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

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, artificial intelligence (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 11 months using a cohort-based model. Students must complete a sequence of 14 half-course equivalents (7.0 full-course equivalents [FCEs]) on a full‐time basis. 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 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. All offers of admission will be conditional on successful completion of a qualifying examination in statistics.

• Computational proficiency: Demonstrated proficiency in Python coding. All offers of admission will be conditional on successful completion of an assessment of Python coding.

• Prerequisite knowledge in foundations of finance and financial accounting, usually demonstrated through the completion of university-level courses. Applicants who have not completed courses in one or both of these subject areas may be offered admission conditional on successful completion of one or more qualifying examinations that will demonstrate the applicant’s equivalent knowledge.

• 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 encouraged, though it is not mandatory.

• 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 three-session program, students must complete a sequence of 7.0 full-course equivalents (FCEs) (14 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.

  • 5.5 FCEs (11 half-course equivalents) are mandatory for all MMA students and are completed as a structured sequence of courses as follows:

    Course Code	Course Title
    RSM8411H	Structuring and Visualizing Data for Analytics
    RSM8413H	Machine Learning Analytics
    RSM8414H	Tools for Probabilistic Models and Prescriptive Analytics
    RSM8431Y0	Analytics Colloquia
    RSM8432H0	Management Analytics Practicum
    RSM8502H	Data-Based Management Decisions
    RSM8512H	Modeling Tools for Predictive Analytics
    RSM8521H	Leveraging AI and Deep Learning Tools in Marketing
    RSM8601H	MMA Self Development Lab (Credit/No Credit)
    RSM8901H	Analytics in Management

  • 1.5 FCEs (3 half-course equivalents) chosen from the following list. Note: not all electives are offered each year.

    Course Code	Course Title
    RSM8001H	Causal Identification for Management Analysis (prerequisites: RSM8411H, RSM8413H, RSM8414H, RSM8512H)
    RSM8002H	The Analytics of Talent Strategy (prerequisites: RSM8411H, RSM8413H, RSM8414H, RSM8512H)
    RSM8224H	Analytic Insights Using Accounting and Financial Data
    RSM8301H	Machine Learning Applications in Finance (prerequisites: RSM8411H, RSM8413H, RSM8414H, RSM8512H)
    RSM8415H	Service Analytics for Management Analysis (prerequisites: RSM8411H, RSM8413H, RSM8414H, RSM8512H)
    RSM8416H	Healthcare Analytics (prerequisites: RSM8411H, RSM8413H, RSM8414H, RSM8512H)
    RSM8423H	Optimizing Supply Chain Management and Logistics
    RSM8522H	Analytics for Marketing Strategy

Program Length

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

Time Limit

3 years full-time

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