Students in the Data Science minor will gain literacy and fluency in data science methods and understand their implications for society and the world. This minor helps students leverage familiarity with data science in fields outside of data science, and gain skills and fluency to work with data in their major domain of study. 

Outcomes

Declaring the Data Science Minor

To declare the Data Science minor students must have

  1. Declared a major
  2. Completed at least 45 credits

Changes to your major/minor are posted once per quarter; the minor can be declared and posted any time during the quarter. You can declare the Data Science minor with your departmental adviser.

How to choose: Data Science Minor or Data Science Option

Some majors have a Data Science option, such as Statistics, Geography, and Atmospheric Sciences. If your major has this option, you should take that rather than this minor. This minor is primarily intended for students majoring in the Arts, Social Sciences, and Humanities, where their major does not include a Data Science option.

Upcoming Courses 

Here is a list of classes in the minor that you can register for in the upcoming quarter:

Spring 2021

 

Requirements

The minor consists of six courses for a total of 25 credits, per the below:

1. One Data Studies Course

Students will acquire foundational data literacies, understand the opportunities and limitations of data-intensive methods and epistemologies, and explore the broader implications of data science. Courses will lay the conceptual and theoretical foundations for understanding the role of data in knowledge production (either within specific domains or more generally), and promote critical reflection on the ethical, social, cultural, and political dimensions of data.

List 1: Data Studies Courses
  • ANTH 303 - Technologies of Health (5cr).  Prerequisite: ANTH 208, ANTH 215, or ANTH 302.
  • ANTH 473 - Anthropology of Science and Technology (5cr). Prerequisite: one 200-level ANTH course.
  • BIOL 270/INFO 270 - Calling BS - Data Reasoning in a Digital World (4cr). No prerequisites.
  • GEOG 258 - Digital Geographies (5cr). No prerequisites.
  • JSIS 310 - Data Ethnography (5cr). No prerequisites.
  • PHG 301 - Introduction to Genetic Epidemiology (5cr). No prerequisites.
  • PHG 303 - Direct-to-Consumer Genetic Testing: Uses and Issues (5cr). No prerequisites.
  • SOC 225 - Data and Society (3-5cr). No prerequisites.
  • HSTAA 317 - History of the Digital Age (5 cr.) No prerequisites. 

2. One Data Skills Course

Students will acquire basic knowledge of data science methods such as programming, data acquisition and management practices, software tools common to data science, statistics, machine learning, data visualization, and qualitative analysis. Students will be able to choose the methods they study and the depth of their study.

