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Dec 11, 2024
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2024-2025 Academic Catalog
Data Science Major
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Data Science Overview
Data is everywhere. Our increasingly global and digitized world produces massive amounts of data every day. Effectively managing and understanding data is essential to all organizations, whether they are in the public, private, or nonprofit arenas. Data science and analytics help organizations harness their data and use it to discover knowledge, identify opportunities, and develop solutions, ultimately leading to smarter policies, more efficient and equitable practices, better services, and more inclusive societies. Clark University’s Data Science program provides a comprehensive interdisciplinary education that seamlessly integrates core data science, computer science, and mathematics courses with specialized domain knowledge. The program is supported by many dedicated faculty from diverse partner departments and schools, including Biology, Chemistry, Computer Science, Economics, Environmental Science, Geography, Interactive Media, Management, Mathematics, Psychology, Physics, and Sustainability and Social Justice. We consider Data Science a multifaceted discipline that aligns well with the principles of liberal arts education. We emphasize critical thinking, problem-solving, creativity, communication, and team collaboration skills alongside technical proficiency; and help students develop these skills. In addition, Clark provides excellent opportunities for applying and further enhancing students’ knowledge and skills. At our program, undergraduates have opportunities to work as teaching assistants, research assistants, and participate in projects, competitions, and conferences, frequently under faculty guidance and with funding support. Clark offers a large variety of academic programs and supports students to pursue their interests through combinations of majors and minors in their chosen fields. Furthermore, our student clubs and growing data science alumni community provide enrichment activities, mentoring and networking opportunities. With our holistic curriculum, experiential learning, and supportive community, students acquire the knowledge, skills, and experiences needed to thrive in dynamic and data-driven industries, secure top-tier placements and unlock opportunities for advanced degrees. They are well prepared for making transformative changes in today’s data-rich world. For more infomation and examples of student projects see the data science program website. Major Requirements
Our data science major includes 8 units of core requirements and 6 units of electives. Each student must choose one of 11 tracks and at least four courses from the track, including at least one at the 200-level, as part of their electives. The core courses cover mathematical and computing foundations and teach general knowledge and skills broadly application to data science work in all disciplines. The tracks are designed to complement the core courses and help students develop domain knowledge and connect it to data science.
Students must earn a minimum course grade of C- in order to receive major or minor credit in data science. No course can be taken as pass/fail for the data science major or minor. Core Requirements
Core requirements in the Data Science Major include 1 year of calculus, 1 year of foundational computing courses, and 4 core data science courses. Foundational Math Courses
This one-year sequence should be completed as soon as possible, preferably in the students’ first year at Clark. Any of these courses can be used to fulfill the formal analysis requirement. Students placed into pre-calculus should take that course as soon as possible and then continue to the 1-year Calculus sequence. Core Data Science Courses
Students must take the following courses: At least one of the following 100-level Data Science Courses At least two of the following 200-level Data Science Courses DSCI 122 should be taken as soon as possible, preferably immediately after completing MATH 121 /MATH 125 - Calculus II. We highly recommend data science students to take both DSCI 105 and DSCI 125 , with one counting towards the core requirements and the other as an elective. These courses cover complementary materials that are crucial for both academic learning and career development in data science. Completing these 100-level core courses as soon as possible will allow students to take advanced courses, become competitive for on-campus job opportunities, and apply for external internships early on. We also encourage students to take all three 200-level core data science courses, with two for the core requirements and the other as an elective. Please note that DSCI 216 and DSCI 225 have DSCI 122 , DSCI 105 or DSCI 125 , and CSCI 121 as prerequisites. DSCI 215 has GIS (Geographic Information Science) and computing courses as prerequisites. Data Science majors who are interested in taking DSCI 215 but don’t have all the GIS perquisites are encouraged to contact the DSCI 215 instructor for discussion and potential registration permission. Electives
Each data science student must choose one of the following 11 tracks and declare the track as part of their data science major declaration. They must take at least 4 courses from their track, including at least one course at the 200-level. A total of 6 electives are required, and the two other electives can be from any track, general electives, and the collection of core courses 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 - Molecular and Computational Biology , 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 - Biochemistry I (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 220 - Database Management And Systems Design , 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 . 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 - Markov Chains: Theory, Application and Algorithms , 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 Learning, Language, and Cognition . 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
Directed Study
Directed Study courses on special topics may be arranged with the permission of a member of the program faculty who will serve as supervisor. Departmental policy requires that a directed study course can only be taken Pass/Fail. Directed study courses may not be substituted for program courses to fulfill major or minor requirements. Suggested Program Sequence
It is important for students to begin the data science program early in order to progress in their major and become competitive for on-campus and off-campus opportunities. Data science courses are hierarchical with strict prerequisites. Delaying any 100-level foundational and core course in our program generally means delaying the data science study by a semester or even an entire year, which can put students at a significant disadvantage. CSCI 120 - Introduction to Computing or CSCI 124 - Accelerated Introduction to Computing should be taken as soon as possible, preferably in the fall of the first year. This course can be followed by DSCI 125 - Introduction to Data Science , DSCI 105 - Applied Data Analytics , or CSCI 121 - Data Structures . It is beneficial to finish all four courses listed here during the first two years of study at Clark. The math sequence, MATH 120 /MATH 124 (Calculus I), MATH 121 /MATH 125 (Calculus II), and DSCI 122 - Mathematical Foundations of Data Science , should be completed as soon as possible, preferably in consecutive semesters. Start to take 200-level DSCI courses after you have the prerequisites and continue to take them. It is also beneficial to explore electives and track courses early on. For example, the following courses are data science electives and fulfill various CORE requirements. While only one track is required for the data science major, gaining additional depth (for example, in mathematics and computer science) and/or breadth (for instance, across two or more application domains) can greatly benefit data science professionals. Students are encouraged to explore double major and minor options. The data science program faculty are eager to assist you in determining the best path forward. Getting Data Science Advise
Students interested in Data Science are encouraged to contact the program (DS@clarku.edu) early on. The data science faculty will be happy to talk to students, discuss their interests, and help them plan their studies at Clark and connect to other students of similar interests.
A major must be declared no later than the second semester of the sophomore year; earlier declarations are encouraged. Students should choose an academic adviser from the program faculty as early as possible. Please note that students can change their advisor before their major declaration by completing a simple form. Data Science is a new, interdisciplinary program, and it is beneficial to have a program faculty to provide academic advice for your data science study. 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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