Faculty of Science

Department of Mathematical and Statistical Sciences

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Mathematical and statistical sciences, University of Alberta

Course Based Master of Science in Modeling, Data and Predictions

Are you good in Math or Stats? Are you aspiring for a data-science related career? The course based MDP program is designed to help you achieve your goals!

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Admission Status

Open

Accepting applications for Fall 2025 Semester Start

Deadline for submissions is

March 31, 2025

Program Details

Faculty:

Science

Delivery Mode:

On Campus

Program Type:

Course Based

Program Duration:

16 months - Full time

Part time options available

Degree Level:

Master of Science

Program Overview

Admissions & Application

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Courses

Course work for the MDP program include core courses required for all students in the program, and selective courses from a list of approved courses. Graduate courses are offered during the Fall and Winter semesters.

Students are required to complete a minimum of 30 credits of core course work, including a 6 credit Capstone Project.

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Core Courses

The required core courses aim to provide training of skills in mathmatical modeling, statistical analysis, statistical machine learning, and modern computational methods.

COURSE

DESCRIPTION

HELPFUL NOTES

UNITS

MATH 509

Data Structures and Platforms

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Basic data analysis with R, SAS, and Python. Program development with Jupyter notebooks. Cloud computing, collaborative software development, docker containers, kubernets. Internet security, privacy and ethics. Technologies will be updated as new developments arise.

Prerequisites: No programming skills are needed.

This course is an introduction to Bayesian modeling using Python, as well as some useful numerical methods and techniques. These are excellent skills, but not fully described in the University Calendar's course description. No programming skills are needed. It’s beneficial to review Bayesian statistics before taking this course.

MATH 572

Mathematical Modeling in Industry, Government, and Sciences

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Developing mathematical models to solve real-world problems, model analysis, fitting model to data, model validation and selection, and interpretation of model outcomes. Types of models include difference equation models, differential equation models, network models, and stochastic models.

Prerequisites: No programming skills are needed.

Some programming experience is recommended in addition to the recommended course on differential equations. The course materials and assignments are good preparations for real-life modeling.

STAT 537

Statistical Methods for Applied Researchers II

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Review of basic statistical concepts of inference and probability theory. Includes applied methods of Linear and non-linear regression and analysis of variance for designed experiments, multiple comparisons, correlations, modeling and variable selection, multicollinearity, predictions, confounding and Simpson’s paradox. Includes case studies and real data applications. Each researcher works on a project to present, highlighting the methods used in the project.

Prerequisites: No programming skills are needed.

Familiarity of materials covered in Stat 252 (Introduction to Applied Statistics II) would be beneficial. Recommended to take Stat 537 in the first semester as a primer and/or prerequisite for other courses.

STAT 513

Statistical Computing

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Introduction to contemporary computational culture: reproducible coding, literate programming. Monte Carlo methods: random number generation, variance reduction, numerical integration, statistical simulations. Optimization (linear search, gradient descent, Newton-Raphson, method of scoring, and their specifics in the statistical context), EM algorithm. Fundamentals of convex optimization with constraints.

Prerequisites: No programming skills are needed.

This course is very challenging. We recommend the textbook “Statistical Computing with R” by Rizzo and working through its exercises. Programming skills are required. Familiarity with Linear Algebra, optimization, and numerical methods would be beneficial.

STAT 541

Applied Statistical Methods for Data Mining

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The course focuses on statistical learning techniques, in particular those of supervised classification, both from statistical (logistic regression, discriminant analysis, nearest neighbours, and others) and “machine learning” background (tree-based methods, neural networks, support vector machines), with the emphasis on decision-theoretic underpinnings and other statistical aspects, flexible model building (regularization with penalties), and algorithmic solutions. Selected methods of unsupervised classification (clustering) and some related regression methods are covered as well.

Prerequisites: No programming skills are needed.

This course provides a fast-paced overview of various machine learning topics. Some prior experience in machine learning and Python programming is useful.

Elective Courses

In addition to the required courses, students can choose elective courses among many data analytic courses offered on campus by different departments. These courses allow students  to further explore their professional interests.

Students must complete at total of 9 elective credits (three courses). These courses are dependent on the semester and we will work with you to select which ones are best for you.