List 2: Data Skills Courses
  • AMATH 482 – Computational Methods for Data Analysis; Prerequisite: either MATLAB and linear algebra or permission of instructor
  • ASTR 324 – Introduction to AstroStatistics and Big Data in Astronomy (3cr). Prerequisite
  • BIOL 419/519 - Data Science for Biologists (4 credits); no prerequisites
  • CSE160 - Data Programming (Python) (4 credits) - no prerequisites
  • CSE163 - Intermediate Data Programming (Python) (4 credits) - prerequisite CSE160 or CSE142 
  • CSE180/INFO180/STAT180 - Introduction to Data Science (4 credits) - basic math as prerequisite (see catalog for details)
  • CSE412 - Introduction to Data Visualization (4 credits) - prerequisites CSE143 or CSE 163
  • CSE414 - Introduction to Data Management (4 credits) - prerequisites  CSE143 [or CSE 163]
  • CSE416 - Introduction to Machine Learning (4 credits) - prerequisites CSE143 or CSE160
  • ECON 484 - Econometrics and Data Science (5 credits);  Prerequisite: ECON 482; MATH 126.
  • ECON 488 Causal Inference (5 credits); Prerequisite: ECON 482; recommended: MATH 126
  • GEOG 360 – GIS & Mapping (5); no prerequisites
  • GEOG 465 – GIS Database and Programming (5) Prerequisite: GEOG 360
  • GEOG 482 – GIS Data Management (5) Prerequisite: GEOG 360
  • HCDE 411 – Information Visualization (5 credits) Prerequisites: HCDE 308 and HCDE 310
  • INFO 201 - Technical Foundations (R) (5 credits) - no prerequisites
  • INFO 370 - Core Methods in Data Science (5 credits); Prerequisite: INFO 201; and CSE 142 or CSE 143; and either CS&SS 221, SOC 221, STAT 221, STAT 311, MATH 390, STAT 390, QMETH 201, or Q SCI 381
  • INFO 371 – Advanced Method in Data Science (5 credits); Prerequisite: INFO 370
  • 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/SOC 221, STAT 311, or STAT 390/MATH 390
  • LING 421 - R for Linguists (5 credits) - Prerequisite: LING 200 or LING 400
  • STAT 220 Principles of Statistical Reasoning (5 credits) - no prerequisites
  • STAT/SOC/CS&SS 221 Introduction to Statistics for the Social Sciences (5 credits) - no prerequisites
  • STAT 311
  • STAT 391 – Quantitative Introductory Statistics for Data Science (4); Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395
  • STAT 403 – Introduction to Resampling Inference (4); Prerequisite: either STAT 311/ECON 311, STAT 341, STAT 390/MATH 390, STAT 481/ECON 481, or Q SCI 381 and Q SCI 482
  • STAT 435 - Introduction to Statistical Machine Learning (4 credits) - Prerequisite: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 308.
  • MSE 479 - Big Data for Materials Science (3 credits) Prerequisites: MSE/477/CHEM441.
  • BIO A 344 - Applied Biomechanics of Human Movement (5 credits); No prerequisites. 
  • Q SCI 381 - Introduction to Probability and Statistics (5 credits); Prerequisite: either MATH 120, MATH 124, MATH 125, MATH 126, Q SCI 190, or Q SCI 291, or a minimum score of 2 on advanced placement test, or a score of 153-163 on the MPT-AS placement test.
  • Q SCI 482 - Statistical Inference in Applied Research I  (5 credits); Prerequisite: Either STAT 311 or Q SCI 381
  • Q SCI 483 - Statistical Inference in Applied Research II  (5 credits); Prerequisite: Q SCI 381 
  • STAT 302 - Statistical Software and its Applications (3 credits); Prerequisite: Either STAT 311/ECON 311 or STAT 390/Math 390. 
  • BIOST 310 - Biostatistics for Health Sciences (4 credits); No prerequisites. 
  • ARCHY 484 - Archeological GIS  (5 credits); Prerequisite: Any 200 level ARCHY course. 
  • BIO A 423 - Social Networks And Health: Biocultural Perspectives (5 credits)

3. One quarter of CSE 491 (Integrative experience in Data Science) 

Information about CSE 491
  • Taught yearly in Spring Quarter 
  • Direct questions about CSE 491 to Data Science Advising at dataminor@uw.edu. Do not contact Computer Science Advising. 

