University of Washington

Data Science Courses at UW

  1. Learning Data Science /
  2. Data Science at UW /
  3. Data Science Courses at UW

We have identified a core set of skills that form the heart of data science education, but also recognize that data science education needs vary across disciplines. Our vision has led us to adopt an approach to data science education that takes the form of specializations within existing majors. These specializations are called “options” and are transcriptable in the sense that the “data science” specialization is listed on student transcripts.

This page will be updated once per year. Please email eScience if you’d like to make any additions or edits to the course list.

General Introduction to Data Science

  • STAT 180/CSE 180/INFO 180 – Introduction to Data Science;  Prerequisite: Either a minimum grade of 2.5 in MATH 098, a minimum grade of 3.0 in MATH 103, a score of 151-169 on the MPT-GS placement test, or score of 145-153 on the MPT-AS placement test
  • INFO 370 – Core Methods in Data Science (5 credits); Prerequisite: INFO 201; and CSE 142 or CSE 143; and either CS&SS 221, SOC 221, STAT 221, STAT 311, MATH 390, STAT 390, QMETH 201, or Q SCI 381
  • IMT 572 – Introduction to Data Science; no prerequisites
  • Coursera: Introduction to Data Science
  • BIOST 544 –Introduction to Biomedical Data Science; Prerequisite: either BIOST 511 or equivalent; either BIOST 509 or equivalent; or permission of instructor
  • BIOL 419/519 – Data Science for Biologists (4 credits); no prerequisites
  • PHYS 542 – Numerical Methods in Applications of Physics; graduate status or permission of instructor
  • FISH 549 – Best Practices in Environmental Data Science; no prerequisites although students should have a working knowledge of the R computing software, such as that provided in FISH 552/553

Software Development for Data Science

  • CSE 160 – Data Programming (4 credits); no prerequisites
  • CSE 163 – Intermediate Data Programming (4 credits); Prerequisite: CSE 142 or CSE 160
  • CSE 583 – Software Development for Data Scientists (4 credits); no prerequisites
  • ChemE 546 – Software Engineering for Molecular Data Scientists (3 credits); no prerequisites
  • CEWA 599 – Geospatial Data Analysis (3 credits); no prerequisites
  • CS&SS 508 – Introduction to R for Social Scientists (1); no prerequisites
  • FISH 552  – Introduction to R Programming for Natural Scientists (2); no prerequisites
  • GEOG 485 – Advanced Digital Geographies (5): Prerequisite: GEOG 360

