Master of Science - Data Science

Completion of the Master of Science in Data Science will help students identify and formulate key business challenges, collect and manage data from a variety of sources, select effective analytical techniques to identify solutions, and communicate results in a clear and creative way, harnessing the power of data in decision making. Data scientists are in high demand in a wide range of industries, including information technology, finance, healthcare, insurance, energy, transportation, manufacturing, telecommunications and more.

Program Learning Outcomes

Upon completion of the for the Master of Science in Data Science, graduates will have the ability to:

Master contemporary data science methods, tools, and best practices.
Formulate and propose data science solutions for complex real-world problems across different domains.
Disseminate data science findings through various formats, including visual, oral, and written, to diverse audiences in an effective manner.

**Course offering frequency subject to change**

Degree Requirements (30)

A minimum of thirty (30) hours is required for a master’s degree. Fifteen (15) hours must be core. If a student is on the thesis pathway, they will complete nine (9) hours of electives and six (6) hours of thesis courses. Non-thesis students will complete total of fifteen (15) hours of elective.

Core (15)

Complete all of the following.

COP 5090 - Scientific Computation and Programming Credits: 3
CAP 5771 - Data Mining & Text Mining Credits: 3
CAP 5765 - Computational Data Analysis Credits: 3
CAP 5320 - Data Wrangling and Exploratory Data Analysis Credits: 3
ESI 5315 - Optimization and Simulation Credits: 3

Electives (9-15)

Earn fifteen (15) hours from following if pursuing the course-only pathway. Earn nine (9) hours from the following if pursuing the thesis pathway. Note: maximum two courses from 4000 level are eligible for electives by departmental approval.

CAP 5634 - Advanced Artificial Intelligence Credits: 3
CEN 5728 - Advanced Human-Computer Interaction Credits: 3
CAP 5610 - Machine Learning Theory and Practice Credits: 3
CAP 5775 - Data Warehousing Credits: 3
COP 5727 - Advanced Database Systems Design Credits: 3
EGS 5626 - Engineering Project Management Credits: 3
CAP 4613 - Applied Deep Learning Credits: 3
CAP 4786 - Topics in Big Data Analytics Credits: 3
STA 4853 - Time Series Analysis for Business, Data Science, and Economics Credits: 3
CAP 5735 - Data Visualization and Reproducible Research Credits: 3
ECO 4422 - Econometrics: Causal Inference, Panel and Survey Data Credits:

Thesis (6)

Earn six (6) hours if pursuing the thesis pathway.

IDS 5970 - Thesis 1 Credits: 3
AND
IDS 5975 - Thesis 2 Credits: 3
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EGN 5911 - Research (0-3)

Research credits (EGN 5911) may be used in situations where thesis completion takes longer than expected, in addition to the minimum required thesis hours above. Program extensions are subject to departmental approval, and do not extend Graduate Assistantship appointments. Extensions may not exceed two semesters (excluding summers) after successful completion of Thesis 2.

Plan of Study: Course-Only Pathway

Fall 1

COP 5090 - Scientific Computation and Programming Credits: 3
CAP 5771 - Data Mining & Text Mining Credits: 3
CAP 5765 - Computational Data Analysis Credits: 3
Elective

Spring 1

CAP 5320 - Data Wrangling and Exploratory Data Analysis Credits: 3
ESI 5315 - Optimization and Simulation Credits: 3
Elective
Elective

Summer 1

Elective
Elective

Plan of Study: Thesis Pathway

Fall 1

COP 5090 - Scientific Computation and Programming Credits: 3
CAP 5771 - Data Mining & Text Mining Credits: 3
CAP 5765 - Computational Data Analysis Credits: 3

Spring 1

CAP 5320 - Data Wrangling and Exploratory Data Analysis Credits: 3
ESI 5315 - Optimization and Simulation Credits: 3
Elective

Fall 2

IDS 5970 - Thesis 1 Credits: 3
Elective
Elective

Spring 2

IDS 5975 - Thesis 2 Credits: 3