The final modules introduce Machine Learning with Scikit-learn, where students build and evaluate their first predictive models. The course concludes with a full Capstone Project — an end-to-end data analysis of a real-world dataset that students clean, explore, visualize, and present with insights and findings. Every graduate leaves with Python skills, data analysis experience, and a portfolio-ready capstone project that demonstrates their abilities to future schools, scholarships, and employers.

Learning Objectives

By the end of this course, students will be able to:

• Set up a professional Python data science environment using VS Code and Jupyter Notebook

• Write Python programs using variables, data types, loops, functions, and file handling

• Use NumPy to perform fast, efficient numerical computations on arrays and datasets

• Load, clean, explore, and manipulate real-world datasets using Pandas

• Perform advanced data wrangling including merging, grouping, pivot tables, and time-series handling

• Create compelling data visualizations using Matplotlib and Seaborn to communicate insights clearly

• Conduct full Exploratory Data Analysis (EDA) on real, messy datasets from start to finish

• Detect and handle outliers, missing values, and data quality issues like a professional data analyst

• Build and evaluate basic machine learning models using Scikit-learn

• Complete an end-to-end data science capstone project and present findings with data-backed insights