Program Overview

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Data Science Program Overview

The Data Science Program is designed to help individuals take their digital data skills to the next level. This intermediate-level program is ideal for those who already have an introductory background and work experience in a data analytics-related field.

Program Highlights

• Intermediate courses to advance digital data skills
• Individual and team projects led by industry experts
• Earn a Certificate or Certificate of Professional Learning
• Open enrolment program

Program Features

The Data Science program features:

• Virtual classroom courses taught by industry experts who transform theoretical concepts into real-world applications
• Intermediate level of content, 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

Through the 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

The program utilizes a range of programming tools, including:

• 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

Program Overview

• Virtual Classroom: Approximately 7-9 hours per week, per course
• Certificate: 8+ months
• Schedule and fees available upon request
• Open enrolment

Who Should Take This Program

This program is suitable for:

• 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, healthcare, 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

Career Opportunities

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

Academic Learning Outcomes

Upon completion of the program, students will:

1. Identify a business problem and determine if, and how, an analytics solution is applicable
2. Translate a business problem into an analytics problem
3. Propose, and refine, analytical solutions to business problems
4. Collect, analyze, interpret, and share data
5. Identify relationships in data
6. Select problem-solving techniques and software tools to test analytical solutions
7. Employ common industry software tools
8. Identify, test, and evaluate model structures to apply to solve a business problem
9. Assess new and emerging technologies, tools, and strategies applicable to data science and related fields
10. Demonstrate an awareness of ethical practices and professional standards applicable to the field of data analytics
11. Exemplify the skills, attitudes, and behaviors required to work and collaborate with people and develop personal management skills
12. Employ effective communication practices

Certificate in Data Science

To earn the Certificate in Data Science, students must complete five elective courses from the listed courses.

Certificate 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
• Microsoft Excel: Basic knowledge of Microsoft Excel is recommended to be successful in the courses

Certificate of Professional Learning in Data Science

To earn the Certificate of Professional Learning in Data Science, students must complete three elective courses from the listed courses.

Certificate 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
• Microsoft Excel: Basic knowledge of Microsoft Excel is recommended to be successful in the courses

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.

Requirements

• Have an Ontario Secondary School Diploma or equivalent
• Be a mature student as defined in the Undergraduate Calendar of McMaster University
• Be deemed an exceptional case
• Knowledge and skills with general computer applications, such as keyboarding, file management, video conferencing, and word processing

Language Requirements

If English is not your native language, you must meet McMaster University’s English proficiency requirements by either qualifying for an exemption or passing a McMaster-approved English test.

Registration Process

To register for courses and programs:

• Visit the program page to find your program of choice
• Click on each tab 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
• Once you have added your courses, proceed to checkout
• Complete all required fields and select a program of study when prompted
• Payment is required in full to secure a spot in your course(s)

Payment Options

• 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

Data Science Schedule

The Data Science course schedules are available to help plan your academic year.

Schedule Details

• Registration: Fall and Winter terms open in mid-July and Spring term opens in mid-March
• Program Requirements: Review certificate or diploma tab
• Time Zone: Courses run in the Eastern Time (ET) Zone
• Course Fees: A McMaster Part-Time Student fee is included in the cost of academic credit courses
• Additional Fees: New students pay a one-time, non-refundable $39 activation fee for their first academic-credit course

Course Schedule

The schedule table is subject to change. Please visit the course pages to browse classes currently available for registration and the latest cost information.

• Statistical Analysis for Data Science (DAT 200): $803.82, 3.0 units
• Data Analytics & Modelling (DAT 201): $1,142.33, 3.0 units
• Data Management (DAT 202): $1,142.33, 3.0 units
• Predictive Modelling and Data Mining (DAT 203): $1,142.33, 3.0 units
• Data Analytics Tools (DAT 204): $1,142.33, 3.0 units
• Data Science Capstone Project (DAT 205): $1,142.33, 3.0 units
• Machine Learning for Big Data Analytics (DAT 301): $1,142.33, 3.0 units