Nov 22, 2024

2023-2024 UMass Dartmouth Undergraduate Catalog

2023-2024 UMass Dartmouth Undergraduate Catalog [Archived Catalog]

Data Science

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BS Degree

The purpose of the program is to prepare students for technology-based careers in fields that require computer programming, data analysis, visualization, and a flexible, broad understanding of informatics. It is intended to appeal to students who want to learn technological and analysis tools used by today’s science, engineering, business, and government organizations.

The program is designed to provide students with first-rate skills and knowledge in computer science, mathematics, statistics, and a relevant substantive field of study with databases of exceedingly large size, so that students can learn statistical modeling and computer-based operations to index, store, extract, analyze, display, and interpret from those computerized databases. The growth in size of databases and the need to be able to “analyze and mine” them is one of the chief challenges for knowledge development and discovery in the 21^st century.

Data Science is an interdisciplinary area that draws upon the traditionally distinct areas of computer science, applied mathematics and statistics, and applications from natural and social sciences, engineering, and business. Graduates from the BS program in Data Science will acquire the skills necessary to manage and analyze massive data sets. A Body of Knowledge for the subject is presented below:

Statistics

Exploratory data analysis
Stratified sampling
Regression, linear models
Goodness of fit of statistical models
Analysis of variance
Design of experiments
Digital signal processing

Machine Learning (quantitative analysis)

General Programming Ability

Java
Python and pandas
R
MATLAB, Maple, Mathematica
MapReduce/Hadoop
High Performance Computing (e.g. via MATLAB)
Databases: MySQL

Data Communication

Data visualization
Web programming: HTML5, CSS, PHP, JavaScript

Calculation of the GPA in the Data Science Major

Students must have a minimum 2.000 cumulative grade point average (GPA) for all courses taken at the University in order to graduate. Students must also have a minimum 2.000 cumulative GPA in the major. For purposes of this computation:

All required courses with a CIS, DSC, and MTH prefix count in calculating the GPA for the Data Science major.
If a course is repeated, only the most recent course grade (whether higher or lower) shall be used to calculate the major cumulative GPA. Repeated courses are subject to the University’s course repeat policy.

Requirements

Students must maintain a minimum grade of ‘C’ in each CIS course and a minimum grade of ‘C-’ in each MTH course leading to the DSC degree.

First Year

First Semester - 17 credits

University Studies Requirement Credits: 3 (see Footnote 1 below)

CIS 180 - Object-Oriented Prog I Credits: 4
DSC 101 - Introduction to Data Science Credits: 3
ENL 101 - Critical Writing and Reading I Credits: 3
MTH 151 - Calculus I Credits: 4
(or MTH 153)

Second Semester: 14 credits

University Studies Requirement Credits: 3 (see Footnote 1 below)

CIS 181 - Object-Oriented Prog II Credits: 4
ENL 102 - Critical Writing and Reading II Credits: 3
MTH 152 - Calculus II Credits: 4
(or MTH 154)

Second Year

First Semester - 16 credits

Free Elective Credits: 3
Laboratory Science Requirement Credits: 4 (see Footnote 2 below)

DSC 201 - Data Analysis and Visualization Credits: 3
MTH 181 - Discrete Mathematics I Credits: 3
MTH 231 - Elementary Statistics I: Exploratory Data Analysis Credits: 3

Second Semester - 14 credits

Laboratory Science Requirement Credits: 4 (see Footnote 2 below)

CIS 280 - Software Spec & Design Credits: 4
MTH 221 - Linear Algebra Credits: 3
MTH 280 - Introduction to Scientific Computation Credits: 3

Third Year

First Semester - 16 credits

Science/Quantitative Elective Credits: 3 (see Footnote 3 below)

CIS 360 - Algorithms & Data Struct Credits: 4
CIS 381 - Social&Ethcl Aspect Comp Credits: 3
ENL 266 - Technical Communication Credits: 3
MTH 331 - Probability Credits: 3

Spring Semester - 15 credits

University Studies Requirement Credits: 3 (see Footnote 1 below)
Free Elective Credits: 3

CIS 490 - Machine Learning Credits: 3
DSC 301 - Matrix Methods for Data Analysis Credits: 3
MTH 332 - Mathematical Statistics Credits: 3

Fourth Year

First Semester - 15 credits

Free Elective Credits: 6 (see Footnote 4 below)
Technical Elective Credits: 3 (see below)

DSC 498 - Data Science Capstone Project I Credits: 3
CIS 430 - Data Mining&Knowl Discov Credits: 3
or
CIS 452 - Database Systems Credits: 3

Second Semester - 14 credits

Free Elective Credits: 3
Technical Elective Credits: 6 (see below)
University Studies Requirement Credits: 3 (see Footnote 1 below)

DSC 499 - Data Science Capstone Project II Credits: 2

Technical Electives

Must be taken from approved list of courses. Students should speak with their advisor before selecting technical electives. Note: CIS 430 or CIS 452 can count as a Technical Elective only if the course is not used to meet the Core Requirements above.

