Analytics Concentration

In today’s business environment, companies are awash in a sea of data. But how do you make sense of it?

Analyzing data using modern technology, sophisticated statistical tools and good old-fashioned storytelling helps firms make smart business decisions. The MBA analytics concentration provides foundational quantitative and analytical skills paired with application in specific management areas.

  • Gain in-depth exposure to essential knowledge that will allow you to model and solve complex business problems
  • Learn the importance of data stewardship through data management methodologies
  • Model the managerial decision-making process through advanced Excel techniques
  • Acquire career-defining statistical skills using industry-leading applications, specifically in SAS and R
  • Apply your knowledge in several functional areas like marketing, finance, health care, supply chain and logistics, or information systems management
  • Understand how marketing analytics and consumer behavior data can be used in data-driven decision-making
  • Develop the skills needed to perform risk analysis and strategic investment management
  • Understand how analytics solve the problems of a complicated health care delivery system
  • Become a technology data analytics leader by learning database management and decision support systems

Loading...

MBA Analytics concentration students must take two of the following data analytics electives:

MGF 637LEC Financial Modeling

In this course, students will use financial econometric models to analyze problems of model specification, estimation, analysis and forecasting commonly faced by analysts in financial markets. The course materials cover the measurement and estimation of asset returns, earnings, macroeconomic data, risk and related applications in financial data analysis and visualization. Topics include regression analysis of time series/ARIMA models, multiple regression specifications and models of asset volatility including ARCH and GARCH. Throughout the course, students will use the statistical functions of the R programming language to analyze, model and forecast a variety of financial data.

Credits: 3.00
Semesters offered: Fall 2018 | Spring 2018


MGF 696LEC Portfolio Theory and Strategy

The course covers sophisticated approaches to investing and it has an introduction and three main parts. The introduction covers measures of performance and risk and methods to calculate them in closed form or from historical data. The first part of the course covers investment strategies across several asset classes, from traditional ones such as value or growth investing to strategies employed by hedge funds, such as arbitrage, option trading and other quant strategies. The second part of the course addresses portfolio construction, from assessing a utility function to the investor to constructing an optimal portfolio maximizing that utility. Investor types covered are individuals saving for retirement, speculators, university endowments or foundations, or pension funds. Finally, the last part of the course addresses the topic of risk measurement and management, with an emphasis of risks faced by decentralized organizations, such as funds of funds, foundations, or pension funds.

Credits: 3.00
Semesters offered: Fall 2018
Co-Requisite: MGF 633


MGM 675LEC Marketing Analytics for Data Driven Decision Making

Marketing Analytics will focus on how to use data analytics to analyze marketing problems for better managerial outcomes with a specific focus on quantitative techniques and hands-on learning utilizing software such as SAS and R. Today, companies are inundated with data as the cost of gathering, storing, and processing data is becoming more economical. The above is especially true for marketing data. However, knowing how to interpret marketing data is challenging. Firms are increasingly using data driven analytics to inform their decision making. The objective of this course is to equip students with skills to analyze data (especially marketing data) so as to enable superior decisions.

Credits: 3.00
Semesters offered:


MGS 613LEC Database Mgmt Systems

This course is designed to provide students with a basic understanding of database management systems (DBMS) and the skills needed to design and implement a relational database. Students will be introduced to data modeling concepts, modeling tools, the process of transforming conceptual models into relational database designs, and finally the steps needed to implement those designs. Emphasis is placed on Entity-Relationship diagramming, data normalization, database administration, and data definition, data manipulation and query development using Structured Query Language (SQL). Other topics covered include: object-oriented databases, database security and integrity, web/database integration, application development in a Client/Server environment, distributed databases, data warehousing, data mining and knowledge management via the Internet to support electronic commerce. Readings, lectures, interactive case assignments and a database design project reinforce the role of DBMS in supporting organizational systems, transaction processing and decision support applications.

Credits: 3.00
Semesters offered: Fall 2018


MGS 616LEC Predictive Analytics

This course teaches the technical and managerial skills needed in developing predictive analytics applications which are used by customer-centric corporations - retail, financial, communication, and marketing groups - to help make decisions involving complex systems. The course concentrates on a set of well-known predictive analytics methods to support business decision making. Topics such as association rule mining, decision trees, neural networks, regression analysis and cluster analysis are covered in great depth. Extensive hands-on experience using software such as SAS Enterprise Miner is provided.

Credits: 3.00
Semesters offered: Fall 2018 | Spring 2018


 • STA 503 Linear Regression (SAS)
 • Other graduate course(s) may be approved by the chair of the MBA/MS Committee as an elective.

Note: Not all electives above are offered every semester