),
10 | B_DATE DATE,
11 | SEX CHAR,
12 | ADDRESS VARCHAR(30),
13 | JOB_ID CHAR(9),
14 | SALARY DECIMAL(10,2),
15 | MANAGER_ID CHAR(9),
16 | DEP_ID CHAR(9) NOT NULL,
17 | PRIMARY KEY (EMP_ID));
18 |
19 | CREATE TABLE JOB_HISTORY (
20 | EMPL_ID CHAR(9) NOT NULL,
21 | START_DATE DATE,
22 | JOBS_ID CHAR(9) NOT NULL,
23 | DEPT_ID CHAR(9),
24 | PRIMARY KEY (EMPL_ID,JOBS_ID));
25 |
26 | CREATE TABLE JOBS (
27 | JOB_IDENT CHAR(9) NOT NULL,
28 | JOB_TITLE VARCHAR(15) ,
29 | MIN_SALARY DECIMAL(10,2),
30 | MAX_SALARY DECIMAL(10,2),
31 | PRIMARY KEY (JOB_IDENT));
32 |
33 | CREATE TABLE DEPARTMENTS (
34 | DEPT_ID_DEP CHAR(9) NOT NULL,
35 | DEP_NAME VARCHAR(15) ,
36 | MANAGER_ID CHAR(9),
37 | LOC_ID CHAR(9),
38 | PRIMARY KEY (DEPT_ID_DEP));
39 |
40 | CREATE TABLE LOCATIONS (
41 | LOCT_ID CHAR(9) NOT NULL,
42 | DEP_ID_LOC CHAR(9) NOT NULL,
43 | PRIMARY KEY (LOCT_ID,DEP_ID_LOC));
44 |
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/5.Databases_and_SQL_for_Data_Science/Week 3 - Accessing Databases using Python/Create-Db2-Service-Credentials.pdf:
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/5.Databases_and_SQL_for_Data_Science/Week 3 - Accessing Databases using Python/LAB 1 -Connecting-v4-py.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "\n",
8 | "\n",
9 | "
Lab: Connect to Db2 database on Cloud using Python
8 |
9 |
10 |
11 | **Instructors: Joseph Santarcangelo**
12 |
13 | Course Link: [Data Analysis with Python](https://www.coursera.org/learn/data-analysis-with-python/)
14 |
15 | ## Program Overview
16 | - Importing Datasets
17 | - Data Wrangling
18 | - Exploratory Data Analysis
19 | - Model Development
20 | - Model Evaluation
21 | - Final Assignment
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/6.Data_Analysis_with_Python/Week 1 - Importing Datasets/Quiz 1 - Understanding the Data.docx:
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/6.Data_Analysis_with_Python/Week 1 - Importing Datasets/Quiz 2 - Python Packages for Data Science.docx:
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/6.Data_Analysis_with_Python/Week 1 - Importing Datasets/Quiz 3 - Importing and Exporting Data in Python.docx:
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/6.Data_Analysis_with_Python/Week 1 - Importing Datasets/Quiz 4 - Getting Started Analyzing Data in Python.docx:
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/6.Data_Analysis_with_Python/Week 1 - Importing Datasets/Quiz 5 - Importing Datasets.docx:
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/6.Data_Analysis_with_Python/Week 1 - Importing Datasets/Readme.md:
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1 | # Importing Datasets
2 |
3 | ## Key Concepts
4 | - Understanding the Data
5 | - Importing and Exporting Data in Python
6 | - Getting Started Analyzing Data in Python
7 | - Python Packages for Data Science
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/6.Data_Analysis_with_Python/Week 2 - Data Wrangling/Quiz 1 - Dealing with Missing Values in Python.docx:
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/6.Data_Analysis_with_Python/Week 2 - Data Wrangling/Quiz 2 - Data Formatting in Python.docx:
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/6.Data_Analysis_with_Python/Week 2 - Data Wrangling/Quiz 3 - Data Normalization in Python.docx:
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/6.Data_Analysis_with_Python/Week 2 - Data Wrangling/Quiz 4 - Turning categorical variables into quantitative variables in Python.docx:
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/6.Data_Analysis_with_Python/Week 2 - Data Wrangling/Quiz 5 -Data Wrangling.docx:
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/6.Data_Analysis_with_Python/Week 2 - Data Wrangling/Readme.md:
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1 | # Data Wrangling
2 |
3 | ## Key Concepts
4 | - Identify and Handle Missing Values
5 | - Data Formatting
6 | - Data Normalization
7 | - Binning
8 | - Indicator variables
9 |
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/6.Data_Analysis_with_Python/Week 3 - Exploratory Data Analysis/Quiz 1 - Descriptive Statistics.docx:
