Overview
Learn to find analytical solutions to business problems
When it’s time to take your digital data skills to the next level, our Data Science Program can help you achieve your goals. This intermediate-level program is ideal for individuals who already have an introductory background and work experience in a data analytics related field.
- Learn technical concepts from leading practitioners in a convenient virtual classroom setting
- Designed for busy professionals with two pathways, earn a certificate or certificate of professional learning
- Benefit from hands-on courses and computer lab training with the latest industry tools
- Work individually and in teams to complete course projects and build a professional portfolio
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Program highlights Learn More
- Intermediate courses to advance your digital data skills
- Individual and team projects led by industry experts
- Earn a Certificate or Certificate of Professional Learning
- Open enrolment program
Data Science program features:
- Virtual classroom courses taught by industry experts who transform theoretical concepts into real-world applications
- Intermediate level of content is ideal for students with prior academic and work experience
- Gain industry-relevant data science skills
- Network with other digital data professionals
What you'll learn Learn More
- How to translate a business problem into an analytics problem
- Problem-solving techniques and industry software tools
- How to build a professional portfolio through coursework
Through our Data Science program, you’ll learn to:
- Identify a business problem and determine if and how an analytics solution is applicable
- Propose and refine analytical solutions to business problems
- Collect, analyze and share data
- Identify relationships in data
- Select appropriate problem-solving techniques and software tools to test analytical solutions
- Employ industry software tools
Programming tools used:
- Python
- SAS
- R
- Tableau
- Linux
- PowerBI
- SQL and NOSQL technologies (e.g. Cassandra)
- MongoDB & Atlas
- Ataccama DQ
- Metadata Manager / Excel
- Hadoop (MapReduce), DataBricks, HDFS, PIG Spark & Kafka, HBase
Learn more about the Data Science program
- Students or recent graduates from programs with introductory level analytics, statistics, and/or business intelligence courses
- Professionals with prior academic and work experience in data analytics and/or introductory level of data science, and related technology topics
- Employees in finance, insurance, health care, marketing, retail, government, logistics, transportation, information systems, media/entertainment sectors, or other sectors that utilize data analysis
- Individuals seeking a new career path in technology, informatics, business intelligence, web analytics, data collection, and Machine Learning
- Students interested in enrolling in advanced data program streams in big data programming and architecture but need to acquire prerequisite knowledge
Earning a Certificate or a Certificate of Professional Learning could open doors to exciting roles as:
- Data Analysts
- Business Analysts
- Business Intelligence Developers
- Computer & information research scientists
- Quantitative analysts
- Data storytellers / Data modelers
- Statistical Analysts
- Marketing Scientists
- Machine Learning Scientists
- Data Scientists
Upon completion of the program, students will:
- Identify a business problem and determine if, and how, an analytics solution is applicable
- Translate a business problem into an analytics problem
- Propose, and refine, analytical solutions to business problems
- Collect, analyze, interpret, and share data
- Identify relationships in data
- Select problem-solving techniques and software tools to test analytical solutions
- Employ common industry software tools
- Identify, test, and evaluate model structures to apply to solve a business problem
- Assess new and emerging technologies, tools and strategies applicable to data science and related fields
- The following objectives will be threaded within each course
- Demonstrate an awareness of ethical practices and professional standards applicable to the field of data analytics
- Exemplify the skills, attitudes and behaviours required to work and collaborate with people and develop personal management skills
- Employ effective communication practices
Are you a current McMaster University undergraduate student?
Explore the ElevateYourSkills option to learn how you can earn a Data Science certificate as part of your degree.
- Use your electives to earn a professional certificate or diploma
- Fully online courses that you can fit into your schedule
- Providing you with real-life career skills and experience before you graduate
Check out our latest Technology and Data Program Preview webinar to learn more about our Data Science program
What our recent graduates say
The flexibility of the course helped me a lot to balance my work.
The flexibility of the course helped me a lot to balance my work. Our field in data is continuously changing. I found that sweet balance in terms of the focus of the course. It’s a life cycle of how the data is coming in all the way to make it a service or a product. The course gives you that 360 spectrum.
Zaki
Big Data graduate
The flexibility of the course helped me a lot to balance my work.
The flexibility of the course helped me a lot to balance my work. Our field in data is continuously changing. I found that sweet balance in terms of the focus of the course. It’s a life cycle of how the data is coming in all the way to make it a service or a product. The course gives you that 360 spectrum.
