Data Science in Practice: Tools and Applications

ACMS 40876/60876 Spring 2026

Course Schedule

Unit	Date	Topic	Slides	Additional Resources	Labs
Course Overview	Jan 13	Intro to Data Science Life Cycle	Lecture 1		Lab 0 (optional)
Software/Tools	Jan 15	Tools: git, GitHub, Quarto	Lecture 2	Software Installation Git/GitHub Quarto
Beginning the Data Science Life Cycle	Jan 20 Jan 22 Jan 27 Jan 29 Feb 3	Problem Formulation + Data Collection Reproducible Workflows Data Preprocessing / Cleaning Data Preprocessing / Cleaning Exploratory Data Analysis	Lecture 3 Lecture 4 Lecture 5 Lecture 6 Lecture 7	renv and conda, .gitignore Lab 1: Data Cleaning ggplot2	Lab 1
Unsupervised Learning	Feb 5 Feb 10 Feb 12 Feb 17	Intro + Dimension Reduction Clustering Dimension Reduction in Practice Unsupervised Model Selection	Lecture 8 Lecture 9 Lecture 10 Lecture 11	Unsupervised Methods Linguistics: DR Linguistics: App	Lab 2
Supervised Learning	Feb 19 Feb 24 Feb 26 Mar 3 Mar 5	Supervised Methods Overview Evaluation + Data Splitting Supervised Learning in Practice Supervised Learning in Practice Supervised Learning in Practice	Lecture 12 Lecture 13 Lecture 14 Lecture 15 Lecture 16	Supervised Methods	Lab 3
Software/Tools	Mar 17 Mar 19 Mar 24 Mar 26	Intro to Parallelization Writing Your Own Software Package Parallelization on the CRC Parallelization on the CRC	Lecture 17 Lecture 18 Lecture 19 Lecture 20	Parallelization, CRC R Package, Python Package	Lab 4
Interpretable Machine Learning	Mar 31 Apr 2	Intro to Interpretable ML Interpretable ML in Practice	Lecture 21 Lecture 22	SHAP, Correlated Variables
Software/Tools	Apr 7	GitHub Flow	Lecture 23	Merge Conflicts GitHub Flow
Large Language Models	Apr 9 Apr 14 Apr 16 Apr 21	Intro to LLMs Fine-Tuning LLMs in Practice LLMs in Practice	Lecture 24 Lecture 25 Lecture 26 Lecture 27	Text Embeddings Fine-Tuning RLHF
Final Project Presentations	Apr 23 Apr 28	Final Project Presentations Final Project Presentations