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/6.Data_Analysis_with_Python/Week 3 - Exploratory Data Analysis/Quiz 2 - GroupBy in Python.docx:
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/6.Data_Analysis_with_Python/Week 3 - Exploratory Data Analysis/Quiz 3 - Correlation.docx:
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/6.Data_Analysis_with_Python/Week 3 - Exploratory Data Analysis/Quiz 4 - Correlation - Statistics.docx:
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/6.Data_Analysis_with_Python/Week 3 - Exploratory Data Analysis/Quiz 5 - Exploratory Data Analysis.docx:
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/6.Data_Analysis_with_Python/Week 3 - Exploratory Data Analysis/README.md:
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1 | # Exploratory Data Analysis
2 | Preliminary step in Data Analysis is to:
3 | - Summarize main characteristics of the data.
4 | - Gain better understanding of the data set.
5 | - Uncover relationships between the variables.
6 | - Extract important variables.
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/6.Data_Analysis_with_Python/Week 4 - Model Development/Quiz 1 - Linear Regression and Multiple Linear Regression.docx:
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/6.Data_Analysis_with_Python/Week 4 - Model Development/Quiz 2 - Model Evaluation using Visualization.docx:
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/6.Data_Analysis_with_Python/Week 4 - Model Development/Quiz 3 - Polynomial Regression and Pipelines.docx:
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/6.Data_Analysis_with_Python/Week 4 - Model Development/Quiz 4 - Measures for In-Sample Evaluation.docx:
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/6.Data_Analysis_with_Python/Week 4 - Model Development/Quiz 5 - Model Development.docx:
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/6.Data_Analysis_with_Python/Week 4 - Model Development/README.md:
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1 | # Model Development
2 |
3 | ## Key Concepts
4 | - Simple and Multiple Linear Regression
5 | - Model Evaluation Using Visualization
6 | - Polynomial Regression and Pipelines
7 | - R-squared and MSE for In-Sample Evaluation
8 | - Prediction and Decision Making
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/6.Data_Analysis_with_Python/Week 5 - Model Evaluation/Quiz 1 - Model Evaluation.docx:
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/6.Data_Analysis_with_Python/Week 5 - Model Evaluation/Quiz 2 - Overfitting, Underfitting and Model Selection.docx:
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/6.Data_Analysis_with_Python/Week 5 - Model Evaluation/Quiz 3 - Ridge Regression.docx:
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/6.Data_Analysis_with_Python/Week 5 - Model Evaluation/Quiz 4 - Model Refinement.docx:
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/6.Data_Analysis_with_Python/Week 5 - Model Evaluation/Readme.md:
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1 | # Model Evaluation and Refinement
2 |
3 | ## Key Concepts
4 | - Model Evaluation
5 | - Over-fitting, Under-fitting and Model Selection
6 | - Ridge Regression
7 | - Grid Search
8 | - Model Refinement
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/6.Data_Analysis_with_Python/Week 6 - Final Assignment/README.md:
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1 | # Final Assignment
2 |
3 | ## Key Concepts
4 | - Create a Jupyter notebook
5 | - Housing price data
6 |
7 | # Peer-graded Assignment: House Sales in King County, USA
8 |
9 |
10 | **Question 1) Display the data types of each column using the attribute dtype, then take a screenshot and submit it, include your code in the image.**
11 | 
12 |
13 |
14 | **Question 2) Drop the columns "id" and "Unnamed: 0" from axis 1 using the method drop(), then use the method describe() to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the inplace parameter is set to True.**
15 | 
16 |
17 |