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Capping Project

A Capping project, also called a Capstone project or Practicum, is designed to give our students an opportunity of applying their newly acquired knowledge to practical problems in a supervised interdisciplinary research project that offers more in-depth learning and research experience on a topic, or through working for companies and government agencies in data analyst intern positions to gain valuable work experience and important professional and soft skills for the workplace. A final written report of the Capping project is a part of the requirements for graduation.

COURSE

DESCRIPTION

HELPFUL NOTES

UNITS

STAT 900 AB

Capstone Project

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Open only to students taking the MSc non-thesis option in mathematics.

Prerequisites: No programming skills are needed.

This is the Capstone Project course, to be taken while completing your Capstone report.

MATH 900 AB

Capstone Project

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Open only to students taking the MSc non-thesis option in mathematics.

Prerequisites: No programming skills are needed.

This is the Capstone Project course, to be taken while completing your Capstone report.

Frequently Asked Questions

What is a full-time course load for the MDP Program?

For graduate programs, the full-time course load is 3 graduate courses per semester. International students need to take the full course load to maintain their full-time status. Students in the MDP program are expected to take 6 courses during the first two semesters, and complete the course requirements by completing the 2 remaining courses and complete the Capstone project during the last semester.

Can I take the required courses during the summer?

The required courses of the MDP program are not offered during the Summer or Spring Semesters (April-August).

Can I take more courses than required, e.g. can I take courses during the summer while working on internships?

Students can choose to take courses in addition to those required by the MDP program. Please note that additional fees will be assessed for additional courses taken.

Can I take all 8 courses during the first two semesters?

For the MDP Program, 3 courses is definitely a full-time course load, because graduate-level courses are more challenging and require more of a time-commitment. Taking 4 graduate-level courses in one semester is definitely an over-weighted schedule, and is not recommended.

Is it possible to complete the MDP Program within 16 months?

The MDP program requires students to complete 8 courses (24 credit-hours), and additional 6 credit-hour Capstone project. These can be achieved as follows:

First Fall Semester: three graduate courses, a total of 9 course credits.
First Winter Semester: three graduate course, a total of 9 course credits.
Second Fall Semester: two graduate courses and the Capstone project, a total of 6 course credits and 6 Capstone credits.

Students may choose to conduct a research project or work as an intern during the Spring and Summer semesters, but this is not required.

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In the case a student choose to take a research project or work as an intern during the Spring and Summer semesters, the following is a plausible schedule:

First Fall Semester (September to December): 3 core courses or 2 core courses + one selective course.
Winter Semester (January to April): 3 core courses or 2 core courses + one selective course, discussions with a Mentor about a research project, or applications for internships and interviews.
Spring and Summer Semesters (May to August): carry out a research project or work as an intern.
Second Fall Semester (September to December): complete the remaining course requirements, and complete the Capstone Project and write the final report (which can be based on research project or your internship experience), apply for jobs, graduation.

How does an internship work, are students provided with an internship? What if I cannot get an internship?

Students can choose to find intern jobs with companies or government agencies, typically during the summer break when students are not required to register full time. For international students, off-campus working needs to be in compliance with the Canadian immigration rules. All of the 12 students in the 2021 MDP cohort found paid intern positions.
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The MDP program will host professional development workshops to prepare students for job applications and interviews.

A student can also choose to conduct a research project. Students with prior work experience may choose to do a research project to gain more in-depth understanding of a particular field. Either working as an intern or on a research project can facilitate the completion of the Capstone project.

For an international student, is a special work permit required to work on an internship?

Students are advised to consult with immigration specialists at the University of Alberta International, or make inquiries at an Immigration, Refugees and Citizenship Canada office about work permit requirements.

Faculty Contacts

Have questions regarding the MDP Program? We would love to hear from you and answer your questions!

DIRECTOR

Prof. Michael Li

Email:

myli@ualberta.ca

Website: www.ualberta.ca/~myli

PROGRAM COORDINATOR

Ms Jane Cooper

Email:

mssmdp@ualberta.ca

Phone: 1 (780) 492-3396

Fax: 1 (780) 492-6826

Territorial Acknowledgement

The University of Alberta, its buildings, labs, and research stations are primarily located on the traditional territory of Cree, Blackfoot, Métis, Nakota Sioux, Iroquois, Dene, and Ojibway/Saulteaux/Anishinaabe nations; lands that are now known as part of Treaties 6, 7, and 8 and homeland of the Métis. The University of Alberta respects the sovereignty, lands, histories, languages, knowledge systems, and cultures of First Nations, Métis and Inuit nations.