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May 09, 2024
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2023-2024 Academic Catalog
Data Science Major
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 Return to: Programs of Study
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Data Science Overview
Data Science, broadly speaking, develops techniques for distilling knowledge and information out of empirical data. It is a dynamic, newly emerging field that combines techniques from Statistics, Mathematics, and Computer Science. It has applications particularly relevant to Clark’s programs in Economics, Business Analytics, GIS, IDCE, Biology, Chemistry, and Physics, although it can also interact with the quantitative aspects of any discipline. Nationwide, Data Science is emerging as an important and popular area of study with excellent employment prospects.
The Data Science major and minor at Clark aim at providing a solid education to students and preparing students for graduate study, and/or careers in industry and non-profits. With our curriculum and our teaching we pursue the following goals:
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Develop in students a solid foundation of data-centered computing and the ability to learn on their own;
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Develop in students a good understanding of the key statistics and computational concepts, principles, and techniques;
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Develop in students a broad range of practical skills required of data science professionals , such as formulating problems, designing data collection strategies, processing and analyzing data, extracting information from the data, and using the information to make sound decisions;
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Develop in students a general understanding of the social issues surrounding data science and the code of conduct in this discipline;
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Develop in students an appreciation and desire for knowledge and life-long learning;
- Foster creativity, independence, discipline, responsibility, respect, and ethical behavior.
Major Requirements
Data Science Major Requirements
8 Core Courses:
2 Mathematics Courses
2 Computer Science Courses
4 Data Science Courses
At least two 200-level Data Science Courses
6 Electives:
- 4 courses from 1 track (application domain, specialization) including at least 1 course at the 200-level
- 2 courses from the approved courses listed on this page which can be from any track, general electives (not associated with any track), and the collection of core courses that are not used towards the core requirements.
Biology Track (Primary Faculty Contact: Javier Tabima Restrepo)
BIOL 101 - Introduction to Biology I , BIOL 102 - Introduction to Biology II , BIOL 106 - Introductory Biostatistics , BIOL 123 - Introduction to Bioinformatics , BIOL 206 - Advanced Biostatistics , BIOL 209 - The Genome Project , BIOL 216 - Ecology , BIOL 265 - Population Genetics , BIOL 284 - Data Visualization and Exploration for the Biosciences in the Tidyverse
Chemistry/Biochemistry Track (Primary Faculty Contact: Charles Jakobsche)
CHEM 101 - Introductory Chemistry I , CHEM 102 - Introductory Chemistry II , CHEM 131 - Organic Chemistry Principles , CHEM 140 - Analytical Chemistry , CHEM 279 - Computer Biochemistry (This course has a prerequisite of BCMB 271 (not counted toward the data science major), which in turn requires BIOL 101 and BIOL 102 (eligible as data science electives, not for this track) and CHEM 131 (elective for this track)).
Computer Science Track (Primary Faculty Contact: Li Han)
CSCI 122 - Introduction to Discrete Structures , CSCI 160 - Algorithms CSCI 210 - Artificial Intelligence , CSCI 262 - Computer Vision , CSCI 250 - Software Engineering , CSCI 255 - Design and Analysis of Algorithms , CSCI 265 - Robotics and Intelligent Systems , CSCI 268 - Internet of Things , CSCI 270 - Theory of Computation , CSCI 220 - Database Management And Systems Design
Economics Track (Primary Faculty Contact: Edouard Wemy)
ECON 010 - Economics and the World Economy , ECON 011 - Principles of Economics , ECON 160 - Introduction to Statistical Analysis , ECON 204 - Microeconomic Theory Using Calculus , ECON 206 - Macroeconomic Theory , ECON 224 - Applied Game Theory , ECON 265 - Econometrics
Environmental Science Track (Primary Faculty Contact: Christopher Williams)
BIOL 101 - Introduction to Biology I , EN 101 - Environmental Science and Policy: Introductory Case Studies , GEOG 104 - Earth System Science , BIOL 206 - Advanced Biostatistics , BIOL 216 - Ecology , GEOG 205 - Introduction to Hydrology , GEOG 228 - Hydroclimatology , GEOG 232 - Landscape Ecology , GEOG 283 - Terrestrial Ecosystems and Global Change
Game Design/Production Track (Primary Faculty Contact: Elliot Epstein)
