Foundations of Python Data Science

This unit establishes hands-on foundations for using Python and notebook environments to ingest, explore, visualize, and preliminarily model data. Students set up development environments, work with NumPy and pandas, perform exploratory data analysis and basic machine learning workflows, and apply ethical practices for data handling and prototyping.

Learning Objectives

• Demonstrate proficiency in Python development environments by installing and using Anaconda, Google Colab, and Jupyter Notebook to create, run, and document reproducible notebook workflows
• Apply NumPy and pandas methods to ingest, upload, stream, sample, clean, and transform structured flat-file and multimedia data for analysis
• Analyze datasets through exploratory data analysis and visualization—selecting appropriate graphs, computing summary statistics, and using SVD—to prepare data for modeling with scikit-learn (clustering, outlier detection, regression, and decision trees)
• Evaluate ethical and privacy considerations by identifying and removing personally identifiable information and conducting rapid prototyping that documents assumptions, data lineage, and reproducibility