4. Additional electives to reach 25 credits: selected from Lists 1, 2, 3, and/or 4.

List 1: Data Studies Courses (No more than 4 courses from this list can count toward the Data Science Minor)
  • ANTH 303 - Technologies of Health (5cr).  Prerequisite: ANTH 208, ANTH 215, or ANTH 302.
  • ANTH 473 - Anthropology of Science and Technology (5cr). Prerequisite: one 200-level ANTH course.
  • BIOL 270/INFO 270 - Calling BS - Data Reasoning in a Digital World (4cr). No prerequisites.
  • GEOG 258 - Digital Geographies (5cr). No prerequisites
  • JSIS 310 - Data Ethnography (5cr). No prerequisites.
  • PHG 301 - Introduction to Genetic Epidemiology (5cr). No prerequisites.
  • PHG 303 - Direct-to-Consumer Genetic Testing: Uses and Issues (5cr). No prerequisites.
  • SOC 225 - Data and Society (3-5cr). No prerequisites.
  • HSTAA 317 - History of the Digital Age (5 cr.) No prerequisites. 
List 2: Data Skills Courses (No more than 4 courses from this list can count toward the Data Science Minor)
  • AMATH 482 – Computational Methods for Data Analysis; Prerequisite: either MATLAB and linear algebra or permission of instructor
  • ASTR 324 – Introduction to AstroStatistics and Big Data in Astronomy (3cr). Prerequisite
  • BIOL 419/519 - Data Science for Biologists (4 credits); no prerequisites
  • CSE160 - Data Programming (Python) (4 credits) - no prerequisites
  • CSE163 - Intermediate Data Programming (Python) (4 credits) - prerequisite CSE160 or CSE142 
  • CSE180/INFO180/STAT180 - Introduction to Data Science (4 credits) - basic math as prerequisite (see catalog for details)
  • CSE412 - Introduction to Data Visualization (4 credits) - prerequisites CSE143 or CSE 163
  • CSE414 - Introduction to Data Management (4 credits) - prerequisites  CSE143 [or CSE 163]
  • CSE416 - Introduction to Machine Learning (4 credits) - prerequisites CSE143 or CSE160
  • ECON 484 - Econometrics and Data Science (5 credits);  Prerequisite: ECON 482; MATH 126.
  • ECON 488 Causal Inference (5 credits); Prerequisite: ECON 482; recommended: MATH 126
  • GEOG 360 – GIS & Mapping (5); no prerequisites
  • GEOG 465 – GIS Database and Programming (5) Prerequisite: GEOG 360
  • GEOG 482 – GIS Data Management (5) Prerequisite: GEOG 360
  • HCDE 411 – Information Visualization (5 credits) Prerequisites: HCDE 308 and HCDE 310
  • INFO 201 - Technical Foundations (R) (5 credits) - no prerequisites
  • INFO 370 - Core Methods in Data Science (5 credits); Prerequisite: INFO 201; and CSE 142 or CSE 143; and either CS&SS 221, SOC 221, STAT 221, STAT 311, MATH 390, STAT 390, QMETH 201, or Q SCI 381
  • INFO 371 – Advanced Method in Data Science (5 credits); Prerequisite: INFO 370
  • 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/SOC 221, STAT 311, or STAT 390/MATH 390
  • LING 421 - R for Linguists (5 credits) - Prerequisite: LING 200 or LING 400
  • STAT 220 Principles of Statistical Reasoning (5 credits) - no prerequisites
  • STAT/SOC/CS&SS 221 Introduction to Statistics for the Social Sciences (5 credits) - no prerequisites
  • STAT 311
  • STAT 391 – Quantitative Introductory Statistics for Data Science (4); Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395
  • STAT 403 – Introduction to Resampling Inference (4); Prerequisite: either STAT 311/ECON 311, STAT 341, STAT 390/MATH 390, STAT 481/ECON 481, or Q SCI 381 and Q SCI 482
  • STAT 435 - Introduction to Statistical Machine Learning (4 credits) - prerequisite: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 308.
  • MSE 479 - Big Data for Materials Science (3 credits) Prerequisites: MSE/477/CHEM441.
  • BIO A 344 - Applied Biomechanics of Human Movement (5 credits); No prerequisites. 
  • Q SCI 381 - Introduction to Probability and Statistics (5 credits); Prerequisite: either MATH 120, MATH 124, MATH 125, MATH 126, Q SCI 190, or Q SCI 291, or a minimum score of 2 on advanced placement test, or a score of 153-163 on the MPT-AS placement test.
  • Q SCI 482 - Statistical Inference in Applied Research I  (5 credits); Prerequisite: Either STAT 311 or Q SCI 381
  • Q SCI 483 - Statistical Inference in Applied Research II  (5 credits); Prerequisite: Q SCI 381 
  • STAT 302 - Statistical Software and its Applications (3 credits); Prerequisite: Either STAT 311/ECON 311 or STAT 390/Math 390. 
  • BIOST 310 - Biostatistics for Health Sciences (4 credits); No prerequisites. 
  • ARCHY 484 - Archeological GIS  (5 credits); Prerequisite: Any 200 level ARCHY course. 
  • BIO A 423 - Social Networks And Health: Biocultural Perspectives (5 credits)
List 3: Cross-Cutting: On Ramp Course List (designed to be taken at the beginning of the program, that convey the potential of this new field in various domains, no prereqs)
  • CS&SS 321/SOC 321/STAT 321 - Data Science and Statistics for Social Sciences I (5 credits) ; No prerequisites
  • SOC 225 Data & Society (5-credit option) ; No prerequisites
  • HSTCMP 202 - Digital World Wars I and II  (5cr.); No prerequisites. 
  • OCEAN 215 - Methods of Oceanographic Data Analysis (4cr.)
  • ARCHY 208 - Intro to Archeological Data Science (5 credits); No prerequisites. 
  • ARCHY 235 -  Exploring Graffiti: a landscape archeology and data science project (5 credits); No prerequisites. 
List 4: Cross-Cutting: Synthesis Course List (designed to be taken towards the end of the program where students use theories, questions, and data that are relevant to the domain alongside data science knowledge and skills, often in the context of a project-based learning environment)
  • BIO A 420 Anthropological Research on Health Disparities
  • DXARTS 490 Data-Driven Art
  • ECON 487 - Data Science for Strategic Pricing (5 credits); Prerequisite: minimum grade of 2.0 in ECON 300; minimum grade of 2.0 in either ECON 382 or ECON 482 (ECON 482 recommended). 
  • FREN 379 French and Francophone Cultural and Literary History through Digital Archives and Tools
  • GEOG 458 – Advanced Digital Geographies (5cr) Prerequisite: Geog 360
  • LING 471 Computational Methods for Linguists (5 credits); Prerequisite: either LING 450 or LING 461.
  • LING472/CSE472 Introduction to Computational Linguistics (5 credits); Prerequisite: either LING 200 or LING 400; either LING 461 or CSE 311. 
  • MKTG 462 – Customer Analytics – Customer Analytics (4 credits); Prerequisite: MKTG 301
  • MKTG 464 – Analytics for Marketing Decisions – Analytics for Marketing Decisions (4 credits); Prerequisite: MKTG 301
  • MKTG 466 – Digital Marketing – Digital Marketing (4 credits); Prerequisite: MKTG 301
  • SOC 403 – Sociology in Practice: Applied Community Research Program
  • RE 497 - Real Estate Data Modeling (4 credits); Prerequisite: RE 250 and RE 416
  • MSE 477 - Data Science and Materials Informatics (3 credits); Prerequisite: CSE 160 or CSE 163 
  • MSE 478 - Materials and Device Modeling (3 credits); Prerequisite: MSE/CHEM 441
  • ARCHY 494 - Archaeological Data Visualization (5 credits); No prerequisites. 
  • ARCHY 496 - Computational Quantitative Methods in Archeology  (3 credits); No Prerequisites. 
  • BIO A 484 - Applied Human Growth and Development (5cr). No prerequisites. 
  • GEOG 458 - Advanced Digital Geographies (5cr). Prerequisite: GEOG 360. 
  • DXARTS 482 - Data-drive Art II 