18 | **Question 3) Use the method value_counts to count the number of houses with unique floor values, use the method .to_frame() to convert it to a dataframe**
19 | 
20 |
21 | **Question 4) Use the function boxplot in the seaborn library to produce a plot that can be used to determine whether houses with a waterfront view or without a waterfront view have more price outliers.**
22 | 
23 |
24 | **Question 5) Use the function regplot in the seaborn library to determine if the feature sqft_above is negatively or positively correlated with price. Take a screenshot of the plot and the code used to generate it.**
25 | 
26 |
27 | **Question 6) Fit a linear regression model to predict the price using the feature 'sqft_living' then calculate the R^2. Take a screenshot of your code and the value of the R^2.**
28 | 
29 |
30 |
31 | **Question 7) Fit a linear regression model to predict the 'price' using the list of features:**
32 |
33 | - **"floors"**
34 | - **"waterfront"**
35 | - **"lat"**
36 | - **"bedrooms"**
37 | - **"sqft_basement"**
38 | - **"view"**
39 | - **"bathrooms"**
40 | - **"sqft_living15"**
41 | - **"sqft_above"**
42 | - **"grade"**
43 | - **"sqft_living"**
44 | - **The calculate the R^2. Take a screenshot of your code and the value of the R^2.**
45 | 
46 |
47 | **Question 8) Create a pipeline object that scales the data performs a polynomial transform and fits a linear regression model. Fit the object using the features in the question above, then fit the model and calculate the R^2. Take a screenshot of your code and the R^2.**
48 | 
49 |
50 | **Question 9) Create and fit a Ridge regression object using the training data, setting the regularization parameter to 0.1 and calculate the R^2 using the test data. Take a screenshot for your code and the R^2**
51 | 
52 |
53 | **Question 10) Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, setting the regularisation parameter to 0.1. Calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2.**
54 | 
55 |
56 | **Share the link for your notebook:** [URL](https://eu-gb.dataplatform.cloud.ibm.com/analytics/notebooks/v2/3cd171cd-9b26-4dd3-b2d5-d02c4dcda826/view?access_token=aad17c79b4b9813b71caf706b64943a36be90d47b8097c031d005387c7bc8a86)
57 |
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/7.Data_Visualization_with_Python/Readme.md:
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1 | # Data Visualization with Python
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | **Instructors:Alex Aklson**
12 |
13 | Course Link: [Data Visualization with Python](https://www.coursera.org/learn/python-for-data-visualization/)
14 |
15 | ## Syllabus
16 |
17 | ### Week 1 - Introduction to Data Visualization Tools
18 | - Introduction to Data Visualization
19 | - Introduction to Matplotlib
20 | - Basic Plotting with Matplotlib
21 | - Dataset on Immigration to Canada
22 | - Line Plots
23 | - Lab: Introduction to Matplotlib and Line Plots
24 | - Quiz: Introduction to Data Visualization Tools
25 |
26 | ### Week 2 - Basic and Specialized Visualization Tools
27 | - Area Plots
28 | - Histograms
29 | - Bar Charts
30 | - Pie Charts
31 | - Box Plots
32 | - Scatter Plots
33 | - Bubble Plots
34 | - Lab: Basic Visualization Tools
35 | - Lab: Specialized Visualization Tools
36 | - Quiz: Basic Visualization Tools
37 | - Quiz: Specialized Visualization Tools
38 |
39 | ### Week 3 - Advanced Visualizations and Geospatial Data
40 | - Waffle Charts
41 | - Word Clouds
42 | - Seaborn and Regression Plots
43 | - Introduction to Folium and Map Styles
44 | - Maps with Markers
45 | - Choropleth Maps
46 | - Lab: Advanced Visualization Tools
47 | - Lab: Creating Maps and Visualizing Geospatial Data
48 | - Quiz: Advanced Visualization Tools
49 | - Quiz: Visualizing Geospatial Data
50 | - Peer-review Assignment
51 |
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1 | # Introduction to Data Visualization
2 |
3 | **Instructor: Alex Aklson**
4 |
5 | ## Key Concepts
6 |
7 | Learn about:
8 | - Data visualization and some of the best practices to keep in mind when creating plots and visuals.
9 | - History and the architecture of Matplotlib.
