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Master of Financial Data Analytics (MFDA)

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The MFDA aims to train highly qualified finance professionals with the computational skills essential for success in today's ever-changing financial markets. The program is the first of its kind in Canada, combining finance, information technology, and data analytics. Students will gain a fundamental understanding of how AI programming and applications play an integral role in today's economy.

There is an immense amount of data created every day, and the financial industry has benefited the most from data analytics. Banking and financial markets use information and analytics in creating a competitive advantage for their organizations. The need for professionals who can combine their finance and market expertise while supporting these emerging technologies is growing. Graduates of the MFDA will be trained in how to analyze financial data with data analytic skills including AI programming and applications. 

Applicants to the MFDA can be from a variety of educational backgrounds, from business, economics and finance, to mathematics, computer science, information technology and other quantitative areas.

MFDA is a GARP Academic Partner; the program curriculum equips students with the required knowledge to approach Part I Exam of the Financial Risk Manager (FRM) professional certification.

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How to apply

How to apply

Tours and Events

Tours and Events

Admission Requirements

Admission Requirements

Grad Guidebook

Grad Guidebook

What are the study requirements of the program?
  • 24 month program, but can be shortened to 12 months depending on the number of courses taken per semester.
  • Full-time
  • 8 courses in program 
  • Project or Internship
International students:
What are the start dates for the program?
 September start
What is the delivery method?
 In person
When can I apply for the program?

Applications open in September for the following year.

View application information and deadlines.
Is this a STEM program?

Yes, the Master of Financial Data Analytics (MFDA) is a STEM program. The Classification of Instructional Programs (CIP) Canada for the program is 307104 – Financial Analytics.

In addition to the general admission requirements for graduate studies, MFDA applicants must meet the following program-specific requirements: 

  • While applicants may hold any four-year undergraduate degree (or its 3-year equivalent from a recognized institution in countries where it is the accepted practice), preference is given to applicants whose undergraduate degree is in the field of business, economics, finance,  information technology, computer science, modelling and computer science, engineering, mathematics, statistics, physics, actuarial science, or other quantitative fields. 
  • While not required, GMAT/GRE/CFA Level 1 may be recommended to strengthen your application or in lieu of GPA requirement.
  • A statistics course is required. Linear algebra and calculus are highly recommended.
  • If applicable, a minimum score of 7 on the IELTS or 100 on the TOEFL. (Note that these English language proficiency scores are slightly higher than those required for some other graduate programs.) Visit the English language proficiency section of this calendar for additional details.
  • There may be an interview for applicants prior to admission. 

Please refer to tuition estimates from the School of Graduate and Postdoctoral Studies.

905.721.8668 ext. 6209
Fees, funding, and general grad finance questions: gradfinance@ontariotechu.ca 
Inquiries about scholarships and awards: gradscholarships@ontariotechu.ca 

CURRENT TUITION AND FEES

Carolyn McGregor AM

Carolyn McGregor AM

Dean
Information Systems

Bin Chang

Bin Chang

Associate Professor
Finance
Karolina Krystyniak

Karolina Krystyniak

Assitant Professor
Finance
Amir Rastpour

Amir Rastpour

Assistant Professor
Operations
Fletcher Lu

Fletcher Lu

Associate Professor
Information Technology
Miguel Vargas Martin

Miguel Vargas Martin

Professor
Networking and IT Security
Kamal Smimou

Kamal Smimou

Associate Professor
Finance
Xinyao (Joseph) Zhou

Xinyao (Joseph) Zhou

Assitant Professor
Finance
Julia (Hui) Zhu

Julia (Hui) Zhu

Associate Professor
Finance
  • MFDA 5100G - Financial Management
    This introductory finance course provides students with a framework to analyze the individual  and corporate investment and financing decisions. We will cover topics such as types of business organizations, financial statements, time value of money, the valuation of individual securities such as stocks and bonds, relationship between return and risk, capital investments undertaken by corporations, corporate finance policies, and ethical and professional standards.
  • MFDA 5200G - Investments

    This course offers a broad overview of key issues in a dynamic world of investing. We will examine how individual investors choose among various investment alternatives by understanding the risk and return relationship for individual securities as well as the principles of portfolio theory. We will discuss valuation and return patterns of equities and fixed income instruments. Students will learn about market efficiency, active portfolio management and behavioural finance. The course is in line with the CFA curriculum requirements on portfolio management and asset valuation.

