Note: This certificate will require 4-6 hours per week of dedicated time to study, including completing virtual data analytics activities and attending the weekly Zoom sessions.
This four-course Certificate in Practical Data Science and Machine Learning builds on the quantitative background from the Certificate in Data Analytics, Big Data and Predictive Analytics or previous industry experience (or equivalent).
Career-Ready Support Offered
While enrolled in the Certificate in Data Analytics, Big Data, and Predictive Analytics (or Certificate in Practical Data Science and Machine Learning), you will receive a variety of supports, including:
- Virtual mentor services: the virtual mentor is available free of charge upon request to certificate registrants taking courses for 1-on-1 Zoom meetings and for consultations by email about the coursework, assignments, final projects, and for career advising.
- Alumni networking support: participating alumni will provide informal career and networking support through LinkedIn to certificate candidates and graduates.
- Career support: data science job search and career advising will be offered during the final capstone course: CIND 860 - Advanced Data Analytics Project.
- Synchronous weekly online sessions: in these Zoom sessions, the teaching team addresses learners’ questions and strengthens their hands-on, data analytics working knowledge for future jobs in the field.
- Educator support: in addition to the lecture videos and slides as well as an ungraded self-assessment quiz, instructors and teaching assistants provide support for your hands-on practice virtual lab activities.
- Tutor: learners will be assigned a tutor to help with assignments and lab materials by pre-scheduled virtual appointments.
- Discussion forums: the teaching team will monitor discussion forums where learners can share their experiences and exchange ideas with each other.
Industry and Careers
Professionals with highly developed skills in data science and machine learning are in demand. This certificate can lead to career paths in various roles. Here are some examples:
- Automation director
- Big data analyst
- Big data consultant
- Big data engineer
- Chief data officer
- Chief productivity officer
- Data analyst
- Data scientist
- Human-computer interaction (AI) expert
- Human-technology integration specialist
- Operations developer (DevOps)
- Operations manager
Who Should Take This Certificate?
- Professionals with experience in Python, machine learning, and predictive analytics who want to expand their expertise
- Individuals who have already taken our Certificate in Data Analytics, Big Data and Predictive Analytics and want to learn the essential components that are intrinsic to practical data science and machine learning
What Will You Learn?
After completing this certificate, you will be able to do the following:
- Construct big data analytics algorithms and use machine learning algorithms to solve real-world problems
- Use the Linux file system effectively (Bash commands, regular expressions), work with the Hadoop platform, write complex queries on big data using Apache Hive, and write scripts and analyze data using Apache Spark
- Use relevant and visualization R packages or Python libraries to address a problem
- Build and design predictive models to represent domains under study, and build ensemble learning models to aim for higher accuracy
- Differentiate between supervised and unsupervised machine learning algorithms based on research questions
- Apply, train, and evaluate different deep learning models based on specific tasks
- Replicate state-of-the-art studies and highlight the similarities and differences between the actual and replicated work in terms of applied datasets, approaches, tools, and outcomes
- Four required courses
- Cumulative grade point average (GPA) of 1.67+
Hold The Chang School'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.
Professional Designations and Accreditation
Toronto Metropolitan University is recognized by INFORMS as a Recognized Analytics Continuing Education Provider. One 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).
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.
Awards and Financial Aid
Ontario Student Assistance Program (OSAP)
This certificate program is OSAP eligible. To learn more, visit the Student Financial Assistance website.
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, Apple's M1 Chip, Intel i5 or i7 is preferable. The storage unit of the student’s computer must be SSD in type and needs to be at a minimum of 256 GB. A PC or MAC computer is needed, as most of the technical tools that are used in these data science courses are compatible to be installed and to run on Linux, macOS 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.
Is this Certificate OSAP eligible?