10 | - Basic plotting with Matplotlib.
11 | - Dataset on immigration to Canada, which will be used extensively throughout the course.
12 | - Read csv files into a pandas dataframe and process and manipulate the data in the dataframe.
13 | - Generate line plots using Matplotlib.
14 |
15 | ## Main Idea
16 | Less is more effective, attractive and impactive.
17 | [Dark Horse Analytics](https://www.darkhorseanalytics.com) are good at conveying this idea especially in geospatial graphs.
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/7.Data_Visualization_with_Python/Week 2 - Basic and Specialized Visualization Tools/Readme.md:
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1 | # Basic and Specialized Visualization Tools
2 |
3 | **Instructor: Alex Aklson**
4 |
5 | ## Key Concepts
6 |
7 | Create with Matplotlib
8 | - Area plots
9 | - Histograms
10 | - Bar charts
11 | - Pie charts
12 | - Box plots
13 | - Scatter plots
14 | - Bubble plots
15 |
16 | Many Data Scientists are vocal about how Pie charts don't convey information correctly and believe that a Bar chart can do the same but in a more informative and effective way.
17 |
18 | Bubble plots
19 |
20 | They are a variation of scatter plots displaying 3 dimensions (x,y,z)
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/7.Data_Visualization_with_Python/Week 3 - Advanced Visualizations and Geospatial Data/1. Advanced Visualization Tools/Readme.md:
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1 | # Advanced Visualization Tools
2 |
3 | ## Waffle Chart
4 | - A waffle chart is am interesting visualization that is normally created to display progress towards goals.
5 | - Part of the Folium package.
6 | - An easy example to remember a Waffle Chart is the GitHub Activity chart.
7 |
8 | 
9 |
10 | Python package for generating `waffle charts` called [PyWaffle](https://github.com/ligyxy/PyWaffle).
11 |
12 | ## Word Clouds
13 | - A word cloud is a depiction of the frequency of different words in some textual data.
14 | - Python package by Andreas Mueller
15 |
16 | 
17 |
18 | ## Seaborn
19 | - Seaborn is a Python visualization library based on Matplotlib
20 |
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1 | ## Data Visualization Final Assignment
2 |
3 | Final assignment of Data visualization with Python contains 4 questions where Questions 1 and 2 are on bar charts. Questions 3 and 4 are on Choropleth maps generated using Folium.
4 |
5 | The [notebook](Data_Visualization_Final_Assingment.ipynb) has detailed descriptions of the questions and answers.
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/7.Data_Visualization_with_Python/Week 3 - Advanced Visualizations and Geospatial Data/Readme.md:
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1 | # Advanced Visualizations and Geospatial Data
2 |
3 | **Instructor: Alex Aklson**
4 |
5 | ## Key Concepts
6 |
7 | Learn about:
8 | - Advanced visualization tools such as waffle charts and word clouds, and how to create them.
9 | - Seaborn, which is another visualization library, and how to use it to generate attractive regression plots.
10 | - Folium, which is another visualization library, designed especially for visualizing geospatial data.
11 | - Use Folium to create maps of different regions of the world and how to superimpose markers on top of a map.
12 | - Use Folium to create choropleth maps.
13 |
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/8.Machine_Learning_with_Python/Readme.md:
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1 | # Machine Learning with Python
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 | **Instructors: SAEED AGHABOZORGI, Joseph Santarcangelo**
11 |
12 | **Course link:** [Machine Learning with Python](https://www.coursera.org/learn/machine-learning-with-python/)
13 |
14 | ## Program Overview
15 | - Introduction to Machine Learning
16 | - Regression
17 | - Classification
18 | - Clustering
19 | - Recommendation Systems
20 | - Final Assignment
21 |
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1 | # Introduction to Machine Learning
2 |
3 | **Instructors: SAEED AGHABOZORGI & Joseph Santarcangelo**
4 |
5 | In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.
