STAT 418: Computational Methods in Data Science
Spring 2026 Course Schedule — Tuesday/Thursday 1:30–2:45 PM
CH 1
Chapter (click to open)
📘 HW
Homework
🎯 CAP
Capstone Milestone
📝 EXAM
Midterm
🎤 PRES
Presentations
| Week | Date | Tuesday | Thursday | Due |
|---|---|---|---|---|
| 1 | 01/12 | 📚 Course IntroductionCh 1Foundations of Probability & Inference,Interpretations of ProbabilityStatistical Inference Paradigms |
Ch 1Python EchoSystem, Distribution Review, and Relationships |
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| 2 | 01/19 | Ch 2Monte Carlo Fundamentals & Uniform Random Variates |
Ch 2Inverse CDF + Transformation methods for Normal Distribution |
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| 3 | 01/26 | Ch 2Rejection Sampling |
Ch 2Variance Reduction Methods |
📘 Homework 1⏰ Due 1/30 |
| 4 | 02/02 | Ch 3Exponential Familes and Maximum Likelihood Theory |
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| 5 | 02/09 | Ch 3Least Squares Estimation; Assumptions; and Diagnostics |
Ch 3Least Squares Estimation; Assumptions; and DiagnosticsStart Continued Generalized Linear Models (GLMs) |
📘 Homework 2⏰ Due 2/13 |
| 6 | 02/16 | Ch 3Continued Generalized Linear Models (GLMs) |
Ch 4Sampling Distribution Proble; ECDF and Plug In Principle |
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| 7 | 02/23 | Ch 4Nonparametric Bootstrap |
📘 Homework 3⏰ Due 2/27 |
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| 8 | 03/02 |
📝 MIDTERM I
In Class
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Ch 4Parametric Bootstrap |
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| 9 | 03/09 | Ch 4Jackknife Fundamentals (Leave-one-out resampling) |
Ch 4Bootstrap Confidence Intervals; Bias Correction |
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| 10 | 03/16 |
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| 11 | 03/23 | Ch 4Cross Validation for Model Selection; Predictive Performance Estmation |
Ch 5Bayesian foundations; Prior specification |
📘 Homework 4⏰ Due 3/27 |
| 12 | 03/30 | Ch 5Conjugate priors; Analytical posteriors |
Ch 5Credible intervals |
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| 13 | 04/06 | Ch 5Markov chain foundations |
Ch 5MCMC, Metropolis-Hastings; Gibbs sampling |
📘 Homework 5⏰ Due 4/10 |
| 14 | 04/13 | Ch 5Convergence diagnostics; Model comparison |
Ch 5Posterior predictive checks; Hierarchical models |
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| 15 | 04/20 | Ch 6Integration and workflows; Text preprocessing; Feature extraction with embeddings |
📝 MIDTERM II
In Class
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📘 Homework 6⏰ Due 4/24 |
| 16 (Queit Week) | 04/27 | Ch 6Pre-trained models for analysis and annotation; RAG systems |
Ch 6Responsible AI; Privacy; Prompt engineering |
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| 17 (Finals Week) | 05/04 | 🎤 CAPSTONE PRESENTATIONS Date TBA | ||