Director, Lazaridis MMA program
67% of job openings are analytics-enabled.
Canada will face a shortage of 150,000 skilled data and analytics professionals.
Business analytics experts ranked second as most difficult skills to find.
We understand that pursuing a Master's degree is a big decision. We’re here to help you work through your options to choose the program that is right for you.
We offer information sessions and one-on-one consultations to help you make the best decision. The sooner we connect with each other, the closer you are to realizing: it’s what’s inside that sets us apart.
‘Ask us Anything’ Drop-in Session: Lazaridis Graduate Programs
Information Session: Lazaridis MMA
‘Ask us Anything’ Drop-in Session: Lazaridis Graduate Programs
Lazaridis Graduate Programs Open House
Information Session: Lazaridis Double Degree MFin/MMA
Business analytics fundamentals courses:
Focus on management-level analytics courses:
Round out your experience and knowledge with electives in business fundamentals and finish your fieldwork project with your group.
Plus, choose three elective courses from the following:
Over the final two terms, you'll apply your skills and knowledge to help a business solve an analytics issue. Working with 3-4 fellow students, you'll prepare a proposal to address underlying managerial issues in the company or organization. In your proposal, you'll address the issue, formulate a well-defined problem from an analytical point of view, and propose a data-driven solution.
After you submit your proposal to your course director and industry rep, you'll gather the required data to structure your analytical solutions, and you'll design and implement the solution. The experience will culminate in your presentation of the findings to a panel of experts from academia and industry.
The MSc Management Analytics (MMA) and Master of Finance (MFin) double degree is designed to train students to be interdisciplinary specialists in finance and data analytics.
The integrated interdisciplinary curriculum is combined with experiential learning for students to gain comprehensive knowledge both in finance and data analytics as well as hands-on experience for solving real-world finance problems using sophisticated analytical tools and quantitative models.
This course introduces students to predictive analytics using techniques from machine learning and statistics. Techniques learned will include linear and logistic regression, and artificial neural networks. Students will learn to apply these tools to a range of common business problems including forecasting, classification and recommendation.
This course develops presentation and communication skills to explain business insights obtained from data. Students will become advanced users of spreadsheet based tools such as Excel and Tableau. This course covers a variety of visualization skills used to illustrate and explore different data types such as traditional tabular data, network data, text/unstructured data and geographic data.
This course provides students with a sound understanding and practice in developing and using databases for the purpose of capturing, organizing and analysing organizational data. Relational databases will be covered extensively. Topics will include understanding requirements, database modelling, normalization, design and structured query language (SQL). Principles and tools for NoSQL databases and MapReduce will also be introduced.
This course will introduce students to causal analysis for supporting business decisions. Students will learn to use retrospective data through regression analysis as well as to design experiments to investigate and support causal hypotheses. Advanced techniques such as difference-in-difference and instrumental variable approaches will be introduced.
This course will cover recent developments in artificial intelligence and big data and their application and implementation in core business processes. This course will examine how to implement advanced artificial intelligence and big data analytics projects within the firm. This will include building and housing an analytics team; identifying, proposing and evaluating analytics projects; and actual implementation of the project.
This course provides a foundation for thinking about the ethical and legal implication of leveraging data science in business decision making. Topics covered include ethical theories as well as legal principles and values concerning privacy, informed consent, data ownership, capture, aggregation, dissemination and protection.
The first part of this course will introduce students to optimization, constrained optimization and mathematical programming. The second part will introduce basic concepts of simulation including input analysis, flowcharting, model building, model validation, verification and output analysis.
This project course focuses on formulating and proposing a business analytics strategy to solve an unstructured business problem. Students focus on understanding the underlying managerial issues in the company or the organization they are working with, and prepare a proposal for the analytics project they are aiming to tackle.
This project course focuses on the implementation and delivery of a business analytics project. Students work on gathering the required data, structure their analytical solutions, design implementation steps and present their findings.
This course will provide students with the opportunity to apply data analytics tools, such as OLS, logistic and probit regressions, simulations, and optimization analysis, to accounting information in order to address a variety of problems. These problems span the various functional areas of accounting, including financial and managerial accounting, auditing and taxation. The types of problems that students may be exposed to include, but are not limited to, using data to detect earnings management in financial information, assess the financial performance, position and/or credit risk of an entity, estimate cost functions, identify cost overruns, perform budgeting simulations, optimize short run production, search for patterns, outliers and anomalies for the purpose of assessing audit risk and/or performing audit procedures, and to assist in tax planning and compliance.
This course intends to provide an overview of Business Analytics tools applied to finance.
We explore analytical tools such as time series analysis applied to financial markets. In particular, we broadly focus on financial decisions involving portfolio selection, risk measurement, risk management, hedging, and other financial modelling decisions involving equity, fixed income and derivative markets. Topics include CAPM and Fama-French 5-factor asset pricing models, portfolio theory, liquidity risks and measurement, options and futures, credit analytics, VaR (Value at Risk), and Monte Carlo Simulation. We focus on R programming to download and process financial and economic data from various sources, such as WRDS, Bloomberg and also publicly available sources such as Yahoo! Finance, Google Finance, FRED (Federal Reserve Bank’s Economic Data Library), and SEC.
