A compilation of code I wrote for my advanced programming class, taken Fall 2025 at ASU from Dr. Chris Plaisier.

Course learning outcomes

Students will be able to:

• Develop and run Python code in multiple ways
• Demonstrate proficiency in programming with Python
• Design and compose data structures
• Demonstrate the ability to load and write text files from Python
• Be able to use APIs for Python packages effectively
• Use Pandas to subset, merge, capture, modify data, and deal with missing data
• Make plots describing data and analyses of data
• Apply hypothesis testing statistics to real-world datasets
• Apply both clustering and classification machine learning
• Segment cells and nuclei from microscopy slides

Module Topics and Skills

• Module 1: Why Use Python, Python Style Guide(PEP8), and IDEs
• Module 2: Intro to Python (no assignments posted to GitHub)
• Module 3: File Types, File Compression, Modules, Packages, and JSON files
• Module 4: Pandas and DataFrames
• Module 5: Plotting with Matplotlib and Scipy.stats
• Module 6: Plotting with Seaborn and Hypothesis Testing using T-tests
• Module 7: Correlation, T-tests, and Effect size
• Module 8: GEOparse to access data from the Gene Expression Omnibus
• Module 9: Differential Gene Expression and Multiple Hypothesis Correction
• Module 10: Linear Regression
• Module 11: Clustering
• Module 12: Classification using Sklearn (Nearest Neighbor and Random Forest) and Deep Learning
• Module 13: Markov Chains and Hidden Markov Models