The Master of Data Science (MDS) is a 12-month coursework program offered by the Department of Mathematics and Statistics that trains individuals to become computationally skilled and ethically minded data analysts. Students become well versed in key technologies in data science, including data wrangling, data mining, data integrity, visualization, machine learning, predictive modelling, and spatial-temporal methods. Through hands-on training, students analyze big data independently and collaboratively such that graduates are primed to help organizations translate data into knowledge and actionable insights. The program features in-class experiential learning opportunities, including how to address and describe complex problems relevant to industry partners, as well as how to explore ethical considerations of privacy, data security, objective analysis and visualization. 

Administrative Staff

Director and Graduate Program Coordinator
Lorna Deeth (548 MacNaughton, Ext. 53034)
mdsdirector@uoguelph.ca
Graduate Program Assistant
Tricia Townsend (438 MacNaughton)
mdsgrad@uoguelph.ca

Graduate Faculty

This list may include Regular Graduate Faculty, Associated Graduate Faculty and/or Graduate Faculty from other universities.

MDS Program

Admission Requirements

Upon recommendation by the Department of Mathematics and Statistics, admission to the Master of Data Science may be granted to applicants who have completed an honour’s Bachelor’s degree or equivalent from an accredited institution with a minimum overall average of 75% (B) in the last four semesters of study with:

1) a major or minor in data science, computer science, mathematics, statistics, or a related field; or

2) working knowledge of statistics and computer programming, as demonstrated through completion of university or college level degree credit courses equivalent to the U of G courses STAT*3240 Applied Regression Analysis and CIS*2500 Intermediate Programming.

Please note: prospective students with an Honour’s Bachelor’s degree in an unrelated field who do not meet the above requirements may gain entry to the program after completing the Diploma in Applied Statistics (or equivalent) with a minimum overall average of at least 75% (B). 

Successful applicants must also meet the University of Guelph’s English Proficiency requirements for admission. If an applicant’s first language is not English, an English Language Proficiency test will be required during the application phase. 

All applications will be received and reviewed by the Data Science Program Committee. The program especially encourages applications from qualified members of under-represented groups, particularly from those who self-identify as women, visible minorities and Indigenous peoples.  

Learning Outcomes

Upon successful completion of the Master of Data Science program, graduates will have the capacity to:

1. Exhibit a solid understanding of statistics and competency in computer programming;
2. Demonstrate an in-depth understanding of the key technologies in data science: visualization, data mining, machine learning, and predictive modelling;
3. Develop advanced skills in data acquisition, processing, and manipulation;
4. Apply statistical methods and predictive modelling to answer queries, predict trends, and model real-world problems;
5. Analyze big data using state-of-the-art software tools to draw meaningful conclusions;
6. Communicate and translate data into actionable insights for diverse audiences;
7. Create compelling narratives/presentations of data analysis results using appropriate data visualization and non-technical language; and
8. Recognize, analyze and apply ethical practices in data science related to intellectual property, data security, integrity, and privacy throughout the full data life cycle, including collection, storage, processing, analysis, and deployment.

Program Requirements

Students in the Master of Data Science program are required to complete a minimum of 4.00 graduate credits, consisting of four core courses (2.00 credits), two electives (1.00 credits), and either the two capstone courses or DATA*6700 Data Science Project (1.00 credits).  

Core Courses:

Course List

Code	Title	Credits
DATA*6100	Introduction to Data Science	0.50
DATA*6200	Data Manipulation and Visualization	0.50
DATA*6300	Analysis of Big Data	0.50
DATA*6400	Machine Learning for Sequential Data Processing	0.50

Capstone Courses:

Course List

Code	Title	Credits
DATA*6500	Analysis of Spatial-Temporal Data	0.50
DATA*6600	Applications of Data Science	0.50

Electives:

Course List

Code	Title	Credits
CIS*6020	Artificial Intelligence	0.50
CIS*6050	Neural Networks	0.50
CIS*6070	Discrete Optimization	0.50
CIS*6160	Multiagent Systems	0.50
CIS*6320	Image Processing Algorithms and Applications	0.50
ENGG*6070	Medical Imaging	0.50
ENGG*6100	Machine Vision	0.50
ENGG*6140	Optimization Techniques for Engineering	0.50
ENGG*6400	Mobile Devices Application Development	0.50
MATH*6020	Scientific Computing	0.50
MATH*6021	Optimization I	0.50
MATH*6022	Optimization II	0.50
MATH*6051	Mathematical Modelling	0.50
MATH*6071	Biomathematics	0.50
PHIL*6400	Ethics of Data Science	0.50
PLNT*6500	Applied Bioinformatics	0.50
STAT*6550	Computational Statistics	0.50
STAT*6801	Statistical Learning	0.50
STAT*6802	Generalized Linear Models and Extensions	0.50
STAT*6721	Stochastic Modelling	0.50
STAT*6821	Multivariate Analysis	0.50
STAT*6841	Computational Statistical Inference	0.50
STAT*6950	Statistical Methods for the Life Sciences	0.50

Courses