*Coming in Winter 2021*

Applied data science, data analytics, big data, machine learning, and predictive analytics are recognized by employers across all industries and professions as having significant and growing societal importance to evidenced-based decision making and organizational performance.

The Certificate in Practical Data Science and Machine Learning is a four-course, fully online certificate that builds on the quantitative background from completing the Certificate in Data Analytics, Big Data and Predictive Analytics or acquiring previous industry experience (or equivalent).           

The certificate will cover practical applications rather than theoretical knowledge, and will not overlap with the existing Master of Science (MSc) in Data Science and Analytics at Ryerson University.

Note: courses for this certificate will be offered beginning in Winter 2021.

What Will You Learn?

You will learn to program queries in Python, and use machine learning and artificial intelligence (AI) platforms to implement the most up-to-date technical and analytic approaches, applications, big data tools, and software for querying big data, unstructured data, visual data, social media data, semi-structured data, and disparate data sets.

Learn how to: 

  • construct big data analytics algorithms for solving real-world problems
  • decide whether a machine learning technique is an appropriate solution for a given problem or application 
  • quantify the difference between a given set of traditional machine learning techniques 
  • create an ensemble model using a diverse set of learning algorithms to improve results 
  • induce knowledge and create new hypotheses by recognizing patterns and linking features 
  • infer the cause-and-effect of a set of features, other than ensuring the statistical correlation among them
  • support the processing of aggregate analytical queries in data warehouses 
  • propose and develop a deep learning workstation to solve classification and regression problems 
  • investigate the core components of neural networks and visualize their interaction 
  • design evaluation strategies to generalize the power of a machine learning model and to overcome over-fitting issues 
  • map the statistical structure of natural language documents sufficient for applications including document classification, sentiment analysis, or question-answering 
  • visualize the architecture of a machine learning model and monitor its performance metrics 
  • explain, appropriately apply, and execute to completion in model scenarios probability and predictive analyses

The Certificate in Practical Data Science and Machine Learning is an excellent complement to the skills you may already possess as a technical professional. 

Who Should Take This Certificate?

This certificate is intended for individuals who want to expand their expertise and learn the essential components that are intrinsic to practical data science and machine learning. This includes current and aspiring employees in both public and private corporations and government entities. 

You may be interested if you are or wish to become a:

  • Data Scientist
  • Data Analyst
  • Big Data Analyst
  • Big Data Visualizer
  • Big Data Consultant
  • Big Data Manager
  • Chief Data Officer
  • Big Data Engineers
  • Human-Computer Interaction (AI) Experts
  • Human-Technology Integration Specialists
  • Chief Productivity Officers
  • Operations Development (DevOps)
  • Operations Management
  • Automation Directors and Managers

Who Teaches the Courses?

Our instructors are selected through a highly competitive process and are expert practitioners and highly skilled academics in the private, public, and research sectors.

Visit our Instructor Profile Directory to read their bios. All instructors possess PhDs in the field of Data Science and Mathematics.

Industry and Careers

Analysts with highly developed skills in data science and machine learning are required by:

  • Data Scientist
  • Data Analyst
  • Big Data Analyst
  • Big Data Visualizer
  • Big Data Consultant
  • Big Data Manager
  • Chief Data Officer
  • Big Data Engineers
  • Human-Computer Interaction (AI) Experts
  • Human-Technology Integration Specialists
  • Chief Productivity Officers
  • Operations Development (DevOps)
  • Operations Management
  • Automation Directors and Managers

Professional Designations and Accreditation

The Institute for Operations Research and the Management Sciences (INFORMS) is the leading association for professionals in the fields of analytics, management science (MS), and operations research (OR). INFORMS serves the scientific and professional needs of analytics professionals and operations researchers including educators, scientists, students, managers, analysts, and consultants. INFORMS is recognized as the premier organization for advancing the profession, practice, and science of analytics, operations research, and management science.

Ryerson University is recognized by INFORMS as a Recognized Analytics Continuing Education Provider. One (1) Data Analytics, Big Data, and Predictive Analytics Certificate course is equal to 39 INFORMS CAP® Professional Development Units (PDUs). CAP® certified professionals are required to earn 30 PDUs every three calendar years. 

The following courses are recognized as satisfying the professional development units (PDUs) requirement of INFORMS to take and then maintain your CAP®:
     CIND 830 - Python Programming for Data Science (pre-requisite course to CIND719 and CIND840)
     CIND 719 - Big Data Analytics Tools
     CIND 840 - Practical Approaches in Machine Learning
     CIND 850 - Practical Deep Learning
     CIND 860 - Advanced Data Analytics Project
     CIND 110 - Data Organization for Data Analysts
     CIND 123 - Data Analytics: Basic Methods
     CIND 119 - Introduction to Big Data
     CMTH 642 - Data Analytics: Advanced Tools
     CIND 820 - Big Data Analytics Project

Ownership Statement: CAP® is a registered mark of the Institute for Operations Research and the Management Sciences (INFORMS).

According to Business News Daily and Chief Information Officer Magazines, the INFORMS CAP, Microsoft MCSE, and MapR CDA have been named as three of the best big data certifications for 2019[1][2].

