2020-2021 Academic Catalog 
    
    Apr 11, 2021  
2020-2021 Academic Catalog

Data Science Minor


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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 minor at Clark aims 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.

Minor Requirements


A minor in Data Science consists of five to seven courses, depending on whether students take the one year sequence of the foundational mathematics and computer science courses or the one semester Honors course.

MATH COURSES:

I. Choose the 2 course sequence of: MATH 120 - Calculus I  and MATH 121 - Calculus II 
    or the 1 semester of: MATH 124 - Honors Calculus I  

II.  MATH 130 - Linear Algebra  

COMPUTER SCIENCE COURSES:

III.  Choose the 2 course sequence of: CSCI 120 - Introduction to Computing  and CSCI 121 - Data Structures 
       or the 1 semester of: CSCI 124 - Honors Introduction to Computing  

DATA SCIENCE COURSES:

IV.  DSCI 125 - Introduction to Data Science  or DSCI 205 /DSCI 305  

V.  DSCI 225 - Applied Machine Learning  

Data Science Faculty


Amir Aazami, Ph.D.

Kenneth Basye, Ph.D.

Jacqueline Dresch, Ph.D.

Frederic Green, Ph.D.

Li Han, Ph.D., Director

John Magee, Ph.D.

Gideon Maschler, Ph.D.
Shuo Niu, Ph.D.

Michael Satz, M.S., Associate Director

Natalia Sternberg, Ph.D.

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