Data Science · DATA

About Data Science

Turning data into decisions — statistics, computing, and communication. Courses carry the DATA subject code and are offered through the College of Computing & Information Sciences. Numbers below 300 are introductory, 300–499 advanced undergraduate, and 500 and above graduate. Prerequisites are enforced at registration.

How the sequence works

Of the 9 DATA courses listed, 3 are introductory (numbers under 300), 2 are advanced undergraduate (300–499). 1 course has no prerequisite and is a reasonable first step. Later courses assume the earlier ones, so read prerequisites before you plan a term — they are enforced at registration and an override from the Data Science department is the only way around them.

DATA 1400 — Introduction to Data Science 3 cr

The data life cycle from question to communication: acquiring data, cleaning it, exploring it visually, and being honest about what it can and cannot support.

Prerequisite: none.

DATA 2200 — Data Wrangling and Visualization 3 cr

Reshaping messy real-world data, joining sources, handling missing values, and building clear visualizations that answer a specific question.

Prerequisite: DATA 1400, CS 1100.

DATA 2600 — Statistical Inference 4 cr

Sampling, estimation, hypothesis tests, confidence intervals, regression, and the assumptions that make each one valid — with simulation to build intuition.

Prerequisite: MATH 1600.

DATA 3100 — Machine Learning for Data Science 3 cr

Predictive modeling in practice: feature engineering, model selection, cross-validation, regularization, and evaluating a model against a real baseline.

Prerequisite: DATA 2600, CS 2400.

DATA 3300 — Databases and Data Engineering 3 cr

Relational and non-relational stores, SQL at depth, pipelines, batch and streaming processing, and moving data reliably between systems.

Prerequisite: CS 1200.

DATA 3500 — Experiments and Causal Inference 3 cr

Designing experiments, running A/B tests, and the methods for estimating a causal effect from observational data — plus the traps in each.

Prerequisite: DATA 2600.

DATA 3700 — Data Ethics and Governance 3 cr

Bias and fairness in data and models, privacy, consent, transparency, and the regulatory landscape, worked through case studies.

Prerequisite: junior standing.

DATA 4200 — Data Science Practicum 3 cr

A team takes a sponsor's real problem and real data from framing to a delivered, documented analysis or model.

Prerequisite: DATA 3100.

DATA 4900 — Data Science Capstone 3 cr

An individual project: a substantial analysis or model on a question of the student's choosing, presented at the showcase with a written report.

Prerequisite: senior standing.

Planning and advising

Your degree audit shows exactly how DATA courses apply to your requirements; this list only shows what is offered. If you are choosing between two courses, deciding when to take a heavy one, or wondering whether a course will run next term, your academic adviser or the Data Science program office can tell you. Course availability by term is set close to registration and is not guaranteed by anything on this page.