Python Data Science Foundations

This unit establishes foundational skills for Python-based data science workflows, including language basics, development environments (Anaconda, Colab, Jupyter), and notebook-driven prototyping. Students learn to acquire, manipulate, explore, visualize, and perform introductory modeling on structured and unstructured data while addressing basic data privacy considerations.

Learning Objectives

• Demonstrate the ability to install, configure, and use Python development environments and notebook platforms (Anaconda, Google Colab, Jupyter) to build reproducible data science workflows
• Apply core Python programming and library skills (NumPy, pandas, scikit-learn) to load, sample, clean, and transform structured flat-file and multimedia data for analysis
• Analyze datasets through exploratory data analysis and visualization by selecting and creating appropriate graphs and summary statistics to communicate patterns and support problem contextualization
• Demonstrate introductory modeling and preprocessing techniques — including tokenization (bag-of-words), SVD, k-means clustering, linear regression, and decision trees — and evaluate basic privacy practices such as identifying and removing personally identifiable information (PII)