Data Science Foundations: Python Scientific Stack

Learn how to use the Python scientific stack to solve problems and complete common data science tasks.

Introduction

• The Python scientific stack
• What you should know
• Setting up

1. Visual Studio Code

• Working with VS Code
• Using code cells
• Extensions to Python language
• Understanding markdown cells

2. NumPy Basics

• NumPy overview
• NumPy arrays
• Slicing
• Boolean indexing
• Understanding broadcasting
• Understanding array operations
• Understanding ufuncs
• Challenge: Working with an image
• Solution: Working with an image

3. pandas

• pandas overview
• Loading CSV files
• Parsing time
• Accessing rows and columns
• Calculating speed
• Displaying speed box plot
• Challenge: Taxi data mean speed
• Solution: Taxi data mean speed

4. Installing Packages

• Introduction to Python packages
• Using environments
• Managing dependencies
• Challenge: Creating requirements
• Solution: Creating requirements

5. folium and Geo

• Creating an initial map
• Drawing a track on a map
• Using geo data with Shapely
• Challenge: Drawing the running track
• Solution: Drawing the running track

6. NYC Taxi Data

• Examining data
• Loading data from CSV files
• Working with categorical data
• Working with data: Hourly trip rides
• Working with data: Rides per hour
• Working with data: Weather data
• Challenge: Graphing taxi data
• Solution: Graphing taxi data

7. scikit-learn

• scikit-learn introduction
• Regression
• Understanding train/test split
• Preprocessing data
• Composing pipelines
• Saving and loading models
• Challenge: Hand-written digits
• Solution: Hand-written digits

8. Plotting

• Matplotlib overview
• Using styles
• Customizing pandas output
• pandas plotting
• Using Matplotlib with pandas
• Interactive plots
• Other plotting packages
• Challenge: Stocking data bar charts
• Solution: Stocking data bar charts

9. Other Packages

• Other packages overview
• Going faster with Numba
• Understanding deep learning
• Working with image processing
• Understand NLP
• Working with bigger data

10. Development Process

• Development process overview
• Understanding source control
• Code review
• Testing overview
• Testing example

Conclusion

• Next steps