Starting Fall 2020
BS in Data Science
"Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data." --- National Academies report on Data Science for Undergraduates: Opportunities and Options
The BS in Data Science degree is a multi-disciplinary program including course from mathematics, statistics, computer science and applications areas. The program offers training, at lower division level, in calculus, linear algebra, and computer science. Introduction to statistical methods, all requiring some basic programming skills (python, R, and/or MATLAB) also taught as part of the program. In the Freshman Data Science Seminar guest lecturer(s) will be introducing examples and case studies and discussing ethics and bias in Data Science.
The training at upper division level includes specialized classes in statistics, large-scale data algorithms, computer science (including algorithms, SQL and Databases), optimization, and scientific computing for various hands-on data science applications, including health sciences and business. Students will be advised on relevant elective courses that address ethics and bias in data science studies, research, and applications, along the lines of PHL 314U Computer Ethics and COMM 389U Ethics of Human Communication.
The objective of the program is to build a mathematically and statistically rigorous foundation so that each graduate is able to make informed decisions on how to operate with numerical and network data (typically discrete but often of very large size); to collect (read/write), visualize, approximate, optimize with respect to relevant cost functionals, make predictions/decisions and interpret them, all utilizing specialized software. A general goal is also to create awareness about the social implications of data bias/ethical conduct etc. when collecting, analyzing, and making decisions based on the data science methodologies that we teach.
The degree program requires a basic core of courses (61 credits) and elective courses (12-15 credits). This structure gives flexibility to the program that allows students to pursue special areas of interest in applications of data science. In addition to meeting the general University degree requirements, the major in data science must complete the following requirements.
|Course Number||Course Title||Credits|
|Mth 251, 252, 253||Calculus I, II, III||12|
|Mth 261||Introduction to Linear Algebra||4|
|Mth 231||Data Science Seminar||2|
|CS 250||Discrete Structures I||4|
|Mth 343||Applied Linear Algebra||4|
|Stat 361||Intro to Statistical Methods||4|
|Stat 363||Statistical Computing and Data Visualization in R||4|
|Mth 271 or CS 161||Mathematical Computing or Introduction to Programming and Problem-Solving||4|
|CS 350||Algorithms and Complexity||4|
|Stat 364||Modern Regression Analysis||4|
|Mth 371||Large-Scale Data Algorithms||4|
|Stat 387||Introduction to Statistical Learning||4|
|CS 486||Intro to Database Management Systems||4|
|Stat 409||Data Science Practicum||3|
|TWO Approved 400-level Mth or Stat courses*||6-7|
|TWO Approved 300- or 400-level courses**||6-8|
|Course Number||Course Title||Credits|
|PHE 350||Health and Health Systems||4|
|PHE 427||Health Informatics||4|
|CS 445||Machine Learning||4|
|CS 441||Artificial Intelligence||4|
|CS 430||Internet and Cloud Computing||4|
|ACTG 335||Accounting Information Systems||4|
|G 324,||Data Management and Analysis||5|
|ISQA 481||Blockchain Fundamentals||4|
|MGMT 442||Human Resources Information Technology & People Analytics||4|
|GSCM 412||Introduction to Enterprise Resource Planning Systems||4|
|Phl, 314U||Computer Ethics||4|
|Comm 389U||Ethics of Human Communication||4|
Check with the adviser for the list of additional courses, including omnibus-numbered courses, which may be approved as electives.