The Master of Management in Analytics (MMA) is an intensive full time, one-year, pre-experience program with a strong emphasis on experiential learning.
The MMA curriculum strikes a balance between advanced statistics, technology, and business strategy to give students a broad range of practical skills and perspectives.
The 3 pillars of MMA:
|Advanced Statistics||Technology||Business Strategy|
Coding with R/Python/SQL
MMA Program Phases:
|2021||EXPERIENTIAL LEARNING MODULE||→||→|
Transition to Market
|2 Months||3 Months||4 Months||
Build your statistical problem-solving acumen in the core. | Enhance your base by learning the latest technologies and how you can scale your skills.
In the final two terms of the MMA, explore a wide variety of management analytics application topics through complementary classes. While the core module teaches the fundamentals, the complementary module presents an opportunity to build on that foundation and explore particular areas of interest.
Sample complementary courses:
- Introduction to Artificial Intelligence
- and Deep Learning
- Enterprise Analytics using Cloud Computing
- Social Media Analytics
- Text Analytics
- Healthcare Analytics
- Epidemiology Analytics
- Pricing Analytics
- Revenue Management
- And more [...]
MMA Program Structure:
The MMA offers a 1.5-year option that includes an internship for students interested in gaining additional professional experience prior to graduation.
Explore MMA courses by Pillar or by Module
BUSA 613 Independent Studies in Analytics 2 (3 credits)
Business Admin : Procuring of real data to perform a data-centric analysis for an organization or research institution. Under the mentorship of the instructor(s), students will focus their project deliverables on all of the following domains: data management, value proposition, analytic formulation, solution development and user application.
MGSC 660 Mathematical and Statistical Foundations for Analytics (3 credits)
Management Science: This course consists of two parts: (i) The first half of the course focuses on probabilistic and statistical foundations of data analytics. At the end of this part, students will have the mathematical knowledge in following topics: probabilities, random variables, the Central Limit Theorem; prior and posterior distributions, and Bayes’ rule; correlation, and Sampling. (ii) The second half of the course focuses on mathematical foundations of decision analytics. At the end of this part, students will have the mathematical knowledge in following topics: linear algebra; calculus of several variables; convexity; separating hyperplanes; unconstrained and constrained optimization; lagrange multipliers.
Professor: Mehmet Gumus
MGSC 661 Multivariate Statistical Analysis (3 credits)
Management Science: The course will begin with the standard linear regressions, and extend to multivariable regression models, factor analysis, principal components, selection models, and dynamic and nonlinear multivariate data methods. Students will be exposed to a broad range of techniques and applications in business analytics through conducting their own statistical analyses.
Professor: Juan Serpa
MGSC 662 Decision Analytics (3 credits)
Management Science: This course teaches quantitative methods used in business decision making. Topics include: optimization models, optimization under uncertainty, and simulation. Business applications of these techniques are emphasized. Students in this course will acquire expertise in quantitative methods for decision making, through computer analysis of real-life problems.
Professor: Javad Nasiry
MGSC 670 Revenue Management (1.5 credits)
Management Science : This course will introduce students to revenue management (RM) practices in air transportation, hospitality (hotels, cruises, theme parks, casinos), car rental, media, broadcasting, natural-gas storage and transmission, electricity generation and transmission, and show business (concerts, theaters, sport events). Most applications are recent and made possible by the advances in technology, data and decision analytics. However, there are issues of legality and customer backlash for charging different prices for virtually the same product. The course will touch upon these issues as well. Topics covered include capacity allocation, network management, overbooking, markdown pricing, and customized pricing.
Professor: Maxime Cohen
MGSC 672 Operations and Supply Chain Analytics (1.5 credits)
Management Science : The course covers analytical models that explore the key issues associated with the design and management of supply chains. A considerable portion of the course is devoted to data-driven decision models that treat uncertainty explicitly. Topics include supply network design, inventory centralization, value of information, and contracts.
