Course curriculum

    1. Execution Plan

    2. Course Outline

    3. Python Overview

    4. What is Google Colaboratory?

    5. Working in Colab: Simple Math

    6. Primitive Data Types

    7. Declaring Variables

    8. Variables and Primitive Data Types (Practice)

    9. Functional Programming and OOP

    10. Lists and Tuples

    11. Lists and Tuples (Practice)

    12. Dictionaries

    13. Dictionaries (Practice)

    14. For Loops

    15. For Loops Assignment (Core)

    16. Conditionals

    17. Conditionals Exercise (Practice)

    18. Intro to Custom Functions

    19. Debugging

    20. Debugging Exercise (Practice)

    21. Custom Functions

    22. Measures of Central Tendency

    23. Writing a Function to Calculate the Mean

    24. Intro to Python Libraries

    25. Functions for Measures of Central Tendency

    26. Central Tendency Functions (Core)

    27. Using Markdown Effectively

    28. Project 1 - Part 1 (Core)

    29. Interview Questions (Optional)

    30. Sets (Optional)

    31. While Loops (Optional)

    1. Intro to Pandas DataFrames

    2. Working with DataFrames

    3. Saving Files to Google Drive

    4. Loading Data with Pandas

    5. Loading Data (Practice):

    6. Saving a dataframe as a .csv

    7. Slicing

    8. Slicing (Practice)

    9. Filtering

    10. Filtering (Practice)

    11. Pandas Groupby

    12. Groupby (Core)

    13. CRISP-DM Workflow

    14. Project Overview

    15. Phase 2) Data Understanding

    16. Initial Inspection

    17. Duplicates

    18. Missing Values

    19. Handling Null Values

    20. Consistency

    21. Combine or Separate Features

    22. Data Cleaning (Core)

    23. Project 1 - Part 2 (Core)

    24. Additional Pandas Resources

    25. Pandas Interview Questions (Optional)

    1. Phase 2.4: Explore Each Feature

    2. Why Visualize Your Data?

    3. Creating Matplotlib Plots

    4. Univariate Visualizations

    5. Recreate Interest vs Principal Graph (Practice)

    6. Histograms

    7. Boxplots

    8. Histograms & Boxplots Exercise (Practice)

    9. Univariate Categorical Plots

    10. Univariate Plots (core)

    11. Overview - Multivariate Visualizations

    12. Bar Plots

    13. Correlation and Heat Maps

    14. Regression Plots

    15. Multivariate Categorical Plots

    16. Multivariate Plots Practice:

    17. Data Visualization (Core)

    18. Customizing Plots

    19. Making a README

    20. Project 1 - Part 3 (Core)

    21. Matplotlib Styles

    22. Saving Plots to Files

    23. Data Visualization Interview Questions

    1. Welcome to Belt Exam Prep!

    2. Data Science Fundamentals - Belt Exam Practice

    1. Multiple Subplots

    2. Combining Plots

    3. FacetGrids

    4. Annotating Distribution Plots

    5. Recreate Graph 2 (Practice)

    6. CRISP-DM Phase 3 Overview

    7. Preparing Ames for Machine Learning

    8. Intro to Modeling

    9. Types of Features

    10. EDA Functions Part 1

    11. EDA Functions Part 2

    12. EDA Functions Part 3

    13. Inspecting Features

    14. Feature Inspection (Practice)

    15. Colab Code Snippets

    16. Project 1 - Part 4 (Core)

    17. Data Viz Resources (Optional)

    18. Folium (Optional)

    19. Interactive Visualizations (Optional)

    20. Interview Questions:

    21. Installing Jupyter

    1. Exam Policies

    2. Exam grading system

    3. How to submit the exam?

    4. Belt Exam

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