Management Analytics

Program Overview

Management analytics involves developing a precise understanding of the factors influencing managerial decisions, and designing data and analytical solutions to support these decisions.

It encompasses the multiple skills needed to extract insights from real-world business data, including:

  • Acquiring a deep understanding of the managerial problem.
  • Identifying the data sources and create analytical data sets.
  • Designing, validating, and implementing analytical solutions.
  • Communicating your results effectively.

During the program, the Management Analytics practicum will provide students with an opportunity to gain in-depth experience through a real-life managerial problem.

Quick Facts

Domestic International
Application payment deadline MMA:

Round one: 19-Nov-2019

Round two: 4-Feb-2020


Round one: 19-Nov-2019

Round two: 4-Feb-2020

Supporting documents deadline MMA:




Minimum admission average MMA:

B in final year of bachelor’s


B in final year of bachelor’s

Program length (full-time only) MMA:

2 sessions


2 sessions

​Master of Management Analytics

Program Description

The professional Master of Management Analytics (MMA) degree program offers a curriculum that combines analytical depth with a focus on business issues and applications. Analytical depth is provided by courses on acquisition and structuring of data, predictive and prescriptive analytics, machine learning and big data methods, AI and deep learning, decision analysis, and simulation modelling. Courses applying analytics to business feature the use of analytics in marketing, operations, supply chain management, accounting, and finance. Students are exposed to real-life application of management analytics through the analytics practicum.

The MMA degree program is offered over two sessions using a cohort-based model. Students must complete a structured sequence of 13 half-course equivalents (6.5 full-course equivalents [FCEs]) on a full‐time basis. The MMA is designed for pre-experience graduates.

​Minimum Admission Requirements

  • Applicants are admitted under the General Regulations of the School of Graduate Studies. Applicants must also satisfy the Rotman School's additional admission requirements stated below.​

  • Appropriate four-year undergraduate degree or equivalent: Given the nature of the MMA program, degrees in Computer Science, Statistics, Mathematics, Engineering, Physical Science, Economics, and Commerce will be preferred, but degrees from any program where there is a significant quantitative and computational component will be considered.

  • Quantitative proficiency: Evidence of a high level of proficiency (a minimum B average) in quantitative subjects is required. Mastery of mathematics is essential including, at a minimum, calculus and linear algebra, as are courses covering probability and statistics. In cases where evidence of quantitative proficiency is not obvious, applicants must provide supplemental evidence.

  • Computational proficiency: Demonstrated proficiency in computer programming. This may be demonstrated through a minimum B average in one or more courses in computer science or in courses relying extensively on computer programming. In cases where evidence of computational proficiency is not obvious, applicants must provide supplemental evidence.

  • English-language proficiency: Applicants must demonstrate the ability to communicate in English in one of the following ways:

    • An undergraduate or graduate degree from a university at which the language of instruction and examination was English.

    • Applicants whose primary language is not English and who graduated from a university where the language of instruction is not English must achieve a Test of English as a Foreign Language (TOEFL) score of at least 100. The International English Language Testing System (IELTS) may be considered in special circumstances with a minimum score of 7.0 required.

  • Two academic references.

  • Essays (written essay, video questions, and real-time written response).

  • All successful applicants are expected to demonstrate effective oral and written communication skills.

  • Demonstration of academic ability; a high Graduate Management Admission (GMAT) or Graduate Record Examination (GRE) score is not mandatory but encouraged.

  • Applicants who meet all the criteria will be assessed on the basis of their application essays, answers to the video questions, grades, and references by the admissions committee. Selected applicants will then be invited for an admission interview. The admission decision will be based on both submitted materials and interview performance.

​Program Requirements

  • Students must be on campus by early to mid-August.

  • Within this two-session program, students must complete a structured sequence of 6.5 full-course equivalents (FCEs) (13 half-course equivalents).No advanced standing will be granted for previous academic work completed or professional designations earned. Students who are unable to follow courses in their prescribed order must attain special approval from the Academic Director in order to continue in the program. The courses in the program are:

RSM 8224H Analytic Insights Using Accounting and Financial Data
RSM 8411H Structuring and Visualizing Data for Analytics
RSM 8413H
Big Data Analytics
RSM 8414H Tools for Probabilistic Models and Prescriptive Analytics
RSM 8423H Optimizing Supply Chain Management and Logistics
RSM 8431Y Analytics Colloquia
RSM 8432H0 Management Analytics Practicum
​​​RSM 8502H
Data-Based Management Decisions
​RSM 8512H
Modeling Tools for Predictive Analytics
RSM 8521H Leveraging AI and Deep Learning Tools in Marketing
​RSM 8522H
​Analytics for Marketing Strategy
RSM 8901H Analytics in Management

​Program Length

2 sessions full-time (typical registration sequence: F/W)

Time Limit

3 years full-time

0 Course that may continue over a program. The course is graded when completed.

Mariam Olafuyi

“You should come to Toronto for the food! It is quite a foodie town and I’m still exploring.”

Mariam Olafuyi
PhD Candidate, Law
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