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. Within the MDS program, students may choose to specialize in the field of Geospatial Analysis through additional technical training in Geographic Information System (GIS)/Remote Sensing (ie. geomatics).

Administrative Staff

Director and Graduate Program Coordinator
Aesha Ali (509 MacNaughton, Ext. 53896)
mdsdirector@uoguelph.ca
Graduate Program Assistant
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 complete applications will be received and reviewed by the Data Science Admissions 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, including spatiotemporal 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;
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; and
9. Demonstrate foundational skills in GIS; students in the Geospatial Analysis field will demonstrate advanced skills in GIS and Remote Sensing technologies.

Program Requirements

All Master of Data Science students are required to complete a minimum of 4.00 graduate credits, as follows.

Students in the standard MDS program will complete the four core courses (2.00 credits), two courses from the MDS Restricted Electives list (1.00 credits), and two capstone courses (1.00 credit). Most MDS students will complete capstone courses DATA*6500 Analysis of Spatial-Temporal Data and DATA*6600 Applications of Data Science. With permission from the MDS Director, students may take DATA*6700 Data Science Project as an additional course, or as a substitute for one or two half-credit MDS Restricted Electives and/or capstone course DATA*6600 Applications of Data Science. 

Geospatial Analysis Field

Students who choose to specialize in the field of Geospatial Analysis will complete the four core courses (2.00 credits), two courses from the Geospatial Analysis Restricted Electives list (1.00 credits), and DATA*6700 Data Science Project (1.00 credit).  The project, to be completed in the Summer semester, must have an applied geomatics/environmental modelling focus, and must be approved by the MDS Director in advance.

Core Courses:

MDS Restricted Electives:

Geospatial Analysis Restricted Electives:

Courses