To help meet the demand for data scientists I am thinking about a Masters programme. One essential question is what to call the program – Business Analytics? Predictive Analytics? Big data? Business Intelligence? I want to avoid being a fashion victim (remember all those e-commerce Masters programs that emerged in the early noughties – where are they now, now that e-commerce is simply business as usual?) and so won’t be using the word ‘big’ anywhere.
I also want to draw line between the old enterprise world of data mining, business intelligence, data warehousing, etc. and the emergence of the new breed of data scientist. While new programs in business analytics are being created in a response to the forecast shortage of data analysts (e.g., Arizona State’s new Masters program) an MSc Data Science feels an appropriate title.
Some design principles are needed before thinking about the content. I think that a MSc Data Science should:
- tackle real world problems, tackle complex problems, and be open to different perspectives, i.e.,
- be interdisciplinary (a collaboration of several disciplines in which concepts and methodologies are explicitly exchanged and integrated) rather than multidisciplinary (drawing on knowledge from different disciplines but each staying within the boundaries of its field)
- engage with the legal, ethical, and professional issues that arise from the practice of data science
- be designed in consultation with industry partners with a view to maximising the employability of graduates
- be intensely practical: students work with large (anonymised) datasets ‘donated’ by industry partners
- be delivered in block mode (3 and 5 day modules) to allow part-time and full-time study options and thus be open to practicing data scientists and others in employment wanting to develop their skills and employability
- have the option of distance/online delivery for reach and convenience
- have on-going practical relevance through guest lectures from industry partners and leading data scientists