5. A minimum of 15 credits at the 300-400 level.

6. A minimum of 15 credits must be taken outside of the student's major requirements.

Data Science Minor Planning Worksheet

Course substitutions

Course substitutions are allowed, but must be approved by the curriculum committee. Students must petition a substitution with the Data Science adviser (via form below) who will relay the petition to the committee. The curriculum committee meets once a month to review petitions. Petitions are due by the 25th day of each month. Petitions submitted after the 25th will be reviewed with the following month's requests. 

Data Science Course Substitution Petition 

Completing the minor

A student who declares the Data Science minor must submit a graduation application, specific to the Data Science minor. This application must be submitted after a student applies to graduate with their major department, and no later than the second week of the quarter in which they intend to graduate. This graduation application is completed through the Data Science minor adviser. Minor Graduation Applications will be processed in the first few weeks of the quarter in which you have applied to graduate, regardless of the quarter of submission. If the graduation application is not submitted, the Graduation and Academic Records Office may contact the student to ask if they wish to pursue the minor or drop it.

Data Science Minor Graduation Application 

Data Science Minor Advising

Data Science advisers offer regular information sessions to learn more about the minor.

Winter 2021 Information Sessions 

If you still have questions after attending an information session you can schedule an appointment with an adviser.

Drop-In Advising Thursdays, 3pm-4pm 

Schedule Data Science Advising Appointment

Information for faculty

Faculty are welcome to submit courses for consideration to be added to the minor. The minor has a governance process that requires proposals to be reviewed by an executive committee (meeting monthly) and a curriculum committee (meeting quarterly). To submit a course for consideration, please complete this form and attach a syllabus to the last question on the form.