Data Science

Data Science is an interdisciplinary undergraduate program at SFU involving coursework in four different areas: statistics, computing science, mathematics, and business. The program was designed after consultations with industry, analysis of employment trends, and faculty research in upcoming data technology. The result is an applied, skills-based program that gives students the tools and abilities they need to gain employment and be successful in the area of data science.

How to Apply

Students must formally apply to be admitted into the program. Applications can be considered both for students entering SFU and for students already enrolled. Admission into the program is decided on a competitive basis. Students must maintain a 2.7 CGPA in DATA program coursework to remain in the program and to graduate. It is strongly recommended that students contact the Data Science Advisor ( early about admission and scheduling.

About Data Science

Technological advancement has led to an exponential proliferation of data. With every click of a mouse, entry of text, scan of a card, etc., more and more information is being generated all over the world. As data solidifies its role as the currency of the future, data science is needed to unlock its value. Simply put, data science is the science of data. It is a means of discovering valuable insights from massive stores of data. Data science is inherently interdisciplinary: a fusion of statistics and computing science, with an underpinning of mathematical principles and end goal of clear communication of findings. Aims of data science include optimizing operations within an organization, enhancing product/service features, describing trends, evidence-based decision-making, and predicting outcomes.

Some well-known innovations and product/service enhancements that have come from Data Science include:

  • Autocorrect
  • Refined Google searches that can filter and customize your search results
  • Software/websites that make predictions about what type of music you may like (e.g., Spotify) or what books you might like to buy (e.g., Amazon)
  • Speech recognition products like Siri, Cortana, and Google Voice that allow you to convert spoken word into text
  • Self-driving cars
  • Price comparison features on websites to help you shop for the lowest price
  • The optimization of airline operations by predicting flight delays, route popularity, customer preferences, and parts repairs
  • Fraud detection

In all of these examples, data scientists used statistical data analysis and computer programming to improve a process/product/service. In general, data scientists use the scientific method—make observations, formulate hypotheses, and conduct experiments to test them—to “liberate and create meaning from raw data”.

Technological advancement has yielded promise of achieving great new heights—e.g., improvements in health care, game-changing innovations and entrepreneurial opportunities, and market optimization. However, recent research in Canada and the United States has highlighted a “talent gap”—a shortage of trained data scientists. It has been projected that tens of thousands of skilled individuals will be needed in Canada alone to be able to analyze and harvest value from the flood of continually incoming data.

SFU’s Data Science Program

Recognizing the demand for data scientists, SFU has created one of the first undergraduate data science programs in Canada. Coursework for the program was carefully chosen to match common skills requested by Canadian data scientist job advertisements, talent shortages described by employers, and skills deemed important for future technologies by leading researchers. While other data science programs in North America generally involve coursework in statistics, computing science, and mathematics only, SFU’s program additionally includes business coursework to meet the job demands of business and industry. Our program was designed with recognition that data scientists not only need to be proficient in computer programming and quantitative analysis but also must be strong communicators with business savvy and teamwork skills.

The Data Science program entails two capstone projects—one at the beginning of the degree and the other towards the end of the degree—to give students the opportunity to use their analytical skills to provide solutions for real-world problems.

Acquired Skills

The Data Science programs provide training across a range of sought-after skills and competencies, including:

Software engineering R
Data acquisition and warehousing Python
Statistical modeling and analytics C and C++
Regression HTML/CSS                      
Data visualization and presentation
Experimental design and survey sampling        SQL
Mathematical modeling  
Critical thinking  
Business communication  
Customer analytics  
Project management

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