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LinkedIn Learning

Data Science Foundations: Python Scientific Stack

via LinkedIn Learning


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


  • 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
  • Next steps

Taught by

Miki Tebeka


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