Data Science Foundations: Python Scientific Stack

Learn about the Python scientific stack, with an emphasis on how to use it to solve problems.

Introduction

• The Python scientific stack
• What you should know
• Using GitHub Codespaces with this course
• Setup

1. Visual Studio Code

• Use code cells
• Extensions to the Python language
• Understand markdown cells

2. NumPy Basics

• NumPy overview
• NumPy arrays
• Slicing
• Learn boolean indexing
• Understand broadcasting
• Understand array operations
• Understand ufuncs
• Challenge: Working with an image
• Solution: Working with an image

3. pandas

• pandas overview
• Loading CSV files
• Parse time
• Access rows and columns
• Calculate distance
• Display speed box plot
• Challenge: Taxi data mean speed
• Solution: Taxi data mean speed

4. Folium and Geospatial Data

• Create an initial map
• Draw a track on map
• Using geospatial data with shapely
• Challenge: Draw the running track
• Solution: Draw the running track

5. NYC Taxi Data

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

6. scikit-learn

• scikit-learn introduction
• Linear regression
• Understand train/test split
• Preprocess data
• Compose pipelines
• Save and load models
• Challenge: Handwritten digits
• Solution: Handwritten digits

7. Plotting

• Overview of matplotlib
• Use styles
• Customize pandas output
• Plotting with pandas
• Use Matplotlib with pandas
• Tips and tricks
• Other plotting packages
• Challenge: Stock data bar charts
• Solution: Stock data bar charts

8. Other Packages

• Other packages overview
• Go faster with Numba
• Understand deep learning
• Work with image processing
• Understand NLP: NLTK
• Working with bigger data

9. Development Process

• Development process overview
• Understand source control
• Learn code review
• Testing overview
• Testing example

Conclusion

• Next steps