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Data scientists and the Wizard of Oz have something in common: Few people really know what they do behind the curtain, which makes it hard to tell good from bad data science. These tips can help you discern the difference.
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Forgetting About The Science
Sound data science requires a scientific approach. Without it, organizations run the risk of making decisions that are based on faulty assumptions, bad data quality, weak models, and erroneous analysis.
"You have to be careful to use scientific techniques to attempt to eliminate bias and to accurately measure. The first thing you're trying to do is formulate a question. You have a thesis and you're trying to measure things. One of the most important and difficult tasks is to pick the right data to answer a specific question. [Also] the data needs to be of a certain standard quality because if the data is false, you're going to end up with bad results," said Michael Walker, founder and president of the Data Science Association. "One of the highest uses of data science is to design experiments, posing the right question and collecting the right datasets, and doing it all up to scientific standards. Then you gather the results and interpret it."