Courses

The fall term and winter term curriculum consists of set of “core” courses, a set of “specialty field” course electives, and Personal Career Management (PCM) classes and activities. Currently, the MDA program offers the following three specialty fields for students to choose from when applying to the program:

  • Artificial Intelligence
  • Finance, Banking and Insurance
  • Generalist

The core courses are common across the different specialty fields, focusing on developing fundamental data analytics and professional skills. The specialty field courses target a specific application area. The courses are offered by the Department of Statistical & Actuarial Sciences and the Department of Computer Science, and are commonly taught by faculty who develop and apply data analytics and data science methods as part of their research program at Western. The Personal Career Management classes and activities are coordinated by the MDA program’s Career Services Officer and are embedded into the program over the fall and winter terms. The PCM classes and activities consist of lectures, presentations, panel discussions, and workshops that will help you understand and have the skills required to chart an effective course for your career. In addition, the Career Services Officer is available to provide support on securing your summer experiential learning opportunity. Through developing your professional skills, connecting with relevant industry partners, and developing your professional network, you will learn how to effectively position yourself to pursue your future career as a data analytics professional.

Core Courses – Required for students in all specialty fields

  • Personal Career Management classes & activities – compulsory attendance
  • 7 courses (0.5 FCE each) required courses:
    • Business Skills for Data Scientists
    • Data Analytics Consulting
    • Databases
    • Introduction to Machine Learning
    • Statistical Modelling I
    • Statistical Modelling II
    • Unstructured Data

Students who can demonstrate sufficient background in any of the above required core courses (e.g., scoring a final mark of 78% or higher in a similar course during their undergraduate degree with sufficient documentation to demonstrate mastery of the topic, such as a detailed course outline) may be permitted to substitute any such course with another graduate level course with a significant data analytics focus from either the Department of Statistical & Actuarial Sciences or the Department of Computer Science. Any such substitutions are subject to the approval of the Director of the MDA program.

The Data Analytics Core consists of courses that combine technical skills drawn from both statistical and computer sciences with complementary professional skills. This Core curriculum emphasizes fundamental data analytics skills, including managing and working with large data (big data programming models and platforms, e.g., MapReduce, Hadoop, etc.), statistical modelling, and algorithmic modelling. Where appropriate, professional skills are threaded into such data analytics-oriented courses through written and oral assessments and group work. These and other professional skills are further developed in two additional courses that develop business, ethics and consulting skills.

Specialty Fields

In addition to Core training, the MDA program offers several Specialty Fields, each representing distinct learning paths that provide domain-targeted training, where data analytics plays a large role.

Artificial Intelligence (AI)

The Artificial Intelligence (AI) specialty field is designed for students who wish to pursue a career in analytics enhanced by a more comprehensive understanding of AI methods and their applications.

The AI field is designed for students who have more than an introductory background in computer science. It has more demanding computer science specific entrance requirements. For specific details please consult the Admissions page.

Graduates from the MDA-AI program will be well-positioned for employment due to the course-based training in fundamental data analytic and AI-specific methods, the communication, business-skills and teamwork training weaved throughout, and the practical learning they receive during their summer experiential learning term. Coursework has been designed to provide technical training in the following areas:

  • Artificial Intelligence
  • Big data management and analysis infrastructure for unstructured data
  • Classification
  • Computer vision
  • Data carpentry/munging
  • Databases
  • Intelligent agents
  • Natural language understanding
  • Neural networks
  • Statistical modelling and inference
  • Supervised and unsupervised machine learning
  • Visualization

Plus, an Artificial Intelligence Seminar course ensures that MDA-AI students have a broad overview of ethics and its relation to AI, including its potential impacts on society.

Students in the AI field are required to take the core curriculum, the Artificial Intelligence Seminar course, plus choose 2 courses (0.5 FCE each) from the following1:

  • Artificial Intelligence I
  • Artificial Intelligence II
  • Advanced Machine Learning
  • Databases II
  • Data Science II
  • Reinforcement Learning

Students who can demonstrate sufficient background in any of the required core courses may be permitted to substitute any such course with another graduate-level course with a significant AI focus, such as one of the remaining AI electives. Any such substitutions are subject to the approval of the Director of the MDA program.

1 Please note that the program will endeavour to offer a wide variety of specialty field courses; however, not all specialty field courses may be offered in a given year.