Zaki
Big Data graduate
Other programs you may be interested in
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Certificates in Data Science
Certificate in Data Science
Pursue your Certificate in Data Science with McMaster Continuing Education
Explore the requirements below and register today!
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Certificate in Data Science Learn More
Earn the Certificate in Data Science by completing five elective courses from the courses listed.
Certificate in Data Science Requirements
Academic credit: 15 units
Students should possess a minimum of introductory level prior education or work experience in the field of data analytics, statistics.
Bring Your Own Device (BYOD) policy
The Data Science program follows a Bring Your Own Device (BYOD) policy. Prior to enrolling it is recommended to review course outlines for laptop and desktop requirements as courses may need a minimum level of technology specification.
Microsoft Excel
Data Science requires varying degrees of proficiency in Microsoft Excel. Basic knowledge of Microsoft Excel is recommended to be successful in the courses. Excel course modules are available through LinkedIn Learning, free of charge, to all active McMaster University students. Please visit LinkedIn Learning for more information.
Do you need additional training?
Additional training is available for Python programming, SQL, and PowerBI through our partner Ed2Go. Please visit our Professional Development page for more information.
Courses (complete any 5)
Certificate of Professional Learning in Data Science
Pursue your Certificate of Professional Learning in Data Science with McMaster Continuing Education
Explore the requirements below and register today!
Information Box Group
Certificate of Professional Learning in Data Science Learn More
Earn the Certificate of Professional Learning in Data Science by completing three elective courses from the courses listed.
Certificate of Professional Learning in Data Science Requirements
Academic credit: 9 units
Students should possess a minimum of introductory level prior education or work experience in the field of data analytics, statistics.
Bring Your Own Device (BYOD) policy
The Data Science program follows a Bring Your Own Device (BYOD) policy. Prior to enrolling it is recommended to review course outlines for laptop and desktop requirements as courses may need a minimum level of technology specification.
Microsoft Excel
Data Science requires varying degrees of proficiency in Microsoft Excel. Basic knowledge of Microsoft Excel is recommended to be successful in the courses. Excel course modules are available through LinkedIn Learning, free of charge, to all active McMaster University students. Please visit LinkedIn Learning for more information.
Do you need additional training?
Additional training is available for Python programming, SQL, and PowerBI through our partner Ed2Go. Please visit our Professional Development page for more information.
Courses (complete any 3)
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- Find Programs & Courses
- Technology and Data Programs
- Data Science Program
Data Science Schedule
Data Science Schedule
This schedule table displays courses planned to be offered this year and is a guide to planning your courses for the academic year. Please note: Spring term registration opens mid-March and Fall/Winter term registration opens mid-July.
For more information about our learning formats and to choose a format that works best for you, please visit our Learning Formats page. All times listed below are in the Eastern Time Zone (ET).
Course Name (Course Code) | Cost | Units | Fall 2024 | Winter 2025 | Spring/Summer 2025 |
---|---|---|---|---|---|
Statistical Analysis for Data Science (DAT 200) | $780.63 | 3.0 | Sun, Sep 22 - Nov 24 9:30 a.m. - 12:30 p.m. Format: Virtual Classroom | Sun, Jan 19 - Mar 23 9:30 a.m. - 12:30 p.m. Format: Virtual Classroom | — |
TBD | 3.0 | — | — | Sat, May 10 - Jul 12 1:00 p.m. - 4:00 p.m. Format: Virtual Classroom | |
Data Analytics & Modelling (DAT 201) | $1,109.28 | 3.0 | Sat, Sep 14 - Dec 7 9:00 a.m. - 12:00 p.m. Format: Virtual Classroom | Sat, Jan 18 - Apr 12 9:00 a.m. - 12:00 p.m. Format: Virtual Classroom | — |
TBD | 3.0 | — | — | Sat, May 10 - Aug 9 9:00 a.m. - 12:00 p.m. Format: Virtual Classroom | |
Data Management (DAT 202) | $1,109.28 | 3.0 | — | Wed, Jan 22 - Apr 9 7:00 p.m. - 10:00 p.m. Format: Virtual Classroom | — |
TBD | 3.0 | — | — | Wed, May 7 - Jul 23 7:00 p.m. - 10:00 p.m. Format: Virtual Classroom | |
Predictive Modelling and Data Mining (DAT 203) | $1,109.28 | 3.0 | — | Sat, Jan 18 - Apr 12 9:00 a.m. - 12:00 p.m. Format: Virtual Classroom | — |