Professor: Saibal Ray
MGSC 673 Introduction to Artificial Intelligence and Deep Learning (1.5 credits)
Management Science : Introduction to deep learning, through its use of neura networks, to learn from data and tasks such as classification, forecasting, data generation. The basis of deep learning up to the applications of the most recent research. Use of deep learning in a production environment, and leveraging techniques such as: Keras, hyperparameter tuning, image classification methods, back propagation, LSTMs, and Autoencoders.
Professor: Nicolas Feller
MRKT 673 Pricing Analytics (1.5 credits)
Marketing : Introduction and basics of price-response functions and pricing optimization. Using data to estimate demand models. Value-based pricing, consumer valuations, personalization. Tactics of price differentiation. Pricing with constrained supply. Team project consultation.
Professor: Michelle Y. Lu
BUSA 600 Analytics Internship (3 credits)
Business Admin : An on-the-job experience in a corporation or organization supervised by an academic faculty member. The learning objectives of this course is to allow the student to put into practice elements that they have learned throughout the program, to gain formal work experience.
INSY 660 Coding Foundations for Analytics (3 credits)
Information Systems: Students will be exposed to a broad set of topics, including fundamentals of computer programming, coding for data acquisition and data manipulation, and specific operational issues related to “big data” analytics. Course material will also cover data privacy, security, and ethical issues.
Professor: Kartik Ganju
INSY 661 Database and Distributed Systems for Analytics (3 credits)
Information Systems: This course will present the student with many key concepts relating to database technology, how database technology is being used for managing large datasets, and the opportunity to put these concepts to practice. This course will cover database management system (DBMS) concepts, database architecture, database design using entity-relationship (ER) modeling, data storage, file organization, the SQL language, normalization, data integrity, database security, data warehousing, and big data related technologies such as NoSQL, Hadoop, MapReduce, Pic, and Hive.
Professor: Animesh Animesh
INSY 662 Data Mining and Visualization (3 credits)
Information Systems: The course will teach practical analytics methods and use R (a tool widely used by data analysts) to provide hands on experience on the data mining techniques covered in the class. The focus of the course is on the application of the tools and techniques rather than learning the theory and math behind the models. This course builds upon concepts of data manipulation and coding seen in INSY 660 and covers these tools and techniques in much more depth. Students will be exposed to real world datasets and examples to get hands-experience with making business decisions using dating mining and predictive analytics, and provide R code to apply the predictive models learned in class. At end of this course students will be comfortable using different data mining techniques to solve business problems on their own using R.
Professor: Warut Khern-am-nuai
IINSY 669 Text Analytics (1.5 credits)
Information Systems : Introduction to the basics of text mining and text-based predictions, including leading scripts/packages/libraries like SentiStrength (for sentiment analysis), categorization and classification of a variety of documents. The application of text analytics in solving real-world busines problems.
Professor: Changseung Yoo
INSY 670 Social Media Analytics (1.5 credits)
Information Systems : Methods and tools to leverage the power of social media, with a focus on a variety of questions ranging from strategic to operational matters pertaining to firms’ social media initiatives, metrics to capture relevant outcomes, and predictive analytics to link social media chatter to business performance.
Professor: Changseung Yoo
INSY 672 Healthcare Analytics (1.5 credits)
Information Systems : Students will get hands-on experience with real-world datasets to examine how data analytics can be used to predict and understand disease outbreaks, how analytics can be used to improve the operation of hospitals, and the manner in which analytics can be used as decision support for physicians to diagnose and treat patients. By the end of the course, students should be able to develop an appreciation on the changes that are taking place in the provision of healthcare services due to analytics and the role and opportunities for analytics to reduce cost and improve quality of healthcare in their communities.
Professor: Daniel Ding
INSY 673 Security Analytics (1.5 credits)
Information Systems : This course provides a comprehensive introduction to data analytics in the context of information security. Students will understand how to leverage data analytics to help in visualizing, detecting, and analyzing information security data. Students will be exposed to real-world datasets and tools and techniques that can be applied to analyze those data.
