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There are few fields that are in higher demand right now than data science. Our Certificate in Data Analytics, Big Data, and Predictive Analytics, available in a fully online format, will help you build the full range of skills you need to advance in your current job or start a new one.

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Find out more about the certificate — join us for an online information session.

Date: Friday, November 19, 2021

Time: 12:00 p.m.–1:00 p.m. EST


This certificate is one of several that offer financial aid options for eligible students. It is also one of more than 30 online certificates you can complete from home.

Starting in Spring 2020, this certificate can be completed fully online.

Data analytics, big data, and predictive analytics domains are recognized as having significant and growing societal importance to organizational performance.

They are becoming essential to:

  • the research and development of products and services
  • communication with clients and customers
  • commerce
  • finance
  • public utility and infrastructure
  • law enforcement
  • government institutions

This certificate will provide you with a strong foundation in analytics, tools, and statistics. It will help you use data analytics, big data, and predictive analytics to optimize performance in fields like data warehousing, data management, IT, and more. It may provide you with the necessary credentials for career promotion or other professional enrichment.

Upon successful completion of this certificate, you will be prepared to take the Institute for Operations Research and the Management Sciences (INFORMS) Certified Analytics Professional (CAP®) exam to become a certified professional in this burgeoning field. See Professional Designations and Accreditation below for details.

What Will You Learn?

Gain relevant, timely, and effective education in data analytics foundations, basic and advanced analytics methods, and big data analytics tools.

Learn how to:

  • Demonstrate fundamental concepts and techniques of big data and its industrial applications.
  • Develop analytical and numerical expressions using real problems.
  • Apply engineering mathematics and computations to solve mathematical models.
  • Understand next-generation big data platforms such as Apache Spark.
  • Know which direction to go next and how to further enrich your knowledge and experience.

Programming Tools

  • R
  • SAS
  • Python
  • XPath and XQuery
  • Structured Query Language (SQL)
  • Hadoop (MapReduce)

Machine Learning

  • IBM Watson
  • Weka

Data Collection and Storage Tools

  • MySQL
  • MongoDB

Advanced Analytics Tools

  • IBM Watson
  • Apache Spark

File System

  • Hadoop Distributed File System

Extract, Transform, and Load Tools

  • Apache Pig
  • Apache Hive

Integrated Development Environment (IDE)

  • MySQL Workbench
  • RStudio
  • Studio3T

This certificate provides training on cutting-edge technology to enrich skills you may already possess as a data analyst.

Who Should Take This Certificate?

This certificate is intended for individuals who want to enrich their knowledge and learn the fundamental components of the world of big data. This includes current and aspiring employees in both public and private corporations and government entities.

You may be interested if you currently hold or wish to hold the following job titles:

  • Data Scientist
  • Data Analyst
  • Financial Analyst
  • Applied Statistician
  • Business Analyst

Who Teaches the Courses?

Our instructors are selected through a highly competitive process and are expert practitioners and highly skilled academics in industrial and research sectors.


Virtual Data Analytics Tutor

In addition to your instructors, you will have access to an online virtual tutor for help with out-of-class assignments and course material: dataanalyticstutor@ryerson.ca.

Industry and Careers

The skills taught in this certificate are applicable across almost all sectors.

In particular, these skills are especially useful for those aspiring to be or are already working as:

  • Data Scientists
  • Data Analysts
  • Financial Analysts
  • Applied Statisticians
  • Business Analysts

Professional Designations and Accreditation

Informs logo

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 maintain your CAP®:

  • CIND 123 - Data Analytics: Basic Methods
  • CIND 110 - Data Organization for Data Analysts
  • CIND 119 - Introduction to Big Data
  • CIND 820 - Big Data Analytics Project
  • CIND 830 - Python Programming for Data Science
  • CMTH 642 - Data Analytics: Advanced Tools

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

Recommended Course Sequence

Note: For students who  registered before July 2, 2020, the curriculum requirements for the Certificate in Data Analytics, Big Data and Predictive Analytics have been revised, effective Fall 2020: 

  • CIND 719 and CKME 136 have been removed from the certificate
  • CIND 820 and CIND 830 have been added to the certificate

Students must complete the curriculum requirements that were in effect at the time of their registration. For curriculum details, please refer to the Archived Calendars for your year of registration.  