6 |
7 |
8 | ## Key Concepts
9 | - To give examples of Machine Learning
10 | - To demonstrate the Python libraries for Machine Learning
11 | - To classify Supervised vs. Unsupervised algorithms
12 |
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/8.Machine_Learning_with_Python/Week 2 - Regression/Readme.md:
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1 | # Regression
2 |
3 | **Instructors: SAEED AGHABOZORGI & Joseph Santarcangelo**
4 |
5 | In this week, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy
6 |
7 | # Key Concepts
8 | - To understand the basics of regression
9 | - To apply Simple and Multiple, Linear and Non-Linear Regression on a dataset for estimation.
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1 | # Classification
2 |
3 | ## Key Concepts
4 | - To understand different Classification methods.
5 | - To apply Classification algorithms on various datasets to solve real world problems.
6 | - To understand evaluation methods in Classification.
7 |
8 | ## Classification methods
9 | - K-Nearest Neighbours
10 | - Decision Trees
11 | - Logistic Regression
12 | - Support Vector Machine
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/8.Machine_Learning_with_Python/Week 6 - Final Assignment/Readme.md:
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1 | # Final Assignment
2 |
3 | ## Review Criteria
4 |
5 | This final project will be graded by your peers who are completing this course during the same session. This project is worth **25** marks of your total grade, broken down as follows:
6 |
7 | 1. Building model using KNN, finding the best k and accuracy evaluation (**7 marks**)
8 | 2. Building model using Decision Tree, finding the best k and accuracy evaluation (**6 marks**)
9 | 3. Building model using SVM, finding the best k and accuracy evaluation (**6 marks**)
10 | 4. Building model using Logistic Regression, finding the best k and accuracy evaluation (**6 marks**)
11 |
12 | [Link to notebook](https://eu-gb.dataplatform.cloud.ibm.com/analytics/notebooks/v2/4d6db394-4d29-4627-aa4b-28bd502c368e/view?access_token=0aa62299e7b37c3b9040fbada756fcaa5bda15542045fdc0a4beb29e234204f3)
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/9.Applied_Data_Science_Capstone/Readme.md:
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1 | # Applied Data Science Capstone
2 |
3 |
4 |
5 |
6 |
7 |
8 | **Instructor: Alex Aklson**
9 |
10 | Link to course: https://www.coursera.org/learn/applied-data-science-capstone/home/welcome
11 |
12 | ## Syllabus
13 |
14 | ### Week 1 - Introduction to Capstone Project
15 | - Introduction to Capstone Project
16 | - Location Data Providers
17 | - Signing-up for a Watson Studio Account
18 | - Peer-review Assignment: Capstone Project Notebook
19 |
20 | ### Week 2 - Foursquare API
21 | - Introduction to Foursquare
22 | - Getting Foursquare API Credentials
23 | - Using Foursquare API
24 | - Lab: Foursquare API
25 | - Quiz: Foursquare API
26 |
27 | ### Week 3 - Neighborhood Segmentation and Clustering
28 | - Clustering
29 | - Lab: Clustering
30 | - Lab: Segmenting and Clustering Neighborhoods in New York City
31 | - Peer-review Assignment: Segmenting and Clustering Neighborhoods in Toronto
32 |
33 | ### Week 4 - Capstone Project
34 |
35 | ### Week 5 - Capstone Project (Cont'd)
36 |
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/9.Applied_Data_Science_Capstone/Week 1 - Introduction/Data_Science_Capstone_Project.ipynb:
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/9.Applied_Data_Science_Capstone/Week 1 - Introduction/Readme.md:
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1 | # Introduction
2 |
3 | ## Key Concepts
4 | - Learn about the problem that you will be working on in this capstone course
5 | - Learn how to get started with Git and Github
6 | - Apply your data analysis and machine learning skills to solve a problem using real world data
7 | - Create a project on Watson Studio, create a project, start a notebook and share it with your peers.
8 |
9 | ## Peer-graded Assignment: Capstone Project Notebook
10 |
11 | ### Overview
12 | In this assignment, you will be asked to create a new repository on your Github account, and to create a Jupyter Notebook and submit a shareable link to it for peer evaluation.
13 |
14 | ### Instructions
15 | Create a new repository on your GitHub account and name it Coursera_Capstone.
16 |
17 | Now, start a Jupyter Notebook using any platform that you are comfortable with and do the following:
18 |
19 | 1. Write some markdown to explain that this notebook will be mainly used for the capstone project.
20 | 2. Import the _pandas_ library as pd.
21 | 3. Import the Numpy library as np.
22 | 4. Print the following the statement: Hello Capstone Project Course!
23 |
24 | Push the Notebook to your Github repository and submit a link to the notebook on your Github repository.
25 |
26 | **Link to my notebook**:https://github.com/Thomas-George-T/A-Tale-of-Two-Cities/blob/master/DS_Capstone_template.ipynb
27 |
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/9.Applied_Data_Science_Capstone/Week 2 - Foursquare API/Readme.md:
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1 | # Foursquare API
2 |
3 | In this module, you will learn in details about Foursquare, which is the location data provider we will be using in this course, and its API. Essentially, you will learn how to create a Foursquare developer account, and use your credentials to search for nearby venues of a specific type, explore a particular venue, and search for trending venues around a location.
4 |
5 | ## Key Concepts
6 | - Learn about Foursquare and its API.
7 | - Learn how to create a Foursquare developer account.
8 | - Create a Foursquare developer account.
9 | - Learn how to use the Foursquare API to search for a specific type of venues, explore a given venue, and search for trending venue around a location.
10 | - Complete a lab to better understand how to make calls to the Foursquare API and retrieve location data from its database.
11 |
12 | ## Known Issues & Fixes
13 |
14 | - [Resolution to Week 2 Lab Problem (Search Foursquare User)](https://www.coursera.org/learn/applied-data-science-capstone/discussions/weeks/2/threads/-LJs_M9XQ-KybPzPVzPi6w?page=2)
15 |
16 | - [Get Relevant part of JSON](https://www.coursera.org/learn/applied-data-science-capstone/discussions/weeks/2/threads/YV4rUtOHQbqeK1LTh8G6FQ/replies/U8jSf0TdSuOI0n9E3frjqA)
17 |
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/9.Applied_Data_Science_Capstone/Week_3_-_Neighborhood_Segmentation_and_Clustering/Readme.md:
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1 | # Neighbourhood Segmentation and Clustering
2 |
3 | In this module, you will learn about k-means clustering, which is a form of unsupervised learning. Then you will use clustering and the Foursquare API to segment and cluster the neighborhoods in the city of New York. Furthermore, you will learn how to scrape website and parse HTML code using the Python package Beautifulsoup, and convert data into a pandas dataframe.
4 |
5 | ## Key Concepts
6 | - Hone your communication skills by documenting your work and submitting a full project report and a presentation or a blog post
7 | - Decide what is the suitable algorithm for the capstone project
8 | - Learn how to deal with missing attributes' values and imbalanced data .
9 | - Learn about CRISP DM framework for machine Learning
10 |
11 | ## My Final Assignment Notebook
12 |
13 | [Notebook](https://nbviewer.jupyter.org/github/Thomas-George-T/IBM-Data-Science-Professional-Certification/blob/master/9.Applied_Data_Science_Capstone/Week_3_-_Neighborhood_Segmentation_and_Clustering/Applied_Capstone_Week_3_Assignment.ipynb)
14 |
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/9.Applied_Data_Science_Capstone/Week_4_-_The Battle of Neighborhoods/Readme.md:
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1 | # The Battle of Neighborhoods
2 |
3 | In this module, you will start working on the capstone project. You will clearly define a problem and discuss the data that you will be using to solve the problem.
4 |
5 | ## Key Concepts
6 | - Define a problem for your capstone project.
7 | - Finding the data that you will use for the capstone project.
8 |
9 | ## Question 1
10 |
11 | Clearly define a problem or an idea of your choice, where you would need to leverage the Foursquare location data to solve or execute. Remember that data science problems always target an audience and are meant to help a group of stakeholders solve a problem, so make sure that you explicitly describe your audience and why they would care about your problem.
12 |
13 | This submission will eventually become your **Introduction/Business Problem** section in your final report. So I recommend that you push the report (having your Introduction/Business Problem section only for now) to your Github repository and submit a link to it.
