Data Analytics

Traits, skills and aptitudes

Those working in the data analytics field are innovative, methodical and directive.  

You need:

• an aptitude for and interest in statistics, artificial intelligence and databases
• an interest in applying scientific principles to solve practical problems
• the ability to analyze information and understand abstract relationships
• the ability to think logically, organize projects and carry them out
• the ability to work in teams as well as alone
• the ability to communicate with individuals from different technical and cultural backgrounds
• attention to detail. 

The ideal Data Analytics post-diploma certificate candidate has: 

• a previous post-secondary diploma or degree in business, economics or finance
• a strong math background and foundational education in statistics
• worked with data and is intrigued by the power of data and how it can be analyzed to support sound business decision-making.
  

Previous experience working with databases is an asset.

To do well in this field, you should enjoy synthesizing data, applying statistical theories and methods and working with others to understand and solve problems. 

Practicum, co-op and work integrated learning opportunities

During your capstone project, you’ll prepare a business case for an organization, complete a data analysis proposal, create a solution, and present the results to the industry partner or within a simulated professional context.  

Specialized intakes

This program is available to international applicants as a stand-alone program or as part of a program bundle - two programs delivered consecutively.

For details on available bundles including this program, see international bundle programs.

Credential

Upon successful completion of this program, you will receive a SAIT Data Analytics post-diploma certificate.

Program length

15 weeks to 1 year

Accepts international applicants - PGWP-eligible

This program accepts international applicants and meets the eligibility criteria for the Post-Graduation Work Permit program, as long as the student is registered in on-campus classes or completes more than 50% of the courses on campus (for blended options). 

International students attending all classes online will NOT be eligible for a Post-Graduate Work Permit. International students are responsible for maintaining their eligibility throughout their studies. 

Related careers

Our graduates may work in the following occupations. Some careers require additional experience and education.

Potential careers (NOC):

• Professional occupations in business management consulting (11201)
• Mathematicians, statisticians and actuaries (21210)
• Data scientists (21211)
• Business systems specialists (21221)
• Database analysts and data administrators (21223)
• Business development officers and market researchers and analysts (41402)
• Information systems specialists (21222)

Admission requirements - Applicants educated in Canada

Applicants must demonstrate English language proficiency and completion of the following courses or equivalents:

• a post-secondary degree or diploma from a recognized university, institute, or college.

A combination of education and experience will also be considered upon approval from the Academic Chair.

Admission requirements - Applicants educated outside of Canada

All applicants who were educated outside of Canada must demonstrate English language proficiency and provide proof they meet the program admission requirements with an international document assessment. Find accepted educational documents and assessment options. 

SAIT may also accept courses completed at certain international post-secondary institutions.

Program outline

The Data Analytics certificate requires 24 credits (eight courses) to complete.

The program can be completed:

• full-time in two 15-week semesters (regular stream),
• fast-tracked in one 15-week semester (accelerated stream), or
• part-time in the evenings across three 15-week semesters (part-time evening online stream).

Required courses

• DATA 401 - Data Literacy 3 Credits
• DATA 410 - Business Context for Data Analysis 3 Credits
• DATA 415 - Statistical Analysis of Data 3 Credits
• DATA 420 - Predictive Analytics 3 Credits
• DATA 445 - Business Analytics 3 Credits
• DATA 460 - Business Intelligence Reporting 3 Credits
• DATA 475 - Advanced Concepts in Data Analytics 3 Credits
• PROJ 406 - Data Analytics Capstone Project 3 Credits

Suggested schedule of study

Progression

You must attain a PGPA and/or a CGPA of 2.0 or better each semester and pass the prerequisite courses to progress through the program.

To qualify for graduation, you must pass all courses, attain a CGPA of 2.0 or better and complete course requirements within the prescribed timelines.

Review our grading and progression procedure > 

Books and supplies

Books and supplies are approximately $1,000 - $1,500 per full-time year.

This is a bring-your-own-device program with a power-user hardware and software requirement. See the specific requirements on our computers and laptops page.

Find your booklist on the SAIT Bookstore’s website. The booklist will be available closer to the program start date. Can’t find your program or course? The bookstore didn’t receive a textbook list. Contact your program directly to determine if they’re still refining course details or if you’re in luck; no textbook purchase is required this term.

Advanced Digital Technology program advising

For any questions or to set up an advising appointment, please contact our School for Advanced Digital Technology.

Phone: 403.284.8543
Email: sadt.advising@sait.ca 

Program outcomes

1. Manipulate data using data science, data modelling, Extract Transform Load (ETL), and ethical considerations to support decision-making in a business environment. 
2. Contextualize data in a format that maps to business processes and objectives, and that aligns data analysis to strategic outcomes.   
3. Communicate the results of their data analysis to a business audience using effective storytelling techniques, such as presentations, dashboards, live demonstrations, and technical reports.  
4. Analyze cloud-based and on-premises data using various tools and techniques.
5. Explain the applications of machine learning and artificial intelligence in the context of data analysis.
6. Apply industry-recognized tools to extract data and generate meaningful insights.
7. Demonstrate the ability to use data to support business processes at the strategic, tactical, and operational levels for evidence-based decision-making.
8. Apply data analytics principles, including statistical and algorithmic methods, to align data with business processes and generate actionable insights.