Dec 04, 2021
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:
Develop in students a solid foundation of data-centered computing and the ability to learn on their own;
Develop in students a good understanding of the key statistics and computational concepts, principles, and techniques;
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;
Develop in students a general understanding of the social issues surrounding data science and the code of conduct in this discipline;
Develop in students an appreciation and desire for knowledge and life-long learning;
- Foster creativity, independence, discipline, responsibility, respect, and ethical behavior.
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
- 4 courses from 1 track (application domain, specialization) including at least 1 course at the 200-level
- 2 courses from the approved electives which can be from any track and the collection of core courses.
Computer Science Track:
CSCI 160 - Algorithms , CSCI 210 - Artificial Intelligence , CSCI 262 - Computer Vision , CSCI 250 - Software Engineering , CSCI 255 - Design and Analysis of Algorithms , CSCI 270 - Theory of Computation , MATH 114 - Discrete Mathematics
ECON 010 - Economics and the World Economy , ECON 011 - Principles of Economics , ECON 160 - Introduction to Statistical Analysis , ECON 265 - Econometrics , ECON 204 - Microeconomic Theory Using Calculus , ECON 224 - Applied Game Theory
GEOG 110 - Introduction to Quantitative Methods , GEOG 190 - Introduction to Geographic Information Science , GEOG 245 - Remote Sensing of the Cryosphere ,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
MGMT 100 - The Art and Science of Management , ACCT 101 - Principles of Accounting , FIN 240 - Corporate Finance , MKT 230 - Marketing Management , MKT 231 - Marketing Research , MKT 234 - Consumer Behavior , MKT 238 - Digital Marketing , 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
MATH 130 - Linear Algebra , MATH 131 - Multivariate Calculus , 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
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.
Kenneth Basye, Ph.D.
Mary-Ellen Boyle, Ph.D.
Tim Downs, 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.
Ali Maalaoui, Ph.D.
John Magee, Ph.D.
Gideon Maschler, Ph.D.
Tom Murphy, Ph.D.
Shuo Niu, Ph.D.
Olufemi Odegbile, Ph.D.
Gil Pontius. Ph.D.
Robert Ream, Ph.D.
Morgan Ruelle, Ph.D.
Michael Satz, M.S.
Inshik Seol, Ph.D.
Peter Story, Ph.D.
Natalia Sternberg, Ph.D.
Zhengyang Tang, Ph.D.
Catalin Veghas, M.S.
Edouard Wemy, Ph.D.
Jing Zhang, Ph.D.
Junfu Zhang, Ph.D.
Data Science Core Courses