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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 PhD in Biology degree.

To add a Data Science Option to your degree, please contact Krista Clouser at kcostes@uw.edu.

 

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 Apr 20th, 2020 @ 10:12:45 PDT