TBD | 3.0 | — | — | Sat, May 10 - Aug 9 9:00 a.m. - 12:00 p.m. Format: Virtual Classroom | |
Data Analytics Tools (DAT 204) | $1,109.28 | 3.0 | — | Sun, Jan 19 - Apr 13 9:30 a.m. - 12:30 p.m. Format: Virtual Classroom | — |
TBD | 3.0 | — | — | Sun, May 11 - Aug 10 9:30 a.m. - 12:30 p.m. Format: Virtual Classroom | |
Data Science Capstone Project (DAT 205) | - | - | Currently Not Available | ||
Machine Learning for Big Data Analytics (DAT 301) | $1,109.28 | 3.0 | — | Sat, Jan 18 - Apr 12 1:00 p.m. - 4:00 p.m. Format: Virtual Classroom | — |
TBD | 3.0 | — | — | Tue, May 6 - Jul 29 7:00 p.m. - 10:00 p.m. Format: Virtual Classroom |
The schedule table is subject to change. Please visit the course pages to browse classes currently available for registration and the latest cost information.
Admission Requirements
Admission Requirements
This program is open enrolment, which means there is no formal application or admission procedure. To enrol in a course, simply register online. Our courses can be taken as part of a program or individually.
To enrol in McMaster Continuing Education programs, you must:
- Have an Ontario Secondary School Diploma or equivalent
- Be a mature student as defined in the Undergraduate Calendar of McMaster University or
- Be deemed an exceptional case
To ensure you are successful in your online courses, you are required to have knowledge and skills with general computer applications, such as keyboarding, file management, video conferencing and word processing.
Language Requirements
If your first language is not English, you must meet the University’s English language proficiency requirements. Completion of TOEFL exam with a minimum acceptable score of 20 on each of the four components (reading, writing, speaking and listening), valid for 2 years.
Expandable List
- Visit continuing.mcmaster.ca/programs to find your program of choice
- Click on each tab on the program page to learn about credential options and requirements, schedule and fees, and a list of all courses in the program
- Select a course and then select an available offering, noting important information such as cost, delivery format, and start/end dates; then click ‘add to cart’
- Once you have added your courses, click the shopping cart icon at the top right-hand corner of the page (bottom of the browser screen on mobile)
- Review your cart and, once you’re ready to proceed with enrolment, click ‘proceed to checkout’
- As the next step, you will be redirected to Mosaic – McMaster’s Administrative Information and Enrolment system
- Once you are in Mosaic, select ‘new to McMaster’ or log in with your existing MacID and password (if applicable)
- Complete all required fields and select a program of study when prompted (i.e., a specific program or open studies for standalone courses)
- Finally, payment is required in full to secure a spot in your course(s)
A payment receipt email will be issued to you immediately after registering, and a course confirmation email will be sent to you overnight. Within approximately 24 hours of registering, you will also receive an important email containing credentials used to activate your MacID, which you must do before you can access courses in Avenue to Learn. Please review our Getting Started page to learn more about the next steps for beginning your studies after registration, and our Help Centre for our Refund Policy and other frequently asked questions. Please note that on average, each course requires 6-8 hours of study per week, per course (sometimes more) and some courses may have listed prerequisites. Please plan your schedule accordingly. Most students take 1-2 courses per term across a few different terms and a full-time equivalent course load is typically 3-4 courses per term.
For more information and a walkthrough on how to register, please check out this video.
- Payment must be made in full at the time of enrolment
- Online credit card or debit payments are preferred
- Accepted credit cards: Visa, MasterCard and American Express
- Accepted debit cards: Visa Debit and Debit Mastercard
- Google Pay is available for faster checkout
- Payments can be made from a Canadian Financial Institution and can take 2 to 4 business days to arrive in your McMaster student account. Once payment has arrived, you can register for your course. Please note that if the amount of the course fees owing is more than what was transferred to your student account, you will be dropped from the course.
- Problem processing your payment? Please reach out to your credit card company if your payment was declined. Otherwise, wait two hours before attempting your registration again.
Ready to get started?
Visit the Schedule tab to select your course and proceed with the registration steps.