University of Washington

Graduate Student Options

  1. Learning Data Science /
  2. Data Science at UW /
  3. Data Science for Graduate Students /
  4. Graduate Student Options

The goal of this option is to educate students in the foundations of data science. The “Data Science” option aims to educate the next generation of thought leaders who will apply new methods for data science.

To ensure that the meaning of the Data Science Options remains consistent across departments and schools, all units in this program agree to shape their options by following the same overall framework. In this framework, to complete the  “Data Science” option, students take at least three courses out of the following four areas, each course from a different area:

Software Development for Data Science

  • CSE 583 – Software Development for Data Scientists (4 credits) ; no prerequisites.
  • ChemE 546 – Software Engineering for Molecular Data Scientists (3 credits); no prerequisites.

**or department specific**

Statistics and Machine Learning

  • CSE416 / STAT 416 – Introduction to Machine Learning (4 credits); Prerequisites: (CSE 143 or CSE 160) and (STAT 311 or STAT 390).
  • STAT 435 – Introduction to Statistical Machine Learning (4 credits); Prerequisites: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 308.
  • CSE 546 – Machine Learning (4 credits) or STAT 535 Statistical Learning: Modeling, Prediction, and Computing (3 credits).
  • STAT 509 – Introduction to Mathematical Statistics: Econometrics I (5 credits)
  • STAT 512-513 – Statistical Inference (4 credits each).

**or department specific**

Data Management and Data Visualization

  • CSE 414: Introduction to Database Systems (4 credits) Prerequisites: CSE 143 (will soon also allow CSE 163).
  • CSE 544 – Principles of DBMS (4 credits)
  • CSE 442 – Data Visualization (4 credits); Prerequisite: CSE 332
  • CSE 412- Introduction to Data Visualization (4 credits); Pre-requisites will be CSE143 or CSE 163.
  • CSE 512 – Data Visualization (4 credits)
  • INFX 562 – Interactive Information Visualization (4 credits); no prerequisites.
  • INFO474: 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/SOC221, STAT 311, or STAT 390/MATH 390.
  • HCDE 511: Information Visualization/Data Visualization and Exploratory Analytics (4 credits)
  • HCDE411: Information Visualization (5 credits) Prerequisites: HCDE 308 and HCDE 310.

Department-Specific Requirements

Participating departments may identify specific data science related courses offered through their program that can be used to fulfill this requirement.

Additionally, to further expand students’ education and create a campus-wide community, students register for at least 2 quarters in the weekly “Topics in Data Science” or sometimes “Current Topics in Chemical Engineering” (CHEME 599). Among other topics, the community seminar covers important aspects of data science such as ethics, biases, and societal implications of data science.