Analytics Concentration

In today’s business environment, companies are awash in a sea of data. 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.

Be Career Ready

  • Gain in-depth knowledge to model and solve complex business problems
  • Model the managerial decision-making process through advanced Excel techniques
  • Acquire career-defining statistical skills using industry-leading applications, specifically SAS, R and Python
  • Apply marketing analytics to consumer behavior data for smarter 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
  • Learn the importance of data stewardship through data management methodologies

Analytics is a secondary concentration and should be taken with such functional concentrations as finance, marketing, health care or operations and supply chain management.

Required Courses Electives

Analytics concentration students must take MGG 633, MGS 613, MGS 616 and two analytics elective courses from those listed under the electives tab:

View/search courses and descriptions.

MGG 633LEC Model Managerial Process

Historically, managers have considered decision making as an art; something learned by trial and error; something based on creativity, judgment, intuition, and experience. This course gives you a structured way of attacking a wide range of real problems, using data-driven analysis to guide decision-making. We will consider how to think about and manage uncertainty and risk, how to translate data about the business into useful insights, how to put value on various courses of action, and how to generally make informed decisions. The main focus of the course will be on modeling decisions in the spreadsheet environment, illustrated by applications from operations, finance, marketing, and human resources. The approaches and techniques for decision-making are useful throughout the firm, both within functional areas and for the essential management challenge of working across functional boundaries.

Credits: 3.00
Semesters offered: Spring 2020

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 2020

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 2020 | Spring 2020

Fall 2020 (08/31/2020 - 12/11/2020)

Reg. Num. Section Type Topic Days Time Location Instructor
21333 F1S LEC TR 9:35 - 10:50 a.m. Nsc 201 Smith, Sanjukta Das

Spring 2020 (01/27/2020 - 05/09/2020)

Reg. Num. Section Type Topic Days Time Location Instructor
15757 S2S LEC TR 11 a.m. - 12:20 p.m. Remote Gaia, Joana
11331 S1S LEC TR 9:30 - 10:50 a.m. Remote Gaia, Joana
19188 S3S LEC MW 3:30 - 4:50 p.m. Remote Smith, Sanjukta Das

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