Python for Data Science and Machine Learning

Python Tools for Data Problems

Data scientists solve problems with code and statistics. This intermediate course arms you with Python's most practical libraries—Pandas for data wrangling, NumPy for computation, matplotlib for visualization, scikit-learn for modeling. You'll move beyond theory to hands-on data challenges: building regression models (linear, multilinear, polynomial), then advancing to decision trees and random forests. Each module solves a real problem with real data, so you graduate not just knowing the tools but confident applying them. The course also addresses model evaluation and data bias—practical concerns that separate working data scientists from theorists.