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 Andrea Pardo
Data Science Option
Overview
- One option from three of the following four areas:
- Software Development
- Machine Learning and/or Statistics
- Data Management and/or Data Visualization
- Computational Methods in Biology
- 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:
- BIOL 519: Data Science for Biologists (4)
- CSE 583: Software Development for Data Scientists (4)
- CHEME 546: Software Engineering for Molecular Data Scientists (3)
- AMATH 581: Scientific Computing (5)
- FISH 552/553 series: Introduction to R Programming for Natural Scientists (2) & Advanced R Programming for Natural Scientists (2)
- FISH 546: Bioinformatics for Environmental Sciences (3)
2. Statistics and Machine Learning (minimum of 3 credits)
HIGHLY RECOMMENDED:
- CSE 416/STAT 416: Introduction to Machine Learning (4)
- AMATH 582: Computational Methods for Data Analysis (5)
ALTERNATIVES:
- GENOME 559: Introduction to Statistical and Computational Genomics (3)
- PSYCH 524: Introduction to Statistics and Data Analysis (4)
- QERM 514: Analysis of Ecological and Environmental Data (4)
- FISH 556: Spatio-temporal Models for Ecologists (5)
- FISH 560: Applied Multivariate Statistics for Ecologists (4)
- ESRM 451: Analytical Methods in Wildlife Science (3)
3. Data Management and Data Visualization (minimum of 3 credits)
HIGHLY RECOMMENDED:
- CSE 414: Introduction to Database Systems (4)
- HCDE 511: Information Visualization (4)
- INFO 330: Databases and Data Modeling (5)
- PSYCH 531: Practical Issues in Data Analysis and Presentation (4)
4. Computational Methods in Biology (minimum of 3 credits)
HIGHLY RECOMMENDED:
- BIOL 511: Topics in Mathematical Biology (3)
- GENOME 540: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis (4)
- GENOME 541: Introduction to Computational Molecular Biology: Molecular Evolution (4)
- GENOME 559: Introduction to Statistical and Computational Genomics (3)
- SEFS 502: Analytical Techniques for Community Ecology (4)
- ATM S 559: Climate Modeling (3)
- ATM S 589: Paleoclimatology: Data, Modeling, and Theory (3)
- FISH 454: Ecological Modeling (5)
- 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
- One course option from three of the following four areas:
- Data Management
- CSE 544: Principles of Database Systems (4)
- Machine Learning
- CSE 546: Machine Learning (4)
- STAT 535: Statistical Learning: Modeling, Prediction, and Computing (3)
- Data Visualization
- CSE 512: Data Visualization (4)
- Statistics
- STAT 509: Econometrics I: Introduction to Mathematical Statistics (4)
- STAT 512 & 513: Statistical Inference I & II (8)
- Data Management
- 2 quarters of the eScience Community Seminar (http://escience.washington.edu/get-involved/escience-community-seminar/)