Statistics and Machine Learning

  • IND E 316 – Design of Experiments and Regression Analysis (4): Prerequisite: IND E 315. Offered: jointly with STAT 316; W.
  • IND E 321 – Statistical Quality Control (4) – Prerequisite: IND E 315 Offered: W.
  • CSE 416/STAT 416 –Introduction to Machine Learning (4 credits); Prerequisites: (CSE 143 or CSE 160) and (STAT 311 or STAT 390)
  • CSE 546 –Machine Learning (4 credits); Prerequisite: either CSE 312, STAT 341, STAT 391 or equivalent
  • EE 546 –Optimization and Learning for Control (3 credits); Prerequisite: permission of instructor
  • CET 521/IND E 546 – Inferential Data Analysis for Engineers (3 credits); Prerequisite: either IND E 315, STAT 390, or equivalent
  • CEE 465 – Data Analysis in Water Sciences; Prerequisite: IND E 315; either AMATH 301 or CSE 142
  • STAT 303 – Introduction to the Ethics of Algorithmic Decision Making (3) DIV; No prerequisites.
  • STAT 391 – Quantitative Introductory Statistics for Data Science (4); Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395
  • STAT 403 – Introduction to Resampling Inference (4); Prerequisite: either STAT 311/ECON 311, STAT 341, STAT 390/MATH 390, STAT 481/ECON 481, or Q SCI 381 and Q SCI 482
  • STAT 425 – Introduction to Nonparametric Statistics (3); Prerequisite: STAT 390/MATH 390
  • STAT 435 –Introduction to Statistical Machine Learning (4 credits); Prerequisites: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 308
  • STAT 504 – Applied Linear Regression (3); Prerequisite: either STAT 342, STAT 390/MATH 390, STAT 421, STAT 481/ECON 481, STAT 509/CS&SS 509/ECON 580, or SOC 425
  • STAT 509 –Introduction to Mathematical Statistics: Econometrics I (5 credits); Prerequisite: STAT 311/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309
  • STAT 512/513 – Statistical Inference (4 credits each); Prerequisite: STAT 395 and STAT 421, STAT 423, STAT 504, or BIOST 512
  • STAT 534 – Statistical computing (3); Prerequisite: experience with programming in a high level language
  • STAT 535 –Statistical Learning: Modeling, Prediction, and Computing (3 credits); Prerequisite: experience with programming in a high level language
  • STAT 538 – Advanced Machine Learning (3) STAT 548 Machine Learning for Big Data (4); Prerequisite: experience with programming in a high level language
  • STAT 556 – Introduction to Statistics and Probability (5) – Fee-based; no prerequisites
  • STAT 557 – Applied Statistics and Experimental Design (5) – Fee-based; Prerequisite: STAT/BIOST/DATA 556 or instructor’s permission
  • STAT 558 – Statistical Machine Learning for Data Scientists (5) – Fee-based; Prerequisite: STAT/BIOST/DATA 557 or instructor’s permission
  • STAT 570 – Advanced Regression Methods for Independent Data (3);  Prerequisite: STAT 512 and STAT 513;BIOST/STAT 533 or STAT 421/STAT 502 and STAT 423/STAT 504; a course in matrix algebra
  • STAT 571 – Advanced Regression Methods for Dependent Data (3);  Prerequisite: BIOST570/STAT 570
  • STAT 581 – Advanced Theory of Statistical Inference (3);  Prerequisite: STAT 513; either MATH 426 or MATH 576
  • STAT 582 – Advanced Theory of Statistical Inference (3); Prerequisite: STAT 581
  • STAT 583 – Advanced Theory of Statistical Inference (3);  Prerequisite: STAT 582
  • INFO 371 – Advanced Method in Data Science (5 credits); Prerequisite: INFO 370
  • IMT 573 – Data Science I: Theoretical Foundations; Prerequisites: either Q METH 201, IMT 570, or equivalent college coursework; either CSE 142, IMT 501, or equivalent college coursework
  • IMT 574 – Data Science II: Machine Learning and Econometrics; Prerequisite: IMT 573
  • ME 599 – Machine Learning Control
  • ATMS 552 – Objective Analysis; no prerequisites
  • AMATH 482/582 – Computational Methods for Data Analysis; Prerequisite: either MATLAB and linear algebra or permission of instructor
  • AST 497 – Introduction to AstroStatistics and Big Data in Astronomy (3 credits)
  • AST 598 – AstroStatistics and Machine Learning in Astronomy (3 credits)
  • CS&SS 510 – Maximum Likelihood Methods for the Social Sciences (5); Prerequisite: POL S 501/CS&SS 501; POL S 503/CS&SS 503
  • CS&SS 554 – Statistical Methods for Spatial Data (3); no prerequisites
  • CS&SS 564 – Bayesian Statistics for the Social Sciences (4); Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent
  • CS&SS 566 – Causal Modeling (4); Prerequisite: course in statistics, SOC 504, SOC 505, SOC 506, or equivalent
  • CS&SS 567 – Statistical Analysis of Social Networks (4); Prerequisite: SOC 504, SOC 505, SOC 506, or equivalent
  • TCSS 555 – Machine Learning (5) at the Tacoma campus; no prerequisites
  • TCSS 554 – Information Retrieval and Web Search (5) at the Tacoma campus; no prerequisites
  • TCSS 551 – Big Data Analytics (5) at the Tacoma campus; Prerequisite: minimum grade of 3.0 in TCSS 343 and TCSS 445 or equivalent
  • AMATH 515 – Fundamentals of Optimization (5); Prerequisite: linear algebra and advanced calculus
  • AMATH 563 – Inferring Structure of Complex Systems (5); Prerequisite: AMATH 561 and AMATH 562, or instructor permission
  • CFRM 521 – Machine Learning for Finance (4); Prerequisite: CFRM 502 or equivalent, which may be taken concurrently; programming skills in R or MATLAB
  • SEFS 540 – Optimization Techniques for Natural Resources (5); Prerequisite: MATH 308 or permission of instructor
  • FISH 556 – Spatio-temporal Models for Ecologists (5); Prerequisite: FISH 552 and FISH 553; and either FISH 454, FISH 458, ESRM 451/Q SCI 451, FISH 558, FISH 559, SEFS 590, STAT 516 and STAT 517, or permission of instructor; recommended: Knowledge of the R programming language Knowledge of likelihood-based statistics Intermediate background in statistical analysis
  • FISH/SEFS 557 – Demographic Estimation and Modeling (4); no prerequisites
  • FISH 558 – Decision Analysis in Natural Resource Management (4); no prerequisites
  • FISH 559 – Numerical Computing for the Natural Resources (5); no prerequisites
  • FISH 560 – Applied Multivariate Statistics for Ecologists (4); Prerequisite: Q SCI 482 or equivalent
  • BIOST 546 – Machine Learning for Biomedical and Public Health Big Data; Prerequisite: BIOST 511 or BIOST 512 and familiarity with R
  • STAT/BIOST 527 – Nonparametric Regression and Classification; Prerequisite: either STAT 502 and STAT 504 or BIOST 514 and BIOST 515
  • BIOST 533 –Theory of Linear Models; Prerequisite: STAT 421 or STAT 423 or BIOST 515; and STAT 513
  • BIOST 540 – Longitudinal and Multilevel Data Analysis; Prerequisite: either BIOST 513, BIOST 515, BIOST 518, BIOST 536, or permission of instructor
  • QSCI 482 – Statistical Inference in Applied Research I: Hypothesis Testing and Estimation for Ecologists and Resource Managers (5); Prerequisite: either STAT 311 or Q SCI 381
  • QSCI 483 – Statistical Inference in Applied Research II: Regression Analysis for Ecologists and Resource Managers (5); Prerequisite: QSCI 482
  • QERM 514 – Analysis of Ecological and Environmental Data I (4); no prerequisite but recommended QSCI 482 or similar

