00 Student Onboarding
Get started with JupyterLite, notebook editing, Markdown, LaTeX, Python data analysis, PDF export, backups, and homework submission workflow.
Open the browser labISYE 4031 Summer 2026 Python Labs
These browser-based labs let you run the course Python notebooks without installing Python, Anaconda, or Jupyter on your computer.
01 Regression Foundations
Work through forecasting context, statistical review, simple linear regression, intervals, residual diagnostics, and transfer practice.
Open the browser lab02 Multiple Regression
Work through multiple regression, matrix least squares, coefficient inference, prediction intervals, polynomial terms, interactions, categorical predictors, and homework notebooks.
Open the browser lab03 Diagnostics and Variable Selection
Work through regression pitfalls, residual diagnostics, multicollinearity, hidden extrapolation, unusual observations, screening methods, and homework notebooks.
Open the browser lab04 Time Series Foundations
Work through time-series components, trend regression, seasonal dummy variables, transformations, autocorrelation diagnostics, adjusted forecasting, and homework notebooks.
Open the browser lab05 Smoothing and Forecasting
Work through exponential smoothing, forecast accuracy measures, Holt's trend-corrected method, Holt-Winters seasonality, prediction intervals, and an integrated homework notebook.
Open the browser lab06 Box-Jenkins ARIMA
Work through stationarity, differencing, ACF/PACF model identification, nonseasonal ARIMA, residual diagnostics, seasonal SARIMA models, and an integrated homework notebook.
Open the browser lab07 Difference-in-Differences
Work through two-period DiD, interaction regressions, continuous exposure, panel fixed effects, parallel trends, event studies, inference, robustness, and applied DiD reporting.
Open the browser lab10 Data Preparation and Feature Engineering
Audit data quality, validate joins, reshape and aggregate records, and create a reproducible modeling table without target leakage.
Open the browser lab11 Model Selection and Evaluation
Separate training, validation, and final testing; use cross-validation and appropriate metrics; and evaluate ordered data with time-respecting holdouts.
Open the browser lab12 Logistic Regression and Classification
Interpret log-odds and odds ratios, estimate classification probabilities, choose decision thresholds, and evaluate binary classifiers without test-set leakage.
Open the browser lab13 Shrinkage Methods: Ridge and LASSO
Use standardized, leakage-safe workflows to understand multicollinearity, tune ridge and LASSO penalties, and compare regularized models with ordinary least squares.
Open the browser lab14 PCA, KNN, and K-means
Explore scaling and distance, principal components, tuned KNN classification, and K-means segmentation with suitable baselines and validation measures.
Open the browser lab15 Project Methods Clinic
Match project questions to response types, data structures, methods, evaluation strategies, and defensible predictive, descriptive, exploratory, or causal claims.
Open the browser lab