We are trying to find a range of learning pathways in performance analysis and analytics at the University of Canberra.
Roland Goecke is working on a Masters in Data Science. It is two-year full-time course. At present the draft framework is:
- Introduction to Statistics
- Introduction to R
- Pattern Recognition
- Basic Data Visualisation
(If students wish they can exit here with a Graduate Certificate in Data Science.)
- Data wrangling
- Data Recording
- Advanced Statistics or Software Computing or Advanced Data Visualisation
(If students wish they can exit here with a Graduate Diploma in Data Science.)
- Research Methods
- Research Project Planning
- Domain Specialisation (Health, Finance, Sport)
- Research Project
Successful completion of the four semesters leads to an award of Masters in Data Science (with a domain specialisation such as Sport).
I am hopeful that all these pathways can offer microlearning opportunities too.
For example, the OERu course in Sport Informatics and Analytics has a topic on R:
OERu design protocols enable pages to be broken down into smaller components (sub-pages) suitable for microlearning opportunities. So for R this looks like:
I see these microlearning opportunities as discrete as well as cumulative. They can fit into no certification, Open Badge, Certificate, Diploma and Masters pathways.
I am looking forward to discussing with Roland and Jocelyn how our varying pathways might converge and diverge to give students on campus and on line the optimum opportunity to engage in self-directed analysis and analytics.
I am hopeful that my friends around the world might see opportunities to connect their pathways as open learning and fee-for-service possibilities. We could a most attractive map to offer … and negotiate.