  • MFDA 5300G - Financial Derivatives Securities
    This course provides an advanced overview to financial derivatives and their application in risk management and investment. The main focus is on learning mechanisms, valuations, and application strategies of popular derivatives. By the end of this course, students will be able to design, price and apply custom-made derivatives to special investment and hedging needs. Business Analytics and Financial Management are prerequisites. Strong knowledge of calculus and statistics is highly recommended.
  • MFDA 5400G - Financial Econometrics
    The course covers the econometric methods as applied to finance, in particular financial security analysis, risk and portfolio management. It teaches students econometric theories, empirical methods, and gives the students experience in estimating econometric models with financial data. Students will use professional financial databases to obtain financial data and statistical programming software for empirical research. The course is in line with the CFA curriculum requirements on Quantitative methods in Finance.
  • MFDA 5500G - Financial Risk Management
    This course introduces the main components of measuring and managing financial risk in financial services industry. It covers the foundation, valuation, and risk models. Students will use WRDS, Bloomberg Terminals, DataStream, and other data sources and programming software to apply risk modelling and management skills.  By taking this course, the students will master skills and knowledge needed to anticipate and respond to critical issues in financial risk management and gain an edge in their career and professional development. The course is in line with the Financial Risk Manager (FRM) curriculum requirements on valuation and risk models.  
  • MBAI 5100G - Business Analytics

    This course will provide a coverage of concepts and tools used in different stages of a data analytics project, including problem definition, data collection and preparation, data analysis, and knowledge transfer. Statistical and other analytical tools such as data mining, machine learning, social network analytics, text mining and their application to business will be explored. 

    In the current business world, data is a crucial valuable asset owned companies. It is vital for businesses to be able to effectively and efficiently define their problems, collect required data, examine the data, and communicate this information in an appropriate manner to decision makers. Given the massive amount of data available and constant technological advancements in the field of data analytics, it is crucial that students gain skills required to tackle these data-related problems. This course will provide hands-on training for learning these sought-after skills. 

  • MBAI 5300G - Programming and Data Processing

    The first part of the course studies data processing using the following Python libraries: Pandas, Matplotlib, NumPy, SciPy, and others. Jupyter notebooks will be used for visualization. In process, the course introduces Calculus and Statistics for machine learning. The second part of the course studies natural language processing techniques, network analysis, web log data analysis, and data integration techniques including data Wrangling.

  • MBAI 5310G - Artificial Intelligence Programming
    Students will learn to program a computer system to make predictions on, classify or cluster data that the system has never seen before. Topics include theory and practice of supervised and unsupervised learning such as reinforcement learning covering well-known algorithms such as linear regression, Naive Bayes, support vector machines, ensemble methods, K-means, and convolutional and recurrent neural networks. The course uses the Python programming language with TensorFlow and Keras.
Semester
Year
Course Codes
Fall Year 1 MFDA 5100G- Financial Management MFDA 5200G- Investment MBAI 5300G- Programming and Data Processing
Winter Year 1 MFDA 5300G- Financial Derivatives Securities MFDA 5400G- Financial Econometrics MFDA 5500G- Financial Risk Management
Fall Year 2 MBAI 5100G- Business Analytics Internship/Projects
Winter  Year 2 MBAI 5310G- Artificial Intelligence Programming Internship/Projects
Semester
Year
Course
Fall Year 1 MFDA 5100G - Financial Management MFDA 5200G- Investment MBAI 5100G- Business Analytics MBAI 5300G- Programming and Data Processing
Winter Year 1 MFDA 5300G- Financial Derivatives Securities MFDA 5400G- Financial Econometrics MFDA 5500G- Financial Risk Management MBAI 5310G- Artificial Intelligence Programming
Spring/Summer Year 1 Internship/Projects
The Finance Laboratory enables you to bridge the gap between theory in the classroom and training on software used in the industry. in this lab, you will use industry standard databases such as:

  • Bloomberg terminal
  • IBES
  • SDC
  • S&P Capital IQ
  • Thomson Reuters Datastream
  • Thomson Reuters tick history
  • LSEG Workspace (ex Refinitiv Workspace)
You will also learn trading in a virtual environment using Rotman trading cases and Rotman Portfolio manager. This experiential learning provides a unique skill set that you can bring to your future employers.

LEARN MORE ABOUT THE FINANCE LAB HERE

The Master of Financial Data Analytics (MFDA) will prepare students for the Part I Exam of the Financial Risk Manager (FRM) professional certification.
Recognized in every major global market, the FRM is the leading certification for Risk Managers. Earning your FRM proves your knowledge and skills are up to the latest international standards.

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" GARP is very pleased to welcome Ontario Tech to the GARP Partnership for Risk Education. The Master of Financial Data Analytics offered by the Faculty of Business and Information Technology is a dynamic, rigorous program.  Its interdisciplinary nature affords students a strong theoretical background in the principles of finance, as well as the opportunity to enhance their data analytic skills.  Graduates of this program will be well-positioned to pursue the FRM designation and to assume strategic roles within the global risk management profession."
William May, Global Head of Certifications and Educational Programs – GARP

International Students
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International Students

Graduate awards and funding
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Graduate awards and funding