This is the repository for the LinkedIn Learning course Data Science Foundations: Python Scientific Stack. The full course is available from LinkedIn Learning.

Data science provides organizations with striking—and extremely valuable—insights into human behavior. While data mining can seem a bit daunting, you don't need to be a highly skilled programmer to process your own data. In this new hands-on course, instructor Miki Tebeka shows you how to use the Python scientific stack to complete common data science tasks.

Learn about the basic tools and core concepts to effectively process data with the Python scientific stack, including how to work with Visual Studio Code for documentation, NumPy for numeric computation, Pandas for data crunching, scikit-learn for data modeling, madplotlib for data visualization, and more. Upon completing this course, you’ll have the skills you need to load and analyze data, run models, solve complex problems, and display results for your team.

1. To use these exercise files, you must have the following installed:
   • Python 3.8 and up
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
3. [Course-specific instructions]
   • Create a virtual environment: python -m venv venv
   • Install dependencies: ./venv/bin/python -m pip install -r requirements.txt
   • Have your IDE use Python from the virtual environment (in VSCode: Python: Select Interpreter)

Miki Tebeka

Check out my other courses on LinkedIn Learning.