No so long ago mathematics mathematics was very popular and allowed for the computer and Internet revolution, and progress in engineering sciences. Then statistics started to gain even more popularity (general linear model) until computers became much more powerfull. We now have fewer statistic jobs, and many are related to clinical trials (small data). Designing probabilistic model is not so popular anymore - although the term has been replaced by predictive modeling.
Then came data mining, then data science.
What will be next?
Taking a look at the history of Science itself, I would guess that the next things would be:
- Data Art (starting with Data Visualization, but then becoming more main stream and/or emotionally appealing)
- Data Engineering (probably needs a better name, but what I mean is products that have, as their original inspiration, the insights gained from data science. Kind of like how a scientist discovers a new phenomenon and then wonders how it could benefit people and builds a product around it. Of course, this is already happening, but still in its infancy, I think.)
- Data Politics (This would be my favorite - less rhetoric and more data, helped along by Data Art and Data Engineering.)
- Data Philosophy (or even Data Religion) This is too speculative, so I will leave it at that.
What about data axiom then build upon it with Data Art moving quickly into Data Engineering and just let it branch out from there to where ever it's needed/wanted. The Data axiom first, back to basics.
We should separate the science and the buzzwords. I think science of "data science" and "data mining" is essentially the same and continuously evolving (and a superset of predictive modeling, since not all data mining is predictive - there is also fraud detection, social network analysis, clustering, etc).
The "data science" and "big data" buzzwords are likely to change - see recent KDnuggets poll
where the leading candidates to replace "Big Data" were Smart Data and Big Analytics.
Good discussion here. I think the intersection of data and visualization is where we are all headed. The ability to leverage data in the design of products/services and business models is key. Apple is perfect example. Great design supported by data.
I agree with the visualization of data as it's the king of human senses, also I think the visual should be more interactive to the general public i.e. a cave man steps outside his cave and looks up to 'SEE' a boulder rolling down the hill towards him, he is not going to measure how big or fast the object is with tools he has not got or understand in anyway, he will use his instinct and just simply move out of the way.
I was surfing the web for my research on a data problem, happen to found this ...very few of the companies apply data science starting with a true definition of business problem and solving using data science techniques.. this looks promising ...do check
Which leads me to think of an interesting hypothesis... How does the presence of food deserts (absence of easily accessible grocery stores) affect Cardiac health from a geographic perspective? Is there an optimal distance between one's home and fresh fruits & vegetables?
Michael F. Clarke said:
In my view, tying data to a geospatial location might be next. This would mean mapping the result of the analysis.
For example, where are our Cardiac Surgery patients comming from? In what parts of our market are we gaining share? In what parts are we loosing share?
Being able to geolocate data and trends would be highly desirabel to marketers, and could propel the discussion forward.