01. Hello, Python [Github] [Kaggle]
A quick introduction to Python syntax, variable assignment, and numbers

02. Exercise: Syntax, Variables, and Numbers [Github] [Kaggle]

03. Functions and Getting Help [Github] [Kaggle]
Calling functions and defining our own, and using Python's builtin documentation
04. Exercise: Functions and Getting Help [Github] [Kaggle]

05. Booleans and Conditionals [Github] [Kaggle]
Using booleans for branching logic
06. Exercise: Booleans and Conditionals [Github] [Kaggle]

07. Lists [Github] [Kaggle]
Lists and the things you can do with them. Includes indexing, slicing and mutating
08. Exercise: Lists [Github] [Kaggle]

09. Loops and List Comprehensions [Github] [Kaggle]
For and while loops, and a much-loved Python feature: list comprehensions
10. Exercise: Loops and List Comprehensions [Github] [Kaggle]

11. Strings and Dictionaries [Github] [Kaggle]
Working with strings and dictionaries, two fundamental Python data types
12. Exercise: Strings and Dictionaries [Github] [Kaggle]

13. Working with External Libraries [Github] [Kaggle]
Imports, operator overloading, and survival tips for venturing into the world of external libraries
14. Exercise: Working with External Libraries [Github] [Kaggle]

LEVEL 1

01. How Models Work [Github] [Kaggle] [HTML]
The first step if you're new to machine learning

02. Explore Your Data [Github] [Kaggle] [HTML]
Load data and set up your environment for your hands-on project

03. Exercise: Explore Your Data [Github] [Kaggle] [HTML]

04. Your First Machine Learning Model [Github] [Kaggle] [HTML]
Building your first model. Hurray!
05. Exercise: Your First Machine Learning Model [Github] [Kaggle] [HTML]

06. Model Validation [Github] [Kaggle] [HTML]
Measure the performance of your model ? so you can test and compare alternatives
07. Exercise: Model Validation [Github] [Kaggle] [HTML]

08. Underfitting and Overfitting [Github] [Kaggle] [HTML]
Fine-tune your model for better performance.
09. Exercise: Underfitting and Overfitting [Github] [Kaggle] [HTML]

10. Random Forests [Github] [Kaggle] [HTML]
Using a more sophisticated machine learning algorithm.
11. Exercise: Random Forests [Github] [Kaggle] [HTML]

12. Exercise: Machine Learning Competitions [Github] [Kaggle] [HTML]
Enter the world of machine learning competitions to keep improving and see your progress

LEVEL 2

1a. Handling Missing Values [Github] [Kaggle] [HTML]
Learn multiple approaches for dealing with missing data fields
1b. Exercise: Handling Missing Values [Github] [Kaggle] [HTML]

2a. Using Categorical Data with One Hot Encoding [Github] [Kaggle] [HTML]
Handle this important but challenging data type
2b. Exercise: Using Categorical Data with One Hot Encoding [Github] [Kaggle] [HTML]

3a. XGBoost [Github] [Kaggle] [HTML]
The most important technique for building high-performance models on conventional data (the type that fits in tables or data frames.)
3b. Exercise: XGBoost [Github] [Kaggle] [HTML]

4a. Partial Dependence Plots [Github] [Kaggle] [HTML]
Extract insights from your models. Insights many didn't even realize were possible.
4b. Exercise: Partial Dependence Plots [Github] [Kaggle] [HTML]

5a. Pipelines [Github] [Kaggle] [HTML]
Make your machine learning code cleaner and more professional
5b. Exercise: Pipelines [Github] [Kaggle] [HTML]

6a. Cross-Validation [Github] [Kaggle] [HTML]
Improve how you compare and choose models and data preprocessing
6b. Exercise: Cross-Validation [Github] [Kaggle] [HTML]

7a. Data Leakage [Github] [Kaggle] [HTML]
Identify and avoid one of the most common and costly mistakes in machine learning.
7b. Exercise: Data Leakage [Github] [Kaggle] [HTML]

1. Creating, reading, and writing workbook
You can't work with data if you can't read it. Get started here.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

2. Indexing, Selecting & Assigning
Pro data scientists do this dozens of times a day. You can, too!
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

3. Summary functions and maps workbook
Extract insights from your data.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

4. Grouping and Sorting
Scale up your level of insight. The more complex the dataset, the more this matters
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

5. Data types and missing data workbook
Deal with the most common progress-blocking problems
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

6. Renaming and combining workbook
Data comes in from many sources. Help it all make sense together
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

7. Method chaining workbook
Put it all together to write more professional Pandas code.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]
Reference [Github] [Kaggle] [HTML]

1. Welcome to data visualization
Overview of data visualization tools and course structure.
Workbook [Github] [Kaggle] [HTML]

2. Univariate plotting with pandas
Learn the basic (and most important) types of graphs
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

3. Bivariate plotting with pandas
Visually capture the patterns and correlations in any dataset.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

4. Styling your plots
Make your plots look beautiful ? a key task before sharing
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

5. Subplots
A key concept to make advanced graphics in Python.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

6. Plotting with seaborn
The faster way to create complex graphics [Seaborn Example Gallery]
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

7. Faceting with seaborn
Extend your graphical powers to capture more variables and patterns
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

8. Multivariate plotting
Plotting in high dimensional spaces.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

9. Introduction to plotly (Optional)
Go from static graphics to interactive data visualization experiences.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

10. Grammar of graphics with plotnine (Optional)
This Python implementation of R's ggplot2 library provides an incredible level of power for custom graphics.
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]

11. Time-series plotting (Optional)
Specialized plots for time-series data [pandas.Period Documentation]
Workbook [Github] [Kaggle] [HTML] Exercise [Github] [Kaggle] [HTML]