14 |
15 | ## Question 2
16 |
17 | Describe the data that you will be using to solve the problem or execute your idea. Remember that you will need to use the Foursquare location data to solve the problem or execute your idea. You can absolutely use other datasets in combination with the Foursquare location data. So make sure that you provide adequate explanation and discussion, with examples, of the data that you will be using, even if it is only Foursquare location data.
18 |
19 | This submission will eventually become your **Data** section in your final report. So I recommend that you push the report (having your Data section) to your Github repository and submit a link to it.
20 |
21 | **Link to my Notebook:** https://github.com/Thomas-George-T/A-Tale-of-Two-Cities/blob/master/DS_Report.ipynb
22 |
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/9.Applied_Data_Science_Capstone/Week_5_-The Battle of Neighborhoods (Cont'd)/Readme.md:
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1 | # The Battle of Neighborhoods (Cont'd)
2 |
3 | In this module, you will carry out all the remaining work to complete your capstone project. You will submit a report of your project for peer evaluation.
4 |
5 | ## Key Concepts
6 | - Carry out the remaining work to complete the capstone project.
7 | - Submit a link to your project notebook and a complete project report.
8 |
9 | ## Instructions
10 |
11 | In this week, you will continue working on your capstone project. Please remember by the end of this week, you will need to submit the following:
12 |
13 | 1. A full report consisting of all of the following components (**15 marks**):
14 |
15 | - Introduction where you discuss the business problem and who would be interested in this project.
16 | - Data where you describe the data that will be used to solve the problem and the source of the data.
17 | - Methodology section which represents the main component of the report where you discuss and describe any exploratory data analysis that you did, any inferential statistical testing that you performed, if any, and what machine learnings were used and why.
18 | - Results section where you discuss the results.
19 | - Discussion section where you discuss any observations you noted and any recommendations you can make based on the results.
20 | - Conclusion section where you conclude the report.
21 |
22 | 2. A link to your Notebook on your GitHub repository pushed showing your code. (**15 marks**)
23 |
24 | 3. Your choice of a presentation or blog post. (**10 marks**)
25 |
26 | **Here are examples of previous outstanding submissions that should give you an idea of what your report would look like, what your notebook would look like in terms of clean, clear, and well-commented code, and what your presentation would look like or your blog post would look like:**
27 |
28 | 1. **Report:** https://cocl.us/coursera_capstone_report
29 | 2. **Notebook:** https://cocl.us/coursera_capstone_notebook
30 | 3. **Presentation:** https://cocl.us/coursera_capstone_presentation
31 | 4. **Blog post:** https://cocl.us/coursera_capstone_blogpost
32 |
33 | ## My Work
34 |
35 | 1. **Report:** https://github.com/Thomas-George-T/A-Tale-of-Two-Cities/blob/master/DS_Report.ipynb
36 | 2. **Notebook:** https://github.com/Thomas-George-T/A-Tale-of-Two-Cities/blob/master/Tale_of_Two_Cities_A_Data_Science_Take.ipynb
37 | 3. **Blog Post:** https://medium.com/@tgt555/a-tale-of-two-cities-clustering-neighborhoods-of-london-and-paris-5328f69cd8b6?sk=79abb05dd7eed6157e7a87c7c52a98b4
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/Readme.md:
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1 | 
2 | 
3 | 
4 |
5 | # IBM Data Science Professional Certificate
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 | ## About this Professional Certificate
14 |
15 | Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a **career in data science** or **machine learning** develop career-relevant skills and experience.
16 |
17 | It’s a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – **no prior knowledge of computer science or programming languages required** – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist.
18 |
19 | The program consists of 9 online courses that will provide you with the **latest job-ready tools and skills**, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.
20 |
21 | Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.
22 |
23 | In addition to earning a Professional Certificate from Coursera, you'll also receive a **digital badge from IBM** recognizing your proficiency in data science.
