Introduction to Python for Biomedical Data Analysis

Lesson 1 • Core Python Syntax and Data Types

Covers variables, numeric types, strings, and Booleans with biomedical naming conventions. Provides the vocabulary for all subsequent data manipulation tasks.

Lesson 2 • Loops and Iteration

Introduces for and while loops for processing patient record collections. Directly supports batch processing patterns used in data pipeline chapters.

Lesson 3 • Setting Up the Python Environment

Install Anaconda, configure Jupyter Notebook, and verify package availability. Establishes the reproducible workspace used throughout the entire course.

Lesson 4 • Control Flow and Logic

Teaches if/elif/else branching and comparison operators using clinical threshold examples. Enables conditional data filtering applied in later analysis chapters.

Lesson 1 • Sets and Membership Testing

Introduces sets for deduplication and fast membership checks on cohort identifiers. Supports data cleaning steps covered in the pandas chapter.

Lesson 2 • Lists and Tuples in Clinical Context

Covers list creation, indexing, slicing, and mutation alongside immutable tuples. Connects to storing ordered measurement series and fixed metadata fields.

Lesson 3 • File Input and Output Basics

Reads and writes plain text and CSV files using built-in Python tools. Bridges raw data files to the structured loading methods introduced with pandas.

Lesson 1 • Selecting and Filtering Data

Teaches loc, iloc, boolean indexing, and query for subsetting rows and columns. Enables cohort extraction and variable selection for downstream analysis.

Lesson 2 • Introduction to DataFrames and Series

Explains the DataFrame and Series objects, their axes, and dtypes. Establishes the core data model used in every subsequent pandas operation.

Lesson 3 • Loading Clinical Data from Files

Covers read_csv, read_excel, and read_json with delimiter and encoding options. Handles common file formats encountered in electronic health record exports.

Lesson 4 • Exploratory Data Inspection

Uses head, info, describe, and value_counts to profile dataset structure and distributions. Identifies missing values and outliers before any transformation step.

Lesson 1 • String Cleaning and Standardization

Uses str accessor methods to normalize diagnosis codes, drug names, and free-text fields. Ensures consistent category labels before grouping and aggregation.

Lesson 2 • Feature Engineering Basics

Creates derived variables such as BMI, age groups, and elapsed time from raw columns. Produces clinically meaningful features for modeling and stratified analysis.

Lesson 3 • Handling Missing Data

Covers dropna, fillna, and interpolation strategies appropriate for clinical measurements. Discusses the impact of each strategy on statistical validity.

Lesson 4 • Outlier Detection and Treatment

Applies IQR fencing and z-score thresholds to flag physiologically implausible values. Connects outlier decisions to domain knowledge and analysis sensitivity.

Lesson 1 • GroupBy Operations for Cohort Analysis

Applies groupby with agg, transform, and apply to compute group statistics. Enables per-diagnosis, per-site, and per-timepoint summaries of clinical outcomes.

Lesson 2 • Reshaping Data: Melt and Stack

Transforms wide-format lab panels to long format using melt, stack, and unstack. Prepares data for longitudinal analysis and tidy-data visualization workflows.

Lesson 3 • Pivot Tables and Cross-Tabulations

Builds pivot_table and crosstab summaries for contingency and frequency analysis. Supports reporting formats required in clinical trial result tables.

Lesson 1 • Statistical Plots with Seaborn

Uses seaborn for distribution, categorical, and relational plots with built-in statistics. Accelerates exploratory visualization of clinical variable relationships.

Lesson 2 • Matplotlib Foundations

Introduces the Figure and Axes model, basic plot types, and formatting controls. Provides the rendering layer underlying all visualization libraries used later.

Lesson 3 • Customizing Plots for Publication

Applies themes, color palettes, font sizes, and layout adjustments for journal standards. Ensures figures meet accessibility and reproducibility requirements.

Lesson 4 • Visualizing Longitudinal Patient Data

Plots time-series biomarker trajectories and event timelines for individual patients. Connects temporal visualization to the data reshaping skills from the previous chapter.

Lesson 1 • Correlation and Multiple Testing Correction

Computes Pearson and Spearman correlations and applies FDR correction for multi-biomarker panels. Addresses inflated false-positive rates in high-dimensional biomedical studies.

Lesson 2 • Comparing Groups with Parametric Tests

Runs t-tests and ANOVA using SciPy to compare means across patient groups. Includes assumption checking for normality and homogeneity of variance.

Lesson 3 • Hypothesis Testing Fundamentals

Covers p-values, significance levels, effect sizes, and power for biomedical study design. Prevents common misinterpretations of statistical significance in clinical contexts.

Lesson 4 • Descriptive Statistics and Distributions

Computes mean, median, variance, skewness, and kurtosis for clinical measurement distributions. Establishes the baseline characterization required before any inferential test.

Lesson 1 • Model Interpretability and Feature Importance

Extracts feature importances, SHAP values, and partial dependence plots to explain model decisions. Supports regulatory and clinical trust requirements for predictive tools.

Lesson 2 • Model Evaluation and Performance Metrics

Applies accuracy, precision, recall, F1, ROC-AUC, and confusion matrices to assess classifiers. Selects metrics aligned with clinical cost asymmetry between false positives and negatives.

Lesson 3 • Regression Models for Continuous Biomarkers

Fits linear, ridge, and lasso regression to predict continuous lab values and dosage responses. Evaluates models with RMSE, MAE, and R-squared on held-out test sets.

Lesson 4 • Machine Learning Concepts and Workflow

Defines supervised learning, train-test splits, and the bias-variance tradeoff in clinical contexts. Establishes the end-to-end modeling pipeline applied in every subsequent section.