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Graduate Program in Neuroscience

Data Science Option

The Data Science Option is approved and will be effective Winter Quarter 2027.

Neuroscience is an increasingly interdisciplinary field requiring tools and education at the intersection of Computer Science and Data Science. The Data Science Option (DSO) trains students in the foundations of data science, allowing them to develop skills in various techniques and tools with the goal of applying these newly acquired skills to their own research and to provide them with valuable proficiencies for the job market after completing graduate school. This option is designed for students with little or no background in data science, computer science, or coding.

Students

Eligible students for the DSO include all full-time PhD Students in the Neuroscience program in good standing who have completed their first year of courses. Students will declare their intent to complete the DSO by receiving approval from their chair (PI) and Dr. Golub, the faculty overseeing the DSO. Students should submit the signed DSO form to the Neuroscience GPA, and they may submit the form at any point after the completion of their first-year courses. In cases where a graduate student does not graduate from the PhD program, the student will retain the “Data Science Option” notation on their transcript even if they go inactive.

DSO PhD Curriculum

Students who choose to participate in the DSO must complete Curriculum Requirements, with the exceptions from the regular curriculum listed below:

    • Must select 9 credits from the Data Science Topic Areas (see below)
    • Waived from the 10 credits of Electives in the regular PhD Curriculum Requirements
    • eScience Seminar: must register for 2 quarters of ENGR591 (1 credit each; 2 credits total)

Data Science Topic Areas

Instructions: complete at least 9 credits from 3 of the 4 topic areas.

Software Development for Data Science

  • CSE 583 – Software Development for Data Scientists (4.0 credits; AUT)
  • GENOME 559 – Introduction to Statistical and Computational Genomics with introductory Python (3.0 credits, WIN)
  • ChemE 546 – Software Engineering for Molecular Data Scientists (3.0 credits, WIN)
  • IMT 511– Introduction to Programming for Information and Data Science (4.0 credits, SPR and SUM)* additional course fee required

Statistics

  • STAT 556 – Introduction to Statistics and Probability (5.0 credits, AUT, fee-based)
  • STAT 512 – Statistical Inference (4.0 credits, AUT); Prerequisite: STAT 395 and STAT 421, STAT 423, STAT 504, or BIOST 512
  • STAT 513 – Statistical Inference (4.0 credits, WIN); Prerequisite: STAT 512
  • BIOST 511 – Medical Biometry I (4.0 credits, AUT)
  • BIOST 514 – Biostatistics I (4.0 credits, AUT)
  • BIOST 517 – Applied Biostatistics I (4.0 credits, AUT)
  • STAT 557 – Applied Statistics and Experimental Design (5 credits, WIN); Prerequisite: STAT/BIOST/DATA 556 or instructor’s permission
  • BIOST 512 – Medical Biometry II (4.0 credits, WIN); Prerequisite: BIOST 511 or BIOSTAT 517 or equivalent
  • BIOST 515 – Biostatistics II (4.0 credits, WIN); Prerequisite: BIOSTAT 514
  • BIOST 518 – Applied Biostatistics II (4.0 cr, WIN); Prerequisite: BIOSTAT 517
  • GENOME 560 – Introduction to statistical genomics (4.0 credits, SPR)
  • BIOST 526 – Bayesian Biostatistics (3.0 credits, SPR); Prerequisite: Any 400-level or higher statistics course or instructor’s permission
  • STAT 534 – Statistical computing (3.0 credits, SPR); Prerequisite: experience with programming in a high level language

Machine Learning

  • CSE 546 – Machine Learning (4.0 credits, AUT); Prerequisite: either CSE 312,  STAT 341, STAT 391 or equivalent
  • CSE 599N – Machine Learning for Neuroscience (4 credits, WIN)
  • STAT 535 – Statistical Learning: Modeling, Prediction, and Computing (3.0 credits, AUT); Prerequisite: experience with programming in a high level language
  • BIOST 546 – Machine Learning for Biomedical and Public Health Big Data (3.0 credits, WIN); Prerequisite: BIOST 511 or BIOST 512 and familiarity with R
  • CSE 517 – Natural Language Processing (4.0 credits, WIN); Prerequisite: Course assumes familiarity with probability and programming
  • STAT 548 – Machine Learning for Big Data (4.0 credits, SPR); Prerequisite: STAT 535 or CSE 546
  • STAT 558 – Statistical Machine Learning for Data Scientists (5.0 credits, SPR); Prerequisite: STAT/BIOST/DATA 557 or instructor’s permission
  • IMT 574 – Data Science II: Machine Learning and Econometrics (4.0 credits, SPR); Prerequisite: IMT 573*additional course fee required

Data Management and Data Visualization

  • CSE 414 – Introduction to Database Systems (4.0 credits, SPR)
  • CSE 544 – Principles of Database Management Systems (4.0 credits, WIN)
  • IMT 562 – Interactive Information Visualization (4.0 credits) *additional course fee required
  • CSE 512 – Data Visualization (4.0 credits, SPR)
  • IMT 561 – Visualization Design (4.0 credits, SPR) *additional course fee required
  • HCDE 511 – Information Visualization (4.0 credits, SPR)

FAQ

What’s an option? Will I earn a certificate for completing the DSO? 
An option is a notation that will go on your official UW transcript. It is not a certificate. Neuro PhD students who want to earn a certificate should consider the Graduate Certificate in Neural Computation and Engineering. 

May I take these DSO courses as S/NS or CR/NC? Or do I need to take them as graded credits? 
You may take them as S/NS or CR/NC if allowed by the course instructor. Refer to the Registrar’s instructions.

What if I have trouble getting into a certain class?  
These classes are in non-Neuro departments so our program can’t control if you get in or not. Please plan to register early for desired classes (as soon as registration opens).

Where is the DSO declaration form? 
The form is on the students forms page . After you receive the signatures, please return the completed form to the Neuro Graduate Program Advisor.