We recommend you complete courses in the following order:

  • CIND 123 - Data Analytics: Basic Methods
  • CIND 119 - Introduction to Big Data  
  • CIND 830 - Python Programming for Data Science   
  • CIND 110 - Data Organization for Data Analysts

The first four courses cover a foundational level of the seven knowledge clusters and technical skill sets (from the INFORMS CAP exam) for big data analysis. You can take them concurrently in any combination in the same term or multiple terms. 

  • CMTH 642 - Data Analytics: Advanced Tools 

The prerequisites for this course are CIND 119, CIND 123 and CIND 830

The prerequisite for this course is CMTH 642.

This capstone course is completed off-campus with online access to your assigned instructor by pre-scheduled appointment, via video teleconference, phone, and email. The capstone course may only be taken after the other five courses have all been completed. 

This is a project completion course on large publicly available data sets. You will be required to come up with a maintainable solution written in either R or Python. You will graduate with a completed project for your career portfolio that you may send to potential employers and present verbally at interviews.

Note: You can choose to complete the certificate at your preferred pace. Some students take two to three courses every term and the capstone course in the third term, to complete the certificate in three consecutive academic terms (Fall, Winter, Spring/Summer).


Marissa“I gained essential data science skills that let me challenge the status quo, improve processes, and add value to my role and my team. My Chang School certificate qualified me for my current leadership position and helped me achieve my professional goals.” – Marissa, Student



Mohammed Karim“The Chang School’s Certificate in Data Analytics, Big Data, and Predictive Analytics has given me a unique combination of essential, hands-on skills that allows me to excel in this competitive field. I was able to make professional connections through my courses, both with my classmates and my instructors, who are working subject matter experts. The program allowed me to build on my computer science degree and focus on big data tools and applications. I was looking to enter a field I was passionate about and to get a job I loved doing, and the skills and knowledge I gained from this program have been at the core of my career success.” – Mohammed Karim

Joanna Kader“Since I emigrated from Syria, Ryerson has been the university that welcomed me and offered the right continuing education courses to Canadianize my skills and restart my career in Canada. When Ryerson announced the Data Analytics, Big Data, and Predictive Analytics program, I jumped at the opportunity and was not disappointed. Before I graduated from the program, I was offered a position as a Director with an innovative IT company looking to start its Big Data department. Now I work in Anti-Money Laundering and Anti-Terrorist Financing, Data and Analysis as Product Owner in one of the largest banks in Canada. The Chang School prepared me to start this career and taught me how to be curious and continuously learning.” – Joanna Kader

Mai-Huong Vo“I took the in-class program and I am glad I did. I truly enjoyed the interactions with my classmates, TAs, and instructors. They were all very personable and helpful. For instance, one of my classmates was a professor at top Canadian university. Our discussions were topical and progressive. We discussed how Big data, for example, could potentially be used to improve our healthcare system. The Chang School program is thoughtfully designed to focus on active ways of learning. The lab-work and assignments were based on real life scenarios and incorporated case studies with real datasets.” – Mai-Huong Vo

“The courses are great, and the teachers and the TA help us learn. Thank you to the certificate team for giving us all the information and help we need to succeed in this program, in class and professionally, by reviewing our resumés and more!” – Simon Atron

“I took the Certificate in Data Analytics, Big Data, and Predictive Analytics because I wanted to open my mind to new ways of thinking and expand my skills with the latest data science developments, concepts, and methodologies. I had the opportunity to network with academics and professionals in the data science sector. I gained a demonstrable proficiency of basic data science concepts, as well as an understanding of methodologies and syntaxes. These skills have built on my MBA degree and expanded my critical thinking.” – Kim, Graduate, 2019

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

Frequently Asked Questions

How long does it take to complete the certificate?

