In this course, you will learn the rich set of tools, libraries, and packages that comprise the highly popular and practical Python data analysis ecosystem. This course is primarily taught via screen-sharing programming videos. Topics taught range from basic Python syntax all the way to more advanced topics like supervised and unsupervised machine learning techniques.
- Python Basics (variables, strings, simple math, conditional logic, for loops, lists, tuples, dictionaries, etc.)
- Using the Pandas library to manipulate data (filtering and sorting data, combining files, GroupBy, etc.)
- Plotting data in Python using Matplotlib and Seaborn
- Logistic Regression using Scikit-Learn
- Classification and Regression Metrics
- Decision Trees using Scikit-Learn
- Random Forests (Scikit-Learn)
- Clustering Algorithms (K-Means, Hierarchical Clustering)
PrerequisitesIntroduction to Programming or demonstration of equivalency.
You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Software: Anaconda, Visual Studio Code, Jupyter Notebook.
Hardware: Computer with Windows or Mac OS preferred.
Applies Towards the Following Certificates
- Python Programming : Elective Courses