Data Science Options

PhD Data Science Option and Advanced Data Science Option

The Department of Atmospheric and Climate Science (AtmS) has partnered with the eScience Institute at the University of Washington to provide a PhD curriculum in Atmospheric Sciences with the Data Science Option (DSO) or Advanced Data Science (ADSO).

PhD students choosing these options will benefit from a formal education in the principles of data science and develop techniques that they can apply to their graduate research and future careers.

Please note that some of the required courses for the Data Science options may have prerequisites, which add to the overall course load, and may have capped enrollment, which may delay entry. Students interested in these options should carefully plan their schedule and check with relevant instructors to ensure they can complete the program in a reasonable time frame. Requirements for the Atmospheric Sciences graduate program should take priority. Students must submit a course plan to their Faculty Adviser (cc the GPC and Academic Adviser) for approval.

A course for the AtmS-DSO or AtmS-ADSO options may be replaced by an equivalent or more advanced course in the same area upon approval. Students should email the Graduate Program Coordinator (cc their Faculty Adviser and Academic Adviser) to request an exception.

PhD Data Science Option

Minimum of 90 credits to graduate, which must include:

  • 36 graded course credits (excludes seminars & colloquia):
    • 25-28 credits in ATM S core courses total credits depend on choice of dynamics sequence)
    • 5-8 elective credits in student’s area of interest (total credits depend on choice of dynamics sequence)
    • 9-15 credits from the Standard Data Science Option list below. These courses may also count towards the elective requirement.
    • 2 credits ENGR 591 (Data Science Seminar)
  • 27 credits (minimum) ATM S 800 Doctoral Dissertation
  • Quarterly seminar and colloquia

Students must take at least three courses out of the following four areas: Software Development, Statistics and Machine Learning, Data Visualization and Data Management, and Department Specific Courses. Each course must be from a different area. These courses may also count towards the elective requirement:

Software Development for Data Science

CSE 583 – Software Development for Data Scientists (4 credits) ; no prerequisites
AMATH 581 –    Scientific Computing (5 credits)
AMATH 583 –    High-Performance Scientific Computing (5 credits) Prerequisite: AMATH 581
OCEAN 506 (temporary number)  –  Effective Computing (3 credits)

Statistics and Machine Learning

CSE 416 / STAT 416 – Introduction to Machine Learning (4 credits); Prerequisites: (CSE 143 or CSE 160) and (STAT 311 or STAT 390)
STAT 435 – Introduction to Statistical Machine Learning (4 credits); Prerequisites: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 308
CSE 546 – Machine Learning (4 credits) or STAT 535 Statistical Learning: Modeling, Prediction, and Computing (3 credits)
STAT 509/ECON 580/CS&SS 509 – Introduction to Mathematical Statistics: Econometrics I (5 credits)
STAT 512-513 – Statistical Inference (4 credits each)
AMATH 515 – Fundamentals of Optimization (5 credits)
ATM S 552 – Objective Analysis (3 credits)

Data Visualization & Data Management

CSE 414 – Introduction to Database Systems (4 credits) Prerequisites: CSE 143 (will soon also allow CSE 163)
CSE 544 – Principles of DBMS (4 credits)
CSE 442 – Data Visualization (4 credits); Prerequisite: CSE 332
CSE 412- Introduction to Data Visualization (4 credits); Pre-requisites will be CSE143 or CSE 163
CSE 512 – Data Visualization (4 credits)
INFO 562 – Interactive Information Visualization (4 credits); no prerequisites
INFO 474 –  Interactive Information Visualization (5 credits); Prerequisites: INFO 343 or CSE 154; and CSE 143; and either Q METH 201, Q SCI 381, STAT 221/CS&SS 221/SOC221, STAT 311, or STAT 390/MATH 390
HCDE 411 –  Information Visualization (5 credits) Prerequisites: HCDE 308 and HCDE 310
ESS 420 – Introduction to GIS for the Earth Sciences
ESS 520  – Application in Geophysical Analysis with Python for the Earth Sciences (4 credits)
AMATH 582 – Computational Methods for Data Analysis (5 credits) Prerequisite: either MATLAB and linear algebra
OCEAN 502 – Spatial Information Technology in Ecosystem Sciences (3 credits)

Department Specific Requirement

ATM S 559 – Climate Modeling (3 credits)
ATM S 565 – Atmospheric Chemistry Modeling (3 credits)
ATM S 552  –  Objective Analysis (3 credits)
ATM S 581   –  Numerical Analysis of Time Dependent Problems (5 credits) Joint with AMATH 586, MATH 586, Prerequisite: either AMATH 581, AMATH 584/MATH 584, AMATH 585/MATH 585
ATM S 582 – Advanced Numerical Modeling of Geophysical Flows (3 credits) Prerequisite: ATM S 581 and AMATH 586 or MATH 586.
ESS 523  – Geophysical Inverse Theory (5 credits)

Although ATM S 552 appears in two areas, the course can only count for one area. A student who takes ATM S 552 must choose the area for which they intend it to count.

PhD Advanced Data Science Option

Minimum of 90 credits to graduate, which must include:

  • 36 graded course credits (excludes seminars & colloquia):
    • 25-28 credits in ATM S core courses total credits depend on choice of dynamics sequence)
    • 2-5 credits in elective courses in student’s area of interest (total credits depend on choice of dynamics sequence)
    • 11-13 credits from the Advanced Data Science Option list
    • 4 credits ENGR 591 (Data Science Seminar)
  • 27 credits (minimum) ATM S 800 Doctoral Dissertation
  • Quarterly seminar and colloquia

Students must take at least three of the four Advanced Data Science Option courses listed below:

CSE 544 – ​Principles of DBMS (4 credits)
CSE 546 – ​Machine Learning (4 credits)​ or STAT 535 ​Statistical Learning: Modeling, Prediction, and Computing (3 credits).
CSE 512 – ​Data Visualization (4 credits)
STAT 509/ECON 580/CS&SS 509 – ​Introduction to Mathematical Statistics: Econometrics I (5 credits) ​or STAT 512-513 – ​Statistical Inference (4 credits each)

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