What is your definition of DS?
By DS I mean a multi-disciplinary field that involves developing methods of recording, storing, and analyzing data to effectively extract useful information.
I believe for Data Science and analysis to be of value it will soon need to become more like natural language processing so that the end consumer of the analysis is required to define, format and create the analysis they need for decision making. DS should be able to identify the relevant elements, perform the analysis, create a consumable presentation and delivery the results to the end-user without the complexity in today’s solutions.
10 years from now we won’t be talking about DS as it would be so common place and entrenched within our lives we won’t be thinking about it. Every application would have some kind of data analysis in-built. No programming skills would be required to analyze these datasets as they would be using NLP. The world would be looking at something else as a buzz word.
If DS is defined to include methods for “effectively extracting useful information” one has to include machine learning in this discussion. Machine Learning (and by extension “Artificial Intelligence”) is a means of identifying patterns in data and creating models that can apply the same pattern analysis to new datasets.
These algorithms are becoming ever more refined and the computing resources to support the training of the algorithms are becoming cheaper all the time.
In 10 years ? The tools to analyse datasets and build visual dashboard and reports will be automated via ML techniques.
The basic tasks of data cleaning and transformation may be largely automated.
Large numbers of complementary and open data sets will be available to supplement your proprietary data.
Handling extremely large datasets will be easier and cheaper than today.
Everything will be done using on-demand cloud resources.
Access to data will be more important than the technology used to analyse the data.
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