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
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
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!
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Certificate of Professional Learning in Data Science
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)
Data Science Courses
Data Science Courses
Advance your career with online Data Science courses
Explore course options below and register today!
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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. The schedule table is subject to change. Please visit the course pages to browse classes currently available for registration and the latest cost information.
Learning format definitions:
- VC = Virtual Classroom learning format
- O = Online learning format
- OSS = Online Self-Study learning format
For more information about our learning formats and to choose a format that works best for you, please visit our Learning Formats page.
Course Name (Course Code) | Winter 2023 | Spring 2023 |
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Statistical Analysis for Data Science (DAT 200) |
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Data Analytics & Modelling (DAT 201) | Date: Sat, Jan 14 – Apr 15
Time: 9:00 – 12:00 p.m. Format: VC |
Date: Sat, May 6 – Aug 12
Time: 9:00 – 12:00 p.m. Format: VC |
Data Management (DAT 202) | Date: Wed, Jan 18 – Apr 5
Time: 7:00 – 10:00 p.m. Format: VC |
Date: Wed, May 3 – Jul 19
Time: 7:00 – 10:00 p.m. Format: VC |
Predictive Modelling & Data Mining (DAT 203) | Date: Sat, May 6 – Aug 12
Time: 9:00 – 12:00 p.m. Format: VC |
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Machine Learning for Big Data Analytics (DAT 301) | Date: Tues, May 2 – Jul 18
Time: 7:00 – 10:00 p.m. Format: VC |
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Data Analytics Tools (DAT 204) | Date: Sun, Jan 15 – Apr 16
Time: 9:30 – 12:30 p.m. Format: VC |
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Data Science Capstone Project (DAT 205) |
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