Data-First Learning In Statistics

Grant program: Teaching and Learning Development Grant (TLDG)

Grant recipient: Luke Bornn, Department of Statistics and Actuarial Science

Project Team: Jack Davis, Elpedia Arthur Junior, Jacob Mortensen, and Steven Wu, research assistants, and Daria Ahrensmeier, Teaching and Learning Centre Educational Consultant

Timeframe: April 2016 to May 2017

Funding: $5,000

Course addressed: STAT 440 – Learning from Big Data

Description: The skills required to work as a statistician/data scientist in modern industry are at a disconnect with our current teaching methods. In particular, statistics courses are often taught in a methods-first approach, with data only entering the picture to support the teaching of methods. In contrast, in industry practitioners are faced with complex, real-world data alongside a business problem, and it is up to the practitioner to select the appropriate method or model. The goal of this project is to build a new course, “Learning from Big Data,” and to study how well it works for the students as well as the instructor. The new course inverts the traditional statistics learning model; by working directly with real-world datasets sourced from open sources and industry collaborations, students will build the skills to aid them in entering the workforce after graduation.

Questions addressed:

• Does the course design work on a day to day basis, from a practical point of view? For example, are students able to access and manipulate data? Are there any significant technical obstacles to overcome?
• Do the acquired data sets serve their intended purpose – to challenge students with problems akin to those seen in the real world and to guide them in achieving the intended technical skills?
• Does the competition/ranking system work as a teaching method?
• Are students obtaining the desired interpersonal workforce skills?

Knowledge sharing: The objective is to disseminate this teaching strategy to other statistics courses at SFU, as well as to the broader statistics community. This will be accomplished through seminars, publications in statistical education journals, and guest lectures at neighbouring colleges and universities.

Keywords: real-world data; statistical learning