Bayesian Models

This blog post from Sean Owen, Director, Data Science @Cloudera / London

I was so glad to find David Donoho’s critical take in 50 Years of Data Science, which has made its way around the Internet. … Along the way to arguing that Data Science can’t be much more than Statistics, it fails to contemplate Data Engineering, which I’d argue is most of what Data Science is and Statistics is not.

Much as I enjoyed reading Donoho’s work, I think its important for people to realise that Data Science isn’t just a new take on applied statistics, a superset yes, but an important superset.

Some additional comments:

• Donoho like Breiman before him splits Statistics/Machine Learning into Generative versus Predictive modelling.  I never really understand this because near 40% of published ML is generative modelling, and the majority of my work.
• Other important aspects of Data Science we cover in our Monash course are:
  • data governance and data provenance
  • the business processes and “operationalisation” (putting the results to work to achieve value)
  • getting data, fusing different kinds of data, envisaging data science projects
• These are above and beyond the area of Greater Data Science (Donoho, section 8) that we refer to as the Data Analysis Process, and is probably the most in-demand skill for what the industry calls a data scientist.

Also, as a Machine Learning guy, who’s been doing Computational Statistics for 25 years, I also think its important to point out that Machine Learning exists as a separate field because their are so many amazing and challenging tasks to do in areas like robotics, natural language processing and image processing.  These require statistical ingenuity, domain understanding, and computational trickery.   I have important contacts both in the Statistical community and the NLP community so I can do my work.