Data Management

  • CSE 414 – Introduction to Database Systems (4 credits) Prerequisites: CSE 143 (will soon also allow CSE 163)
  • CSE 544 – Principles of Database Management Systems (4 credits); no prerequisites
  • IMT 575 – Data Science III: Scaling, Applications, and Responsibility; Prerequisite: IMT 574
  • CET 522 – Transportation Data Management & Visualization; No prerequisites
  • TCSS 564 – Database Systems Internals (5) at the Tacoma Campus; Prerequisite: TCSS 343; TCSS 445
  • GEOG 465 – GIS Database and Programming (5) Prerequisite: GEOG 360
  • GEOG 482 – GIS Data Management (5) Prerequisite: GEOG 360

Data Visualization

  • CSE 442 –Data Visualization (4 credits); Prerequisite: CSE 332
  • CSE 412 –Introduction to Data Visualization (4 credits); Prerequisites will be CSE 143 or CSE 163
  • CSE 512 –Data Visualization (4 credits); no prerequisites
  • CS&SS/ 569  – Visualizing Data (4 credits); Prerequisites: SOC 504, SOC 505 & SOC 506
  • ENVH 465/565 – GIS in Public Health (3 credits); no prerequisites.
  • FISH 554 – Beautiful Graphics in R (2 credits) Prerequisites: FISH 552 & FISH 553 or equivalent R programming experience
  • GEOG 360 – GIS & Mapping (5); no prerequisites
  • HCDE 511 –Information Visualization/Data Visualization and Exploratory Analytics (4 credits); no prerequisites
  • HCDE 411 –Information Visualization (5 credits) Prerequisites: HCDE 308 and HCDE 310
  • IMT 562 –Interactive Information Visualization (4 credits); no prerequisites
  • INFO 474 –Interactive Information Visualization (5 credits); Prerequisites: INFO 343 or CSE 154; and CSE 143; and either Q METH 201, Q SCI 381, STAT 221/CS & SS 221/SOC 221, STAT 311, or STAT 390/MATH 390
  • SMEA 587 – Introduction to Spatial Data Manipulation and Visualization (3 credits); no prerequisites, although knowledge of R programming language is recommended

Societal and Ethical Aspects of Data Science

  • GEOG 258 – Digital Geographies (5); no prerequisites
  • INFO 350 – Information Ethics and Policy (5); no prerequisites
  • HCDE 512 – Human Centered Data Science; no prerequisites
  • SOC 225 – Data and Society (3 credits + 2 credits); no prerequisites
  • IMT 575 – Data Science III: Scaling, Applications, and Responsibility (also under data management above); Prerequisite: IMT 574
  • TCSS 588 – Bioinformatics (5 credits) at the Tacoma campus; Prerequisites: an undergraduate level algorithms course
  • BIOST 532 – Research Ethics in Data Science (2 credits); no prerequisites

Marketing Analytics

  • MKTG 462 – Customer Analytics – Customer Analytics (4 credits); Prerequisite: MKTG 301
  • MKTG 464 – Analytics for Marketing Decisions – Analytics for Marketing Decisions (4 credits); Prerequisite: MKTG 301
  • MKTG 466 – Digital Marketing – Digital Marketing (4 credits); Prerequisite: MKTG 301

Policy, Management, and Decision-Making

  • PUBPOL 542 – Computational Thinking for Governance Analytics; no prerequisites
  • PUBPOL 543 – Visual Analytics for Policy and Public Managers; no prerequisites
  • PUBPOL 599 B – Data-Driven Management and Policy (online, open to any graduate student)