GAME 025 - Game Design Fundamentals , GAME 190 - Game Programming with Data Structures , GCPT 220 - Data Analytics and Modeling in Games , GAME 255 - Game Studio , GAME 260 - Serious Game Project , GAME 265 - Artificial Intelligence for Games , GAME 270 - Game Analytics
Geography/GIS Track (Primary Faculty Contact: Lyndon Estes)
GEOG 110 - Introduction to Quantitative Methods , GEOG 190 - Introduction to Geographic Information Science , GEOG 246 - Geospatial Analysis with R , GEOG 247 - Intermediate Quantitative Methods in Geography , GEOG 260 - GIS & Land Change Models , GEOG 279 - GIS & Map Comparison , GEOG 282 - Advanced Remote Sensing , GEOG 287 - New Methods in Earth Observation , GEOG 293 - Introduction to Remote Sensing , GEOG 296 - Advanced Raster GIS
Management Track (Primary Faculty Contact: Hamidreza Ahady Dolatsara)
MGMT 100 - The Art and Science of Management , ACCT 101 - Principles of Accounting , BAN 104 - Introduction to Management Information Systems , FIN 240 - Corporate Finance , MKT 230 - Marketing Management , MKT 231 - Marketing Research , MKT 234 - Consumer Behavior , MKT 238 - Digital Marketing , QBUS 110 - Quantitative Methods for Managers QBUS 250 - Operations Management , MGMT 260 - Applying the Art and Science of Management (Capstone) , MKT 237 - Branding Concepts and Principles , MGMT 210 - Management and Behavioral Principles
Mathematics Track (Primary Faculty Contact: Michael Satz)
MATH 123 - Introduction to Statistics , MATH 130 - Linear Algebra , MATH 131 - Multivariate Calculus , MATH 133 - Mathematical Modeling , MATH 210 - Introduction to Quantitative Finance , MATH 217 - Probability and Statistics , MATH 218 - Topics in Statistics , MATH 219 - Linear Models , MATH 220 - Introduction to Stochastic Modeling , MATH 244 - Differential Equations
Physics Track (Primary Faculty Contact: Barbara Capogrosso Sansone)
PHYS 120 - Introductory Physics - Part I, with Calculus , PHYS 121 - Introductory Physics - Part II, with Calculus , PHYS 123 - Methods of Physics , PHYS 127 - Computer Simulation Laboratory , PHYS 169 - Information Theory, Inference, and Networks , PHYS 219 - Electronics Laboratory , PHYS 243 - Technology of Renewable Energy
Psychology Track (Primary Faculty Contact: Andrew Stewart)
PSYC 101 - General Psychology , PSYC 105 - Statistics , PSYC 108 - Experimental Methods in Psychology , PSYC 200 - Lab in Program Evaluation , PSYC 201 - Lab in Social Psychology , PSYC 202 - Lab in Developmental Psychology , PSYC 210 - Research on Ideology and Violence , PSYC 217 - Research in Family Interactions
General Electives, Not Affiliated with Any Track
DSCI 103 - Data, Computing and Society & MATH 113 - Sports Analytics
Capstone:
A data science major may complete their capstone in a 200-level course (which can also be counted toward their 14-unit major requirements) or through another approved activity as early as the summer before their senior year or AFTER the student has successfully completed at least 10 courses toward their data science major, including at least one course at the 200-level and at least two core data science courses beyond DSci 122 and DSci 105/125.
The following 200-level courses qualify for data science capstones:
Other advanced courses may be taken for data science capstones with permission of the data science program.
Students who wish to take a 200-level course to satisfy their capstone requirement must arrange to do so with the instructor within the first two weeks of the course. The course instructor will serve as the capstone advisor and certify the completion of the capstone requirement.
Alternative capstone experience may include any of the following, if the experience includes a significant data science component and is approved by the Data Science program.
- Faculty-Guided Research
- Self-Designed Project
- Academic Internship
- Suitable Courses during Study Abroad
- Practices for and participations in data science/analytics contests and hackathons
Data Science Faculty
Li Han, Ph.D., Director
Amir Aazami, Ph.D.
Charles Agosta, Ph.D.
Kenneth Basye, Ph.D.
Michael Boyer, Ph.D.
Mary-Ellen Boyle, Ph.D.
Paul Cotnoir, Ph.D.
Hamidreza Ahady Dolatsara, Ph.D.
Tim Downs, Ph.D.
Elliot Epstein, Ph.D.
Lyndon Estes, Ph.D.
Jackie Geoghegan, Ph.D.
Frederic Green, Ph.D.
Yelena Ogneva-Himmelberger, Ph.D.
Gary Holness, Ph.D.
Minji Jung, Ph.D.
Aghil Alaee Khangha, Ph.D.
Arshad Kudrolli, Ph.D.
Ali Maalaoui, Ph.D.
John Magee, Ph.D.
Ranjan Mukhopadhyay, Ph.D
Gideon Maschler, Ph.D.
Tom Murphy, Ph.D.
Shuo Niu, Ph.D.
Olufemi Odegbile, Ph.D.
Alexander Petroff, Ph.D.
Gil Pontius. Ph.D.
Robert Ream, Ph.D.
Javier Tabima Restrepo, Ph.D.
John Rogan, Ph.D.
Morgan Ruelle, Ph.D.
Florencia Sangermano, Ph.D.
Barbara Capogrosso Sansone, Ph.D.
Michael Satz, M.S.
Inshik Seol, Ph.D.
Peter Story, Ph.D.
Natalia Sternberg, Ph.D.
Andrew Stewart, Ph.D.
Zhengyang Tang, Ph.D.
Terrasa Ulm, Ph.D.
Catalin Veghes, M.S.
Edouard Wemy, Ph.D.
Christopher Williams, Ph.D.
Jing Zhang, Ph.D.
Junfu Zhang, Ph.D.
Foundation and Core Data Science Courses
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