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.

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Analytics concentration students must take MGG 633, MGS 613, MGS 616 and 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 2019 | Spring 2020


MGF 694LEC Financial Modeling Using "R"

This course utilizes "R," instead of Excel, as the computational tool. Students will learn how to download and process public data associated with economics, finance and accounting. Students will also learn how to apply "R" to various finance theories.

Credits: 3.00
Semesters offered: Fall 2019
Pre-Requisite: Two graduate courses, preferably Co


Fall 2019 (08/26/2019 - 12/06/2019)

Reg. Num. Section Type Topic Days Time Location Instructor
23258 F1F LEC M 5 - 7:50 p.m. Jacobs 214 Yan, Yuxing

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 2019
Co-Requisite: MGF 633.


MGM 653LEC Digital Marketing Analytics

The course focuses on various digital marketing topics, including website content configuration, search engine and conversion rate optimization, website metrics/KPI analyses, A/B testing, etc. Various online advertising or SEM models will also be explored such as PPC, CPM, sponsored search/Google AdWords, Display Ads/Google Display Network. In addition, specific social media platforms/metrics and mobile media platforms/metrics will be covered. Deliverables consist of a suite of Google Analytics assignments using real-time consumer data, several cases (data intensive), and a final exam.

Credits: 3.00
Semesters offered: Spring 2020


MGM 675LEC Marketing Analytics for Data Driven Decision Making

This course will prepare future marketing professionals to make well-informed, data-driven marketing decisions. While the course will explore basic foundational principles and techniques in marketing analytics, focus will also be placed on developing an awareness of the types of data that are available to today?s marketers and how they can best utilize it to gain a competitive advantage. Understanding the art and science of applying marketing data, the questions to ask when interacting with analysts, and ultimately, interpreting and telling the complete story behind the data are all important themes that will be explored throughout this course. Suggested prerequisite is MGM 615 or equivalent introductory marketing course.

Credits: 3.00
Semesters offered: Spring 2020


MGO 619LEC Business Forecasting

This course is devoted to delivering, constructing, and implementing business forecasting analytical models and systems (with data) that are capable of enhancing decision making and providing decision support for managerial and policy decisions at the firm, industry, and country levels. It is emphasized that applications of predictive methodology and empirical analysis are an integral part of the course. Both methods and results provide solutions to problems and insights to decision makings.

Credits: 3.00
Semesters offered: Spring 2020


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

Reg. Num. Section Type Topic Days Time Location Instructor
21181 S1O LEC MW 11 a.m. - 12:20 p.m. Clemen 103 Lin, Winston T

MGO 636LEC Supply Chain Analytics

This course focuses on design, modeling and optimization of supply chain networks. Topics covered include: global supply chain strategy formulation, performance metrics, new forecasting models applicable for supply chain contexts, newsvendor models for capacity and aggregate planning, models for location and design of supply and distribution entities, inter-organizational planning, advanced planning systems, multi-echelon inventory analysis, distribution requirements planning (DRP) systems, joint transportation-inventory models, and pricing and revenue management techniques.

Credits: 3.00
Semesters offered: Fall 2019


Fall 2019 (08/26/2019 - 12/06/2019)

Reg. Num. Section Type Topic Days Time Location Instructor
19461 PM1 LEC M 6:30 - 9:10 p.m. Alfier 104 Wang, Xiaoqiang
12108 F10 LEC M 6:30 - 9:10 p.m. Alfier 104 Wang, Xiaoqiang

MGS 670LEC Health Care Analytics

This course is designed to help students apply core analytics capabilities to the challenging world of health care. It will be split in two separate sessions; the first half being held in the classroom, and the second half dealing with real-world problem solving. Building on core coursework in statistics and predictive modeling, the first half of the class will study the nuances of the health care industry, beginning with the move to value based reimbursement - the foundation upon which the health care analytics movement is based. The second half of the course will utilize the toolbox to analyze payer, provider, patient and social determinant databases and propose 3-4 different opportunities, mutually agreed upon by the corporate partners. The students will be split into project teams, each dealing with a different opportunity.

Credits: 3.00
Semesters offered: Fall 2019
Pre-Requisite: MGG 633 OR MGS 616 must be taken as


Fall 2019 (08/26/2019 - 12/06/2019)

Reg. Num. Section Type Topic Days Time Location Instructor
24594 F1S LEC TR 11 a.m. - 12:20 p.m. Baldy 123 Zielinski, Lawrence J

MGS 657LEC Online Analytical Processing: Data Warehousing

In large organizations data invariably resides in multiple-platforms and in multiple formats. Therefore, it becomes essential to pre-process the data at appropriate levels of normalization for meaningful analysis. Extraction, transformation and loading data into structures such as data marts and data warehouses are essential steps to predictive data modeling and mining. Data mining begins with a basic understanding of where the data is stored and how it can be assembled for analytical purposes. This course provides an introduction to data warehouse design, data cleansing, exploration and visualization.

Credits: 3.00
Semesters offered: Fall 2019
Pre-Requisite or Co-Requisite: MGS 613 (Database M


Fall 2019 (08/26/2019 - 12/06/2019)

Reg. Num. Section Type Topic Days Time Location Instructor
24505 F2S LEC MW 11 a.m. - 12:20 p.m. Norton 218 Gaia, Joana
12297 F1S LEC MW 8 - 9:20 a.m. Alfier 104 Gaia, Joana

MGS 653LEC Social Network Analytics

The aim of the course is to provide students with an overview of measures, models and methods of analysis that can be used to study social networks to further business interests within organizations using data from internal and external IT data sources. The focus of the course will be on modeling methods and IT tools to analyze large volumes of data for predictive and descriptive analysis. Students will also learn the use of standard statistical software packages such as SAS and special network analysis software.

Credits: 3.00
Semesters offered: Spring 2020


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

Reg. Num. Section Type Topic Days Time Location Instructor
16681 S1S LEC MW 11 a.m. - 12:20 p.m. Jacobs 110 Smith, Sanjukta Das

 • 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

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