This course will help prepare you for the role of HR data scientist or individual wanting to become more sophisticated in terms of their people analytics. You will learn which analyses are appropriate for various kinds of HR-related data-driven questions. The types of analyses you may be exposed to include, but are not limited to: a brief introduction to psychometric theory; how to build predictive models of employee turnover and job performance; statistical methods for validation of HR assessment measures; estimation of adverse impact; utility analysis; meta-analytic methods specific to HR; how to analyze and interpret employee engagement data; multi-level analysis of organizational data; methods to investigate pay equity; methods to effectively visualize/communicate complex organizational data to decision-makers.
The course deals with how marketers can extract useful information and intelligence from marketing data for designing marketing strategies. The emphasis is on advanced data analysis techniques relevant to marketing decisions. Students will learn to master strong analytic skills with the application to customer relationship management, brand marketing, customer segmentation, sales promotion, social media marketing and other marketing topics.
Students in this course will study the application of data driven analytics to core problems in operations and supply chain management. This course will use techniques including regression, optimization and simulation to model and analyze problems in inventory management, site selection, revenue management, transportation and logistics. The course will emphasize the use of large, realistic supply chain data sets to develop management insights.
“My experience in the Lazaridis MMA program was immeasurably helpful in preparing me for my new career. It helped me shift my career path from pure accounting to the data analytics field and provided me with practical management skills.”
Grace Wu (MMA ’21), Ecommerce Fraud Strategy Analyst, Canadian Tire Bank
Take the first step in your graduate education and apply to one of our graduate programs. Follow our three-step admission process — we’ll walk you through how to apply and prepare for your first day as a graduate student.
Applications are accepted on a rolling basis within deadlines until we reach enrolment capacity.
The first consideration deadline is Jan. 15*, at which time the admissions committee will review completed applications. Following the early deadline, the admissions committee considers applications on a rolling basis. Offers of admission are awarded to successful candidates until the program is full.
After you have submitted your OUAC application, paid the non-refundable application fee, and Laurier has received your application, you'll receive an email from email@example.com advising you to upload the additional required documentation to Laurier's Online Registration and Information System (LORIS).
For our Master of Science in Management Analytics program, you must include:
*Note: Your application should be complete (OUAC application completed and supplemental documents and references submitted) by this date. This process normally takes at least two weeks. Please apply through OUAC as early as possible.
Proficiency in written and spoken English is essential to graduate studies at Laurier. Applicants whose first language is not English and who have not completed an undergraduate or graduate degree at an institution where English is the language of instruction are required to provide evidence of English language proficiency. Normally this evidence is a score of at least 100 in TOEFL or at least 7 in IELTS. If applicable, results from accepted testing services must be uploaded to LORIS.
*Tuition fees for each school year are changed and come into effect in the spring term each year. Wilfrid Laurier University reserves the right to modify any part of this program. Fees are approved by the Board of Governors and are subject to change without notice.
We are pleased to offer a limited number of entrance scholarships, ranging from $5,000 to $10,000, for outstanding applicants to the Lazaridis MSc in Management Analytics program. All applicants are automatically considered. Scholarships will be awarded based on academic excellence; no additional application is required.
Laurier also offers a variety of Scholarships and Bursaries to graduate students which are usually based on academic performance once you are in the program.
There are different options available for financing your education. Learn more about funding and scholarship opportunities to support you throughout your graduate career.
If you pay all or part of your tuition fees yourself, a Tuition Tax Credit is available. Consult with a professional tax advisor for more information.
Once you are accepted into the MMA program, you are in a position to apply for OSAP. OSAP is a needs-based program and offers financial assistance to eligible students in Ontario to help pay for tuition, textbooks, incidental fees to the university, and the cost of living while attending school. You must apply directly to OSAP to determine your eligibility.
We have our own contact at TD Bank who works with our MMA candidates. If you are a Canadian citizen, or a permanent resident of Canada you may contact Carl Coelho for an assessment of eligibility for a student line of credit and assistance with the application process:
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"There is virtually no industry left untouched by the digital advancements we’re living through and our program is designed to give you the knowledge, confidence and managerial perspective needed to define the future of this growing professional field."
Michael Pavlin, Associate Professor
William Birchall Chair in Management Analytics
Businesses need skilled workers who can adapt, analyze and use data to advance strategy. If you’re already good at math and statistics, this degree will add the practical management-analytics skills you’ll need to advance your career.
Master the knowledge and skills required in the field through case analysis, in-class projects and a two-term project. Upon graduation, you will be able to:
A background in management analytics can open doors to a number of exciting, future-focused industries, including: