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How do scientific methods contribute to the development of modern science?

How much of the "data science" practice is really the development of new machine learning or statistics methods?

  • I understand that it requires the domain knowledge of your problem, programming skills, and machine learning/statistics familiarity, but I don't think that the data scientists usually need to come up with a new classification method or statistical hypothesis test. With tools such as R or scikit-learn I consider the data science as an "application" (and the selection) of existing mathematical etc frameworks in the task at hand. Is my understanding correct?

  • Answer:

    Most of the time Data Scientist face this issue of implementing all sort of techniques but the results are still bad. Then comes the innovation, building something new by either delta x improving the earlier algorithms, combining set of algorithms to work together or a totally new algorithm. So, understanding the problem and apply the best algorithm to develop a best data model is all asked by the Clients. Now, it is Data Scientist job to get it done and find some insights.

Ankit Sharma at Quora Visit the source

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Like my tagline says, I'll give you the straight truth: almost none of  your time will be spent doing that. In fact, 50-80% of your time will be spend on mundane "data cleaning" tasks. Now, that having been said, you'd better have a good understanding of each modeling technique you use. Scores of people just use their favorite technique, even if it's completely inappropriate for the business problem to be solved, use-case, and data involved. So no, don't be thinking that you're going to be doing a lot of novel research. But you *do* have to be a scientist and employ the scientific method. And you've got to know the pros, cons, assumptions, and pitfalls of the algorithms and models used by R, scikit-learn, and all the rest.

Mark Meloon

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