You may choose how quickly you want to complete all six required courses. To complete the certificate the fastest way possible, students take four courses (CIND123, CIND830, CIND119 and CIND110) in Term 1, the 5th course CMTH642 in Term 2, and the Big Data Analytics Project CIND820 course in the third term, to complete the certificate in three straight academic terms (Fall, Winter, Spring/Summer) or one academic year.

Note: Courses run every term and typically fill up six weeks before the start of term. Enrol early to avoid disappointment.

Can I take more than one course in the evening and still hold my full-time job?

Yes. It is possible to take more than one course in one term and still hold your full-time job. If you are only able to take two or three courses in Term 1, those courses should be CIND 123, CIND 830, and CIND 119.

In the event that you are unable to take more than one course in Term 1, start with CIND 123, followed in Term 2 by CIND 830, CIND 119, and CIND 110.

If required, you may opt to take CMTH 642 and CIND 110 in Term 3. In the fourth term you would take the CIND 820.

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 allowed 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 times the regular student tuition fee. Visit International Students for details.

The following courses are degree credit:

  • CIND 123 - Data Analytics: Basic Methods
  • CIND 110 - Data Organization for Data Analysts
  • CIND 119 - Introduction to Big Data
  • CIND 820 - Big Data Analytics Project
  • CIND 830 - Python Programming for Data Science
  • CMTH 642 - Data Analytics: Advanced Tools

Who is the target audience for this certificate?

This certificate is intended for current or aspiring adult professionals who have an interest in the subject for career advancement. Employers seek data analysts and data scientist who come from a wide range of interdisciplinary educational and professional backgrounds. 

Are there any company affiliations or career placements available as part of this certificate program?

There are no direct corporate associations or career placements available. However, companies and organizations seeking data analysts and data scientists do contact the Program Director with job postings and the postings are emailed out to all students in the sixth and final course, CIND 820 -  Big Data Analytics Project and to recent certificate graduates. The Chang School can provide career search support upon request, such as resumé preparation and one generalized letter of reference for applying to all job positions.

What programming languages are used to teach this certificate?

The certificate is taught using R-language and Python.

What prerequisites or professional experiences are recommended for this certificate?

This is an Open Admissions program. No previous background is required. However, due to the rigorous nature of this certificate, applicants who are wondering if they’re a good match should contact Client Services at ce@ryerson.ca and attach a current resumé.

Are there any specific PC requirements for course work?


Computer requirements for data science courses:

The minimum required RAM on a student’s computer 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 3 course assignments per course, having a GPU in your computer is required. 

The storage unit of the student’s computer must be a solid state drive model and needs to be at a minimum 256 GB.  

A PC computer is needed (no Mac OS or Apple products ), as most of the technical tools that are used in these data science courses are compatible to be installed and to run on Linux and/or Windows operating systems.

What is the process for installing SAS software on my computer? Is there a cost?

SAS is a free virtual application. Visit Ryerson’s Computing and Communications Services (CCS) website to download it.

What laptop or tower PC computer configuration should I have to complete the capstone course?

You will need a PC laptop or PC computer (no Mac OS or Apple products) with:

  • Operating system: Windows 10; 32-bit or 64-bit
  • Processor: Intel®Core™i5-3230M CPU @ 2.60GHz
  • Installed memory (RAM): 16 GB (15.9 usable) (NOTE: You need 16 GB to run Hadoop.)

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.

Certificate Requirements

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

Admission Criteria


  • Ontario Secondary School Diploma (OSSD) or equivalent
    • With 6 Grade 12 U credits (including English, Mathematics (Advanced Functions and one of either Calculus and Vectors or Data Management), and Science (Biology or Chemistry or Physics)) OR M credits with a minimum average of 70 percent or equivalent academic status


  • Mature student status with 4 years of relevant professional experience
    •  And permission of the academic coordinator

Awards and Financial Aid


Ontario Student Assistance Program (OSAP)
This certificate program is OSAP eligible. To learn more, visit the Student Financial Assistance website.

Contact Us

Questions? Contact Client Services.

Email:  ce@ryerson.ca

Additional Details


You may only select 1 of CIND 110 or CCPS 270.