INSY 695 Advanced Topics in Information Systems (1.5 credits)
Information Systems : Current emerging topics in information systems. Course content will vary each term.
Professor: Fatih Nayebi
ACCT 626 Data Analytics in Accounting (1.5 credits)
Accounting: Exploration of how financial and non-financial metrics can be linked to business performance through practica application learning. Examination of mandatory and voluntary corporate disclosure, financial statement analysis, return predictability, and fraud detection.
Professor: Hongping Tan
BUSA 611 Independent Studies in Analytics 1 (1.5 credits)
Business Admin : Procuring of real data to perform a data-centric analysis for an organization or research institution. Under the mentorship of the instructor(s), students will focus their project deliverables on one of the following domains: data management, value proposition, analytic formulation, solution development or user application.
BUSA 649 Community Analytics Project (3 credits)
Business Admin : Analytics project in a small-/medium-sized organization focusing on the application of the concepts of real-world analytics problems that organizations without large budgets deal with.
Professor: Shoeb Hosain
BUSA 693 Analytics and Solution Consulting Practicum (6 credits)
Business Admin : Students will work with real companies and organizations on their capstone projects, giving them real world experience to step into the position with confidence. Real-life problems are often multi-disciplinary or multifunctional in nature. Hence, tackling them requires understanding of both management issues and analytical techniques. The topics are selected based on the specific requirements of the capstone projects and will complement and build on concepts previously covered throughout the Masters of Management in Analytics program.
Professor: Shoeb Hosain
FINE 675 Financial Valuation Analytics for Startups (1.5 credits)
Finance: Introduction to finance with an emphasis on analytics, focusing on how the merger of analytics and finance plays a first order role for startups and the decision to accelerate growth.
MRKT 671 Advanced Marketing Analytics (1.5 credits)
Marketing : The course will introduce students to advanced marketing analytic techniques available to managers and give them hands-on experience on using these with actual datasets. The major learning vehicle will be lectures with step-by-step exposition of analytical techniques with actual data. These will be then complemented with cases involving data analysis. Topics covered include customer and product analytics techniques.
MRKT 672 Internet Marketing Analytics (1.5 credits)
Marketing : What makes internet marketing different? Introduction to internet marketing - search engine optimization. Inbound marketing - search advertising and privacy concerns. Online tracking and privacy issues.
ORGB 660 Managing Data Analytics Teams (1.5 credits)
Organizational Behaviour: In this course, students will learn to: understand barriers to effective work in teams or more broadly in organizations; develop a collaboration style and learn about themselves as team members and leaders – what are their strengths, what can they improve on; learn how to jointly develop a vision and superordinate goal; develop skills in team communication, using the ‘power of framing’; build effective working relationships with diverse individuals and groups; become familiar with common organizational functional areas, their analytics perspectives, and their role in cross-functional teams.
Professor: Melissa Sonberg
ORGB 661 Ethical Leadership and Leading Change (1.5 credits)
Organizational Behaviour: Students will learn to: excel in the practice of distributed and shared leadership, critical to contemporary business; develop a collaboration style and learn about themselves as leaders – their strengths, areas for improvement; learn how to jointly develop a vision and superordinate goal; establish guidelines, protocols, and criteria for leading change and for the respectful and ethical collection, storage, and use of data derived from others; develop their capacity to lead across hierarchical levels, including ‘leading upwards’; understand how leadership is necessary for resolving ‘wicked problems’ (problems that require leadership, not management).
Professor: Melissa Sonberg
ORGB 671 Talent Analytics (1.5 credits)
Organizational Behaviour : Learning objectives: to gain experience in collecting and integrating performance and personnel outcome data; to develop and practice skills needed to analyze these types data; to cultivate knowledge and an understanding of the potential and limitations of talent analytics; to become able to apply the skills and knowledge from class to future organizational settings.
Professor: Brian Rubineau
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