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Data Science PhD Options

The Department of Biology recognizes that many graduate students will work with large data sets, and expertise in this area is a skill that will benefit students beyond their degree program into a variety of career paths.  As a result, the Department of Biology joined with several other departments on campus to offer two Data Science Options for the Ph.D. in Biology degree.

To add a Data Science Option to your degree, please contact to the graduate program manager (the position will be filled soon. Meanwhile, please contact the Chair of the Department, David Perkel.)

 

Data Science Option

Overview

  1. One option from three of the following four areas:
    1. Software Development
    2. Machine Learning and/or Statistics
    3. Data Management and/or Data Visualization
    4. Computational Methods in Biology
  2. 2 quarters of the eScience Community Seminar (http://escience.washington.edu/get-involved/escience-community-seminar/)

Course Options

1. Software Development (minimum of 3 credits)

HIGHLY RECOMMENDED:

  1. BIOL 519: Data Science for Biologists (4)
  2. CSE 583: Software Development for Data Scientists (4)
  3. CHEME 546: Software Engineering for Molecular Data Scientists (3)
  4. AMATH 581: Scientific Computing (5)
  5. FISH 552/553 series: Introduction to R Programming for Natural Scientists (2) & Advanced R Programming for Natural Scientists (2)
  6. FISH 546: Bioinformatics for Environmental Sciences (3)

2. Statistics and Machine Learning (minimum of 3 credits)

HIGHLY RECOMMENDED:

  1. CSE 416/STAT 416: Introduction to Machine Learning (4)
  2. AMATH 582: Computational Methods for Data Analysis (5)

ALTERNATIVES:

  1. GENOME 559: Introduction to Statistical and Computational Genomics (3)
  2. PSYCH 524: Introduction to Statistics and Data Analysis (4)
  3. QERM 514: Analysis of Ecological and Environmental Data (4)
  4. FISH 556: Spatio-temporal Models for Ecologists (5)
  5. FISH 560: Applied Multivariate Statistics for Ecologists (4)
  6. ESRM 451: Analytical Methods in Wildlife Science (3)

3. Data Management and Data Visualization (minimum of 3 credits)

HIGHLY RECOMMENDED:

  1. CSE 414: Introduction to Database Systems (4)
  2. HCDE 511: Information Visualization (4)
  3. INFO 330: Databases and Data Modeling (5)
  4. PSYCH 531: Practical Issues in Data Analysis and Presentation (4)

4. Computational Methods in Biology (minimum of 3 credits)

HIGHLY RECOMMENDED:

  1. BIOL 511: Topics in Mathematical Biology (3)
  2. GENOME 540: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis (4)
  3. GENOME 541: Introduction to Computational Molecular Biology: Molecular Evolution (4)
  4. GENOME 559: Introduction to Statistical and Computational Genomics (3)
  5. SEFS 502: Analytical Techniques for Community Ecology (4)
  6. ATM S 559: Climate Modeling (3)
  7. ATM S 589: Paleoclimatology: Data, Modeling, and Theory (3)
  8. FISH 454: Ecological Modeling (5)
  9. SEFS 508: Plant Process and Systems Modeling (3)

eScience Community Seminar (minimum of 2 quarters)

CHEM E 599F: eScience Community Seminar (1 credit each quarter)

 

Advanced Data Science Option

  1. One course option from three of the following four areas:
    1. Data Management
      1. CSE 544: Principles of Database Systems (4)
    2. Machine Learning
      1. CSE 546: Machine Learning (4)
      2. STAT 535: Statistical Learning: Modeling, Prediction, and Computing (3)
    3. Data Visualization
      1. CSE 512:  Data Visualization (4)
    4. Statistics
      1. STAT 509: Econometrics I: Introduction to Mathematical Statistics (4)
      2. STAT 512 & 513: Statistical Inference I & II (8)
  2. 2 quarters of the eScience Community Seminar (http://escience.washington.edu/get-involved/escience-community-seminar/)

Date last changed Sep 23rd, 2022 @ 15:21:39 PDT