Program: Data Science Major - Clark University

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.

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 electives which can be from any track and the collection of core courses.

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:

DSCI 216 - Stochastic Computing
DSCI 225 - Applied Machine Learning
DSCI 297 Honors
DSCI 299 - Directed Study
GEOG 246 - Geospatial Analysis with R
GEOG 260 - GIS & Land Change Models
GEOG 279 - GIS & Map Comparison
GEOG 287 - New Methods in Earth Observation
MGMT 260 - Applying the Art and Science of Management (Capstone)
Any 200-level course in the computer science track
Any 200-level course in the economics track
Any 200-level course in the mathematics track

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 Major

Data Science Overview

Major Requirements

2 Mathematics Courses

2 Computer Science Courses

4 Data Science Courses

At least two 200-level Data Science Courses

Computer Science Track:

Economics Track:

Geography/GIS Track:

Management Track:

Mathematics Track:

General Electives, Not Affiliated with Any Track

Capstone:

Data Science Faculty

Data Science Core Courses