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