In addition to CAP practical requirements, Microsoft Certified Solutions Associate (MCSA) Certificate requires their applicants to pass an exam in analyzing big data using “R” and another advanced data science exam using Azure Machine Learning technology. Moreover, the MapR Certified Data Analyst certificate demands the ability to perform analytics on large datasets using a variety of tools, including Apache Hive, Apache Pig, and Apache Drill. This requires an advanced skill of not only extracting and transforming data (regular certificate) but also loading data to empower required data use cases.

The completion of the Certificate in Data Analytics and  the Certificate in Practical Data Science satisfies the educational requirements for the Microsoft Certified Solutions Associate (MCSA) Certificate exam, the MapR Certified Data Analyst exam and the INFORMS CAP Exam. 

[1] Business News Daily (April 10, 2019).  Best Big Data Certifications.  Available from: https://www.businessnewsdaily.com/10754-best-big-data-certifications.html
[2] CIO. (May 16, 2019).  A7 data analytics certifications that will pay off.  Available from: https://www.cio.com/article/3209911/certifications/big-data-certifications-that-will-pay-off.html

Recommended Course Sequence

Note: Prior to taking the Term 1 two certificate courses, you must first successfully complete CMTH 642 - Data Analytics: Advanced Methods, offered year-round, and CIND 830 - Python Programming for Data Science.

We recommend you complete courses in the following order:

Term 1
     CIND 719 - Big Data Analytics Tools
     CIND 840 - Practical Approaches in Machine Learning

Term 2
     CIND 850 - Practical Deep Learning

Term 3
     CIND 860 - Advanced Data Analytics Project

Frequently Asked Questions

How long does the certificate take to complete?
Students may complete at the earliest the certificate in three academic terms. Students take two courses in the first term; completion of both first term courses is required to take the third course. After the first three courses are completed, the capstone course is taken in the final term to complete the certificate in three straight academic terms.

Note: Courses typically fill up six weeks before the start of term. Please enrol early to avoid disappointment.

Can I take more than one course and still hold my full-time job?
Yes. It is possible to take more than one course and still hold your full-time job. 

Can I substitute one of the courses in this certificate for a different course offered at The Chang School?
No. There are no course substitutions permitted in this certificate program.

Are any of the courses available for substitution or transfer credit?
No. All courses in this certificate must be taken at The Chang School and are not eligible for course substitution or transfer credit application.

Do course fees change if I am an international student?
Yes. If the course is degree-credit, the international fee rate is three (3) times the regular student tuition fee. 

What prerequisites are required for the certificate courses?
Note: CIND830 Python Programming for Data Science is a prerequisite course for CIND 840 and CMTH 642 is a prerequisite for CIND719. It is fully online.

What programming language is used to teach this certificate? 
The certificate is taught using Python and R programming languages.

Are there any specific PC requirements for course work? 
The minimum required RAM on a student’s computer for data science courses must be 8 GB, but 16 GB is highly recommended for efficient processing for training/testing heavy machine learning algorithms, loading R packages and using Python libraries. For the processor, Intel i5 or i7 is preferable. For the certificate courses’ required weekly data analytics technical activities and three course assignments per course, having a GPU in your computer is required. The storage unit of the student’s computer must be SSD in type and needs to be at a minimum 256 GB. A PC computer is needed (Not Mac or Apple), as most of the technical tools that are used in these data science courses are compatible to be installed and to run on Linux or Windows operating systems. Additionally, Oracle VM VirtualBox is a software you may use to enable running, in parallel, two operating systems on your computer.

Which textbooks must I purchase for this certificate?
You will receive your textbook information in your course outline in the first week of class. Please do not purchase any textbooks in advance.

Is this Certificate OSAP eligible?

Certificate Requirements

  • Four required courses
  • Cumulative grade point average (GPA) of 1.67+

To graduate, you must successfully complete, within your official time span, the requirements from the year you registered in the program. For more information, refer to Time Span in our Glossary of Terms. If certificate requirements change and courses are no longer available, you may request Course Substitutions/Directives. You must apply to graduate on my.ryerson (RAMSS) within the appropriate application deadlines (refer to Important Dates). Visit Graduation for details.

Admission Criteria


Hold Ryerson University's Data Analytics, Big Data, and Predictive Analytics certificate or equivalent.


Mature student status and other relevant qualifications or relevant industry experience (to be determined by the Program Director).

If you are an undergraduate student, you should be aware of possible restrictions. Check Curriculum Advising for complete details.

Data Science Bootcamp

Are you looking for some general data science training for your company’s leaders? 
Consider our four-day Data Science Bootcamp.
Master of Science (MSc) in Data Science and Analytics
Some courses from this certificate partially fulfill the prerequisite course requirements for Ryerson University’s Master of Science (MSc) in Data Science and Analytics. 

For more information, please email datascigrad@ryerson.ca 

Big Data Consortium Reports 

Closing Canada's Big Data Talent Gap - Canada's Big Data Consortium (PDF)  (October 2015)
A Vision for Predictive Health Care in Canada (PDF)  (June 2018)
A Vision for Data Monetization in Canada (PDF)  (June 2018)

Contact Us

Questions? Contact Anne-Marie Brinsmead, Program Director
Email: a2brinsm@ryerson.ca

Additional Details