Introduction to Data Science and scikit-learn in Python

This module introduces learners to Python programming for hypothesis testing. It covers the basics of Python and Jupyter Notebook, including lists, dictionaries, loops, functions, and libraries. Participants will gain hands-on experience through coding assignments and quizzes, setting the stage for the subsequent modules.

Learners will deep dive into Numpy, Pandas, and scikit-learn in this module. They will explore the manipulation and joining of dataframes, data reshaping, and combining data. The module also includes a case study on finding outliers, providing practical insights into data analysis and manipulation.

This module revisits scikit-learn for machine learning in hypothesis testing. Participants will learn to apply linear regression, explore train/test splits and cross-validation, and gain insights into the math behind machine learning. The module also delves into loading and analyzing datasets, providing practical skills for applying machine learning models.

The final module focuses on using classification to predict the presence of heart disease. Learners will engage in predicting heart disease presence, strengthening their understanding of applying classification models to real-world health data.