Data Science in Business Concentration

Print Degree Planner (opens a new window) |

Dr. Yusen Xia, Program Director

The mission of the MS in Data Science and Analytics with the Data Science in Business concentration is to educate students how to acquire, organize and model data sets in order to formulate the questions that guide decision-making in corporate and non-corporate settings. Graduates are ideal candidates for the range of technical data-driven positions in a variety of industries/businesses currently emerging such as data scientist, quantitative marketing analyst, credit risk analyst, predictive modeler, health informatics, web analytics. Please see program website for more details.

Regulations for the Degree

All Data Science in Business students will complete the Business Communication Skills Requirement explained earlier in this chapter. The regulations, policies, and procedures given in the “Master’s Enrollment” and “Master’s Programs” sections of this chapter apply to M.S. in Data Science and Analytics with a concentration in Data Science in Business program. A maximum of 6 semester hours of transfer credit is possible in this 30-hour program.

Foundation Requirements

These courses are in addition to the 30 hours required for the M.S. in Data Science and Analytics with a concentration in Data Science in Business. They are assigned as part of the admissions process based on a review of each student’s transcripts. They can be exempted if equivalent work has been previously completed with grades of C or higher, or as deemed suitable by the MSA Admission Committee.

Accounting

financial and managerial accounting principles

MBA 8025 - Financial Statement Analysis 3 Credit Hours
or
ACCT 2101 and
ACCT 2102

Behavioral Science

Management principles

MBA 8165 - Leadership and Organizational Behavior 3 Credit Hours
or
MGS 3400

May Satisfy this Requirement

Students may satisfy this requirement with either

MGS 3400 - Management Principles
SOCI 1101 - Sociology Principles
or
PSYC 1101 - Psychology Principles

Economics

Both

ECON 2105 - Macroeconomic Principles
ECON 2106 - Microeconomic Principles

Mathematics

Students must have completed three semesters of advanced calculus (MATH 2211, MATH 2212, and MATH 2215). Students who have only completed one semester of calculus could be conditionally admitted and get full admission after taking MSA 8005 Mathematical Foundations for Analytics.

MSA 8005 - Mathematical Foundations for Analytics 1 to 3 Credit Hours

Statistics

Students must have ECON 8710 or completed two semesters of undergraduate mathematical statistics (MATH 4751, MATH 4752).

ECON 8710 - Introduction to Statistical Foundations for Econometrics 3 Credit Hours

Degree Requirements

Course Requirements

Required Courses (21 Hours)

MSA 8020 - Data Visualization 1.5 Credit Hours
MSA 8040 - Data Management for Analytics 3 Credit Hours
MSA 8050 - Scalable Data Analytics 3 Credit Hours
or
IFI 8450 - Scalable Data Analytics for Business 3 Credit Hours
MSA 8010 - Data Programming for Analytic 3 Credit Hours
or
IFI 8410 - Introduction to Programming and Predictive Analytics for Business 3 Credit Hours
MSA 8150 - Machine Learning for Analytic 3 Credit Hours
or
IFI 8420 - Machine Learning and Deep Learning for Business 3 Credit Hours
MSA 8190 - Statistical Foundations for Analytics 3 Credit Hours
or
IFI 8110 - Business Statistics for Analytics 3 Credit Hours
MSA 8200 - Econometric Predictive Analytics 3 Credit Hours
MSA 8600 - Deep learning analytics 1.5 Credit Hours

Elective Courses (9 Hours)

Students will select at least two elective courses. These courses will either be in an application area of interest or the student can deepen their technical skill development. The faculty adviser must individually approve electives. Examples of possible electives include the following:

CIS 8020 - Systems Integration 3 Hours
CIS 8100 - Management of Information Services 3 Credit Hours
CIS 8200 - Information Systems Strategy 3 Hours
CIS 8401 - Mobile Applications Development 3 Hours
ECON 8780 - Financial Econometrics 3 Credit Hours
FI 8000 - Valuation of Financial Assets 3 Credit Hours
FI 8200 - Derivative Markets I 3 Credit Hours
FI 8260 - Hedge Funds And Their Trading Strategies 3 Credit Hours
FI 8320 - Corporate Financial Strategy for Innovation 3 Credit Hours
FI 8460 - The FinTech Ecosystem 3 Credit Hours
HA 8160 - Introduction to the Health Care System 3 Credit Hours
HA 8550 - Healthcare Planning and Financial Management 3 Credit Hours
HA 8620 - Operations Management and Quality in Health Care 3 Credit Hours
HA 8670 - Health Information Systems 3 Credit Hours
HA 8750 - Predictive Analytics in Health Care 3 Credit Hours
MK 8010 - Marketing Metrics 3 Credit Hours
MK 8705 - Social Media Intelligence 3 Credit Hours
QRAM 8610 - Financial Engineering 3 Credit Hours
QRAM 8620 - Quantitative Financial Risk Models 3 Hours
MSA 8500 - Image Analytics for Operations 3 Credit Hours
MSA 8650 - Advanced Deep Learning with Business Applications 3 Credit Hours
MSA 8770 - Text Analytics 3 Credit Hours
MGT 8110 - Applied Regression Analysis 3 Credit Hours
MGT 8730 - Project Management 3 Credit Hours
MGT 8740 - Supply Chain Strategy 3 Credit Hours
RMI 8050 - Risk Management Modeling 3 Credit Hours
RMI 8300 - Predictive Risk Modeling 3 Credit Hours

Program of Study

Any changes in the program must be approved by the faculty adviser and a copy of the changes sent to the GRSS.

Time Limit

The time limit for completing the M.S. in Data Science and Analytics program is five years from the first semester a course in section a, or b (above) is taken.