MyOcean

Data Science Option

(starting Summer 2024)

The goal of this option is to educate our graduate students in the foundations of data science and is customized to Oceanography. The Data Science Option focuses on students who will apply existing methods for data science in their research. Many of the courses listed here are recommended prerequisites for the Advanced option.

Students are required to take at least three courses distributed across the four following areas: Introduction to Data Science and Software Development, Statistics and Machine Learning, Data Management and Visualization, and Field Specific Applications Courses. Each course must be taken from a different area and a minimum of 11 total credits is required, with 9 credits unique to the DSO.

Introduction to Data Science and Software Development

AMATH 583: High-Performance Scientific Computing (5 credits)
CSE 583: Software Development for Data Scientists (4 credits)
CHEME 546: Software Engineering for Molecular Data Scientists (3/3 credits)
ME 574: Introduction to Applied Parallel Computing for Engineers (3 credits)
FISH 549: Best Practices in Environmental Data Science (3 credits)

Statistics and Machine Learning

AMATH 515: Optimization: Fundamentals and Applications (5 credits)
ATM S 552: Objective Analysis (3 credits)
CSE/STAT 416: Introduction to Machine Learning (4 credits)
STAT 435: Introduction to Statistical Machine Learning (4 credits)
FISH 458: Advanced Ecological Modeling (5 credits)
FISH 556: Spatio-Temporal Models for Ecologists (5 credits)
FISH 560: Applied Multivariate Statistics for Ecologists (4 credits)
QERM 514: Analysis of Ecological and Environmental Data I (4 credits)
AMATH 563: Inferring Structure of Complex Systems (5 credits)
ME/EE 578: Convex optimization (4 credits)
GENOME 559: Introduction to Statistical and Computational Genomics (3 credits)
ESS 569: Machine Learning in Geosciences (previously ESS 590, 4 credits)

Data Management and Visualization

AMATH 582: Computational Methods for Data Analysis (5 credits)
CSE 412: Introduction to Data Visualization (4 credits)
CSE 414: Introduction to Database Systems (4 credits)
CSE 442: Data Visualization (4 credits)
ESS 520: Introduction to Geographic Information Systems for the Earth Sciences (5 credits)
HCDE 411: Information Visualization (5 credits)
INFO 474: Interactive Information Visualization (5 credits)
FISH 554: Beautiful Graphics in R (2 credits)

Field Specific Applications Courses

ATMOS 559: Climate Modeling (3 credits)
ATMOS 565: Atmospheric Chemistry Modeling (3 credits)
ATMOS 581/AMATH 586/MATH 586 581: Numerical Analysis of Time Dependent Problems (5 credits)
ATMOS 582 582: Numerical Modeling of Geophysical Flows (3 credits)
ESS 523: Geophysical Inverse Theory (5 credits)
FISH 558: Decision Analysis in Natural Resource Management (5 credits)
FISH 546: Bioinformatics for Environmental Sciences (3 credits)
FISH 559: Numerical Computing for the Natural Resources (5 credits)
GENOME 569: Bioinformatics Workflows for High-Throughput Sequencing Experiments (1.5 credits)
GENOME 540: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis (4 credits)
BIOST 545: Biostatistical Methods for Big Omics Data (3 credits)
AMATH 581: Scientific Computing (5 credits)

Additionally, students must register for at least 2 quarters of the weekly “Topics in Data Science” or sometimes “Current Topics in Chemical Engineering”, CHEM E 599.