Data Science

The MSDS provides a pathway for students who wish to acquire foundational skills to either enter or advance their careers within the newly emerging field of data science.

The Master of Science in Data Science is designed around hands-on application of data management and analysis methods and tools used to translate available data into decision-guiding knowledge, with particular emphasis on technical aspects of computational programming and statistical modeling. The curriculum combines an overview of the key scientific research design and statistical data analyses concepts, with some of the more widely used data management, manipulation, analysis, and visualization technologies. The overall goal of this program is to equip students with adequately deep technical knowledge to enable them to function as data scientists capable of processing, amalgamating and analyzing large volumes of complex, multisource data. This 32-credit program emphasizes practical, hands-on exercises to drive the acquisition and application of knowledge necessary to exercise professional skills and develop a foundation of data processing and statistical model building skills.

Learning Goals and Objectives

Technical Skills

• Programming - The ability to manipulate and process available data using either proprietary (e.g., SPSS) or open-source (e.g., R or Python) applications and/or languages.
• Analysis - The ability to conduct advanced analyses of data using either proprietary (e.g., SPSS) or open-source (e.g., R or Python) applications and/or languages.
• Assessment - The ability to assess the validity and reliability of data analytic outcomes.

 

Laws and Ethics

• Law - Familiarity with statutory and regulatory provisions governing business data and the usage of business data.
• Ethics - The ability to recognize an ethical issue and analyze the potential outcomes for the relevant constituencies and make a reasoned judgment.
• Governance - The ability to understand current and emerging data security, privacy, access and usage rights and obligations.

 

Application and Communication

• Selection - The ability to evaluate the available techniques and tools, and choose those best suited to the problem at hand.
• Scoring - The ability to deploy and productize final data modeling solutions.

 

Admission Requirements

The MSDS program entrance requirements are as follows:

• Undergraduate degree, with a minimum of 3.0 cumulative GPA
• Earning grade B or higher on undergraduate quantitative courses (e.g., Statistics, Math, etc.)
• Two (2) plus years of verifiable professional work experience recommended
• Statement of interest

 

Program Requirements

The Master of Science in Data Science is comprised of 8 four-credit courses for a total of 32 credit hours.

 

Foundational Courses (20 credits)

DSE5011G	Foundations of Data Management	4
DSE5021G	Foundations of Statistical Analysis	4
DSE5113G	Data Exploration	4
DSE5213G	Data Visualization	4
DSE5315G	Data Governance, Laws and Ethics	4

 

Advanced Courses (12 credits)

DSE6111G	Predictive Modeling	4
DSE6211G	Machine Learning	4
DSE6311G	Capstone: Multisource Analytics	4