Finance, Banking and Insurance

The courses for the Finance, Banking and Insurance Specialty Field represent a targeted set of courses for students who wish to pursue careers as an analytics professional in this sector. Broad learning outcomes for the Finance, Banking and Insurance specialty field are to produce graduates who:

  • Understand the basics of the Canadian financial sector including the responsibilities of the main players (e.g., bankers, insurers, regulators), the main organizational groups within banks (e.g., trading, commercial banking, retail banking, risk management) and insurers (underwriting, investing, risk management) and their roles, as well as the way in which data analytics fits into these pillars.
  • Understand the basic features of financial markets such as stocks, bonds, commodities.
  • Understand the basic products available to retail and commercial customers of banks and insurers such as loans, mortgages, insurance policies, and various investment products.
  • Understand particular data analytics tools relevant to banking tasks, such as logistic regression models for credit analysis, Value at Risk models for risk management, and various stochastic models for insurance claim arrival and stock price fluctuation.
  • Understand how to implement these models in their standard industry forms and also, with a critical eye, to know what these models can and cannot reasonably be expected to do.

Financial Modelling courses give students the tools they need for immediate job readiness in the risk management division of a major commercial bank. Those completing the courses understand the various sources of financial risk to a major bank or insurer: equity market risk, interest rate risk, foreign exchange risk, credit risk, and operational risk. They understand the mathematical underpinnings for modeling these risks and how to implement and calibrate the models in code. Importantly, they acquire an understanding of the sometimes severe limitations of these models.

Graduates from the Finance, Banking and Insurance specialty field will understand risk management at banks, including its drivers and implementation strategies, and its specific regulatory pressures. The world of finance changes a great deal every decade, and so it is very important for quantitative finance specialists to be able to be lifelong learners who can break problems down to their essential parts.

Students choose 3 courses (0.5 FCE each) from the set of courses*

  • Advanced Financial Modelling
  • Banking Analytics
  • Data Analytics for Consumer and Retail Credit
  • Financial Risk Management
  • Introduction to Financial Markets and Quantitative Finance with Excel
  • Investment Portfolio Management
  • Life Contingencies II
  • Loss Models I
  • Monte Carlo Methods and Financial Applications
  • Mortality Modelling
  • Stochastic Processes
  • Survival Analysis
  • The Mathematics of Financial Options

* Please note that the program will endeavour to offer a wide variety of specialty field courses in each year. However, not all specialty field courses may be offered in a given year.

Generalist

This specialty field is for students who wish to pursue a career in analytics and want a broader introduction to the various domains where analytics methods are employed rather than focusing more deeply on a single area. It also offers the opportunity for students to gain additional training in data analytics methodology not covered by the required set of core courses. It will produce graduates who:

  • Have an understanding of the diverse ways in which data analytics methodology is applied, as determined by the learning objectives from the variety of specialty field courses they have chosen.
  • Have an expanded knowledge of different data analytics methodologies, as determined by the learning objectives from the elective courses they have chosen.

Students enrolled in the Generalist specialty field must select a minimum of one course from another specialty field; however, no more than one from any individual specialty. These two requirements ensure that Generalist students receive a broad introduction to data analytics while being exposed to the process of applying data analytics to a domain-specific problem. Generalist students will be required to select their specialty field courses in consultation with a faculty advisor.

Subject to these requirements, students choose 3 courses (0.5 FCE each) from the following set of courses*:

  • Actuarial Practice I
  • Advanced Financial Modelling
  • Advanced Machine Learning
  • Analysis of Brain Imaging Data
  • Advanced Data Analysis
  • Artificial Intelligence I
  • Artificial Intelligence II
  • Banking Analytics
  • Cognitive Computing
  • Data Analytics for Consumer and Retail Credit
  • Databases II
  • Distributed and Parallel Systems
  • Financial Risk Management
  • Introduction to Financial Markets and Quantitative Finance with Excel
  • Investment Portfolio Management
  • Life Contingencies II
  • Loss Models I
  • Monte Carlo Methods and Financial Applications
  • Mortality modelling
  • Reinforcement Learning
  • Stochastic Processes
  • Survival Analysis
  • The Mathematics of Financial Options
  • Time Series
  • Visual Computing
  • Any other graduate level course with a significant data analytics focus from either the Department of Statistical & Actuarial Sciences or the Department of Computer Science, with approval from the Director of the MDA program. 

* Please note that the program will endeavour to offer a wide variety of specialty field courses; however, not all specialty field courses may be offered in a given year.