Data Science Methods and Applications (Interdisciplinary Minor)

Coordinator: Elin Waring (Sociology)

Steering Committee: Juan DelaCruz (Economics and Business), Itai Feigenbaum (Computer Science), Juliana Maantay (Earth, Environmental, and Geospatial Sciences), Elia Machado (Earth, Environmental, and Geospatial Sciences), Megan Owen (Mathematics), Nikolaos Papanikolaou (Economics and Business), Naomi Spence (Sociology)

New Interdisciplinary Minor in Data Science Methods and Applications offered by the Sociology department.

Program Description: 15 to 18-Credit Minor in Data Science Methods and Applications

The 15 to 18-credit interdisciplinary minor in data science methods and applications is appropriate for students in majors across various disciplines who are interested in learning methods for working with big, complex, and/or "messy" data and application to real world topics. The minor provides students with interdisciplinary course work focused on obtaining, managing, analyzing, interpreting and communicating about data in all of its forms. Students will learn Python and R programming, as well as other languages used by data scientists.

Degree Requirements

Group 1 Required (9 Credits)

Credits
MAT 128Foundations of Data Science

3

MAT 328Techniques in Data Science

4

SOC 3470Reasoning with Data

3

MAT 128: (prerequisite: Score of 65 or higher on College Math section of Accuplacer exam or department permission.)

MAT 328: (prerequisite: MAT 128)

SOC 3470: (prerequisite: Completion of College Math Requirement; PHI 169 or a 200 level Sociology course; or by permission of Department.)

Group 2 (3-5 Credits)

Disciplinary Data Analysis Course 

Select as appropriate:

Credits
SOC 345Quantitative Analysis of Sociological Data

4

PSY 226Statistical Methods in Psychology

4

GEH 245Introduction to Quantitative Methods of Geography

3

ECO 302Economic Statistics

3

BBA 303Business Statistics I

3

BIO 240Biostatistics

3

HSD 269Fundamentals of Biostatistics for Health Professionals

3

MAT 301Applied Statistics and Computer Analysis for Social Scientists

3

MAT 327Statistical Inference

4

MAT 330Probability and Statistics

4

SOC 345: (prerequisite: SOC 301 with a grade of C- or better)

PSY 226: (prerequisite: PSY 166; and MAT 132 or MAT 172 or MAT 174 or MAT 175)

ECO 302: (prerequisite: ECO 166; and MAT 132 or MAT 171 or MAT 172 or MAT 174 or MAT 175)

BBA 303: (prerequisite: MAT 132 or MAT 171 or MAT 172 or MAT 174 or MAT 175

BIO 240: (prerequisite: BIO 166 and BIO 167 and MAT 175)

HSD 269: (prerequisite: MAT 132 or its equivalent, or demonstrated competence in database manipulation, spreadsheet calculations, and word processing)

MAT 301: (prerequisite: MAT 132 and MAT 171)

MAT 327: (prerequisite: MAT 176)

MAT 330: (prerequisite: MAT 176), or another course approved by the program.

Group 3 Elective (3-4 credits)

One elective from this list:

Credits
SOC 339American Demography

4

GEP 330Spatial Statistics and Advanced Quantitative Methods in Geography

3

GEP 360Geovisualization and Analytic Cartography

4

ECO 402Econometrics

4

CMP 414Artificial Intelligence

4

MAT 327Statistical Inference

4

MAT 349Operations Research

4

CMP 446Computational Tools for Bioinformatics

4

SOC 339: (prerequisite: SOC 301)

GEP 330: (prerequisite: GEP 204 or GEP 205 or instructor's permission. An introductory course in descriptive statistics is recommended.)

GEP 360: (prerequisite: GEP 204 or GEP 205 or Department Permission)

ECO 402: (prerequisite: ECO 302 or BBA 303)

CMP 414: (prerequisite: CMP 338)

MAT 327: (prerequisite: MAT 176)

MAT 349: (prerequisite: MAT 313 and CMP 167)

CMP 446: (prerequisite: BIO 166, CMP 167, and CMP 232)

Or course approved by the program. Independent studies must present a proposal that explains the relationship to data science.

Data Science students should be aware that graduate programs in Data Science, Biostatistics, and Data Analytics generally expect that students have completed at least Calculus 1, Linear Algebra, and Programming 1 (CMP 167) at the undergraduate level.