Master of Science in Environmental Data Science

Program Director (Data Science): Yolanda Gil, PhD
Program Co-Director (Environmental Studies): John Heidelberg, PhD
Program Associate Director (Environmental Studies): Jessica Dutton, PhD

The Master of Science in Environmental Data Science is an interdisciplinary joint degree program offered by the Viterbi School of Engineering and the Dornsife College of Letters, Arts and Sciences. Students will complete a core set of courses to provide a foundation in data science and environment/public policy and choose electives to optimize their preparation for their preferred career path and unique professional opportunities. Data science technologies can complement interdisciplinary analyses of complex environmental issues in the emerging field of environmental data science to address societal and research challenges in climate change, water and air pollution, policy analysis, terrestrial and aquatic ecosystem management, and biodiversity among others.

The curriculum is designed to be accessible to students with any background, including students with an environmental background and no computer science knowledge as well as students with a computer science background and no environmental knowledge. Students with undergraduate degrees in computer science, engineering, science or mathematics will acquire the necessary knowledge to analyze environmental data with diverse sources and purposes and can request to replace introductory data science courses with more advanced ones. Students with undergraduate degrees in environmental studies, biology, Earth sciences, and related sciences will acquire formal and practical data science skills and can request to substitute introductory courses in environmental studies with more advanced ones. There is no requirement of prior knowledge of programming or computer science, as the curriculum is designed with special introductory courses that are accessible to students with diverse backgrounds.

Students will learn a range of data science skills such as developing scalable data systems, using state-of-the-art software and infrastructure for data science, designing data analyses with statistical methods, and working in multi-disciplinary data science teams. They will also gain a foundation in the central theories, concepts and principles of natural sciences, and how to leverage data to form relevant questions in environment management and sustainability to find actionable insights to understand and protect Earth’s natural resources. At the start of the program, students will be offered specific opportunities in problem-based learning in partnership with the USC Wrigley Institute for Environmental Studies.

Prospective students can refer to the Dornsife website for more information about the program.

Upon graduation, students will have both foundational and practical skills for a successful career in environmental data science with a digital portfolio assembled throughout their coursework and ample job opportunities to be involved in practical environmental data science projects on campus.

The program prepares students for a range of professional paths in research and environmental data management for positions in areas such as: state and federal government natural resource regulation, academic research, environmental consultation and non-profit environmental advocacy, restoration planning, conservation and wildlife management, remote sensing specialists, and corporate responsibility and monitoring.

Applications will be reviewed by both the Dornsife College of Letters, Arts and Sciences and the Viterbi School of Engineering.

Applicants should have an undergraduate degree in science, technology, engineering, math or related social science from a regionally accredited university, programming experience or strong math background, and a satisfactory cumulative undergraduate GPA (grade point average).

Prospective students will need to complete and submit:

The USC Graduate Admission Application, along with:
- Electronic Transcripts
- Resume/CV
- Personal Statement
- Letters of Recommendation (2 Highly Recommended)
English Proficiency Requirement (International Students Only)
Financial Documentation (On-Campus International Students Only)

As of September 7, 2021, the GRE exam is not required.

The most up to date description of the program is in the USC Catalogue. Course descriptions are available here.

Total Units: 32

Core Courses (24 units):

DSCI 510 Principles of Programming for Data Science (4 units)
DSCI 549 Introduction to Computational Thinking and Data Science (4 units)
DSCI 550 Data Science at Scale (4 units)
ENST 500 Interdisciplinary Approaches to Environmental Studies (4 units)
ENST 530 Environmental Risk Analysis (4 units)
SSCI 581 Concepts for Spatial Thinking (4 units)

Data Science Elective - Choose One Course (4 units):

DSCI 551 Foundations of Data Management (4 units)
DSCI 552 Machine Learning for Data Science (4 units)
DSCI 553 Foundations and Applications of Data Mining (4 units)
DSCI 554 Data Visualization (4 units)
DSCI 555 Interaction Design and Usability Testing (4 units)
DSCI 556 User Experience Design and Strategy (4 units)
DSCI 564 Probability and Statistics for Data Science (4 units)
DSCI 599 Special Topics (4 units) (with adviser approval)

Environmental Studies Elective - Choose One Course (4 units):

ENST 492 Directed Environmental Policy and Science Internship (2, 4 units)
ENST 500 Interdisciplinary Approaches to Environmental Studies (4 units)
ENST 520 Environmental Law and Policy (4 units)
ENST 540 California Coastal Zone Science and Policy (4 units)
ENST 590 Directed Research Units (4 units)
GEOL 412 Oceans, Climate, and the Environment (4 units)
GEOL 425L Data Analysis in the Earth and Environmental Sciences (4 units)
GEOL 470L Environmental Hydrogeology (4 units)

Notes:

The fastest you may finish this program is in 4 semesters (2 academic years). Students may only take 2 courses per semester.
Students with a computer science background will have the option of replacing DSCI 510, DSCI 549, and DSCI 550 with DSCI 551, DSCI 552, and DSCI 553. As a result, they will be able to take additional data science elective courses.

All information contained here is summarized from the USC Catalogue and is considered non-official. For all rules, regulations, procedures, and outlines, please see the current academic year USC Catalogue. The USC Catalogue supersedes all other publications.

Current students follow degree requirements in effect for the academic year they began their studies at USC. If you are a current student, please refer to your STARS report or the appropriate USC Catalogue for your year. Students seeking to advance their catalogue year to follow updated curricula may contact their department advisor.