24 |
25 | ## Applied Learning Project
26 | This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you **practical skills with applicability to real jobs**, including:
27 |
28 | **Tools:** Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
29 |
30 | **Libraries:** Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
31 |
32 | **Projects:** random album generator, predict housing prices, best classifier model, battle of neighborhoods
33 |
34 | Read more below:
35 |
36 | **Course Link:** [IBM Data Science Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-science)
37 |
38 | ## Instructors
39 | - Alex Aklson
40 | - Polong Lin
41 | - Romeo Kienzler
42 | - Svetlana Levitan
43 | - Joseph Santarcangelo
44 | - Rav Ahuja
45 | - SAEED AGHABOZORGI
46 |
47 | ## Specialization Overview
48 |
49 | | Sr. No | Course |
50 | |:------:|----------------------------------------------------------------------------|
51 | | 1. | [What is Data Science?](1.What_is_Data_Science) |
52 | | 2. | [Tools for Data Science](2.Tools_for_Data_Science) |
53 | | 3. | [Data Science Methodology](3.Data_Science_Methodology) |
54 | | 4. | [Python for Data Science and AI](4.Python_for_Data_Science_and_AI) |
55 | | 5. | [Databases and SQL for Data Science](5.Databases_and_SQL_for_Data_Science) |
56 | | 6. | [Data Analysis with Python](6.Data_Analysis_with_Python) |
57 | | 7. | [Data Visualization with Python](7.Data_Visualization_with_Python) |
58 | | 8. | [Machine Learning with Python](8.Machine_Learning_with_Python) |
59 | | 9. | [Applied Data Science Capstone](9.Applied_Data_Science_Capstone) |
60 |
61 | ## Resources
62 |
63 | #### Capstone
64 | - [A Tale of Two Cities: Clustering neighborhoods of London and Paris](https://medium.com/analytics-vidhya/a-tale-of-two-cities-clustering-neighborhoods-of-london-and-paris-5328f69cd8b6)
65 |
66 | #### Data Science Toolkit
67 | - [IBM Developer Skills Network](https://labs.cognitiveclass.ai/login?logout=true) : Data Science toolkit including JupyterLab, JupterNotebook, Apache Zeppelin, RStudio, etc. in your browser.
68 | - [Google Colab](https://colab.research.google.com) : Practice Python in your browser and execute Machine learning Models with Google Colab.
69 | - [Online Notebook viewer](https://nbviewer.jupyter.org) : View jupyter notebooks online.
70 | - [Foursquare API](https://developer.foursquare.com) : Foursquare API developer credentials portal.
71 | - [ArcGis](https://developers.arcgis.com/labs/python/search-for-an-address/) : Search for an address with Python.
72 |
73 | #### Useful Functions
74 |
75 | - [Check for NaN in Pandas DataFrame](https://datatofish.com/check-nan-pandas-dataframe/)
76 | - [Pandas get dummies or One Hot encoding](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html)
77 | - [Rename a column in Pandas in Python](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html)
78 | - [Data cleaning with Pandas](https://towardsdatascience.com/data-cleaning-with-python-using-pandas-library-c6f4a68ea8eb)
79 | - [RStudio](https://cran.rstudio.com)
80 | - [RStudio package: Shiny](https://shiny.rstudio.com/)
81 | - [RStudio package: leaflet](https://rstudio.github.io/leaflet/)
82 | - [Importing JSON and HTML into pandas](https://www.datacamp.com/community/tutorials/importing-data-into-pandas)
83 |
84 |
85 | #### Useful Resources
86 | - [End to End Machine learning library](https://e2eml.school/blog.html#skills)
87 | - [Beginning with Exploratory data Analysis (EDA)](https://nbviewer.jupyter.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb)
88 | - [In depth Exploratory data Analysis (EDA)](https://www.kaggle.com/lalitharajesh/iris-dataset-exploratory-data-analysis)
89 | - [K-means Clustering](https://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb)
90 | - [Understanding K-Means Clustering](https://www.appliedaicourse.com/blog/k-means-clustering/)
91 |
92 | #### Building Portfolio and Real world Experience
93 | - [Building an effective Data science Portfolio](https://towardsdatascience.com/how-to-build-an-effective-data-science-portfolio-56d19b885aa8)
94 | - [Getting real life Data science experience](https://towardsdatascience.com/3-ways-to-get-real-life-data-science-experience-before-your-first-job-545db436ef12)
95 | - [How not to build a data science project](https://towardsdatascience.com/how-not-to-build-a-data-science-project-baa494d98da4)
96 |
97 |
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7 | ![](\"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\")
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8 | "\n",
9 | "