AXD 363 - Virtual Reality Design Credits: 3
AXD 446 - Advanced Topics in Animation + Game Arts Credits: 3
Topic for AXD 446 must be an advanced topic relating to AXD 363.
CIS 322 - Data Structures and Fundamental Algorithms Credits: 3
CIS 323 - Fundamentals of Computer Systems Credits: 3
CIS 340 - Large Software Systems Credits: 3
CIS 361 - Models of Computation Credits: 3
CIS 362 - Empirical Meth Comp Sci Credits: 3
CIS 370 - Design Of Operating Syst Credits: 4
CIS 410 - Program Language Design Credits: 3
CIS 412 - Artificial Intelligence Credits: 3
CIS 422 - Des Parallel Algorithms Credits: 4
CIS 430 - Data Mining&Knowl Discov Credits: 3
(can only be used if not being used for the CIS core requirement)
CIS 431 - Human-Computer Interactn Credits: 3
CIS 433 - Mobile Application Development with Android Credits: 3
CIS 434 - Mobile Application Development with iOS Credits: 3
CIS 440 - Software Process and Project Management Credits: 3
CIS 442 - Digital Forensics Credits: 3
CIS 443 - Applied Cryptography Credits: 3
CIS 444 - Cyber Defense and Operations Credits: 3
CIS 446 - Secure Software Development Credits: 3
CIS 447 - Network Security & Data Assurance Credits: 3
CIS 452 - Database Systems Credits: 3
(can only be used if not being used to meet the CIS Core requirement)
CIS 454 - Computer Graphics Credits: 3
CIS 455 - Bioinformatics Credits: 3
CIS 461 - Formal Methods for Software Engineering Credits: 3
CIS 463 - Game Engine Design Credits: 3
CIS 464 - Computer Game Design Credits: 3
CIS 465 - Topics Computer Vision Credits: 3
CIS 466 - Introduction to Mobile Robotics Credits: 3
CIS 467 - Image Analysis & Processing Credits: 3
CIS 468 - Data Visualization Credits: 3
CIS 469 - Software Development for the Web Credits: 3
CIS 471 - Compiler Design Credits: 3
CIS 475 - Computer Networks Credits: 3
CIS 476 - Network Programming Credits: 3
CIS 477 - Computer and Information System Security Credits: 3
CIS 481 - Paral&Distr Sftware Syst Credits: 3
DSC 468 - Data Visualization Credits: 3
EGR 411 - Intro to Geographic Info Systems Credits: 3.00
MIS 315 - Information Systems Credits: 3
MTH 302 - Theory of Numbers Credits: 3
MTH 311 - Advanced Calculus I Credits: 3
MTH 312 - Advanced Calculus II Credits: 3
MTH 333 - Statistical Computing Credits: 3.00
MTH 350 - Applied Discrete Math Credits: 3
MTH 353 - Applied Linear Algebra Credits: 3
MTH 361 - Numerical Analysis I Credits: 3
MTH 362 - Numerical Analysis II Credits: 3
MTH 381 - Combinatorial Theory Credits: 3
MTH 420 - High Performance Scientific Computing Credits: 3
MTH 421 - Complex Analysis Credits: 3
MTH 431 - Applied Statistical Investigation Credits: 3.00
MTH 432 - Applied Statistical Consulting Credits: 3
MTH 440 - Mathematical & Computational Consulting Credits: 3
MTH 441 - Modern Algebra I Credits: 3
MTH 451 - Differential Geometry Credits: 3
MTH 461 - Elementary Topology Credits: 3
MTH 461 - Small-World Networks Credits: 3
MTH 463 - Math Modeling Credits: 3
MTH 465 - Small World Networks Credits: 3
MTH 471 - Partial Differential Equation Credits: 3
MTH 472 - Numerical Methods for Partial Differential Equations Credits: 3
MTH 473 - Numerical Linear Algebra Credits: 3
MTH 474 - Numerical Optimization Credits: 3
MTH 475 - Advanced Numerical Methods for PDEs Credits: 3
PHL 319 - Information & Technology Ethics Credits: 3

Total Credits: 120

Footnotes

[1] See University Studies requirements, Clusters 3 and 4.

[2] Complete one of the following sequences in Laboratory Science:

BIO 121/131 and BIO 122/132
CHM 151/161 and CHM 152/162
PHY 113 and PHY 114

[3] Science electives are any course in BIO, CHM, MAR, MLS or PHY. Students should choose a course that has also been approved to meet the University Studies Cluster 2A requirement if Chemistry has been completed for the laboratory science requirement.

[4] Up to three credits of EGR 490, Engineering Internship, can be counted as a Free Elective. EGR 490 must be approved by the Faculty Sponsor, Program Chair, and Associate Dean prior to the start of the internship.

University Studies Requirements for Data Science

Degree candidates for the BS degree in Data Science must satisfy the University Studies requirements as described in the Academic Policies section of this catalogue.

University Studies requirements in Clusters 1, 2B, and 5 are automatically satisfied by the courses shown as required for this major.
The University Studies requirements in Clusters 2A, 3, and 4 may be fulfilled by selecting courses from the approved lists in each cluster, using the University Studies electives shown in the course requirement tables for each major.

University Studies Departmental Requirements

Students majoring in Data Science will meet their departmentally determined University Studies requirements as follows:

Math Placement

Students not starting in Calculus (MTH 153) will be placed in Pre-Calculus (MTH 150), Pre-Calculus Enhanced (MTH 150E), or Introductory & Intermediate Algebra (MTH 100) in the first semester. This delays the start of Calculus a semester or more, which may then extend the program beyond four years.

Data Science

BS Degree

Calculation of the GPA in the Data Science Major

Requirements

First Year

First Semester - 17 credits

Second Semester: 14 credits

Second Year

First Semester - 16 credits

Second Semester - 14 credits

Third Year

First Semester - 16 credits

Spring Semester - 15 credits

Fourth Year

First Semester - 15 credits

Second Semester - 14 credits

Technical Electives

Total Credits: 120

Footnotes

University Studies Requirements for Data Science

University Studies Departmental Requirements

Cluster 1

Cluster 2

Cluster 5

Math Placement