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

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 

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