How to Grow a Data Scientist | Analytics, Data Science & Business Intelligence - TDWI

Troy Hiltbrand, chief digital officer at Kyäni explains, With the advent of algorithmic business, data scientists are a critical resource for the organization of the future. Increased demand creates a tight labor market, forcing companies to look inward to develop their own data scientists.

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Across organizations, there exist individuals who seem to have a knack for data analytics. They might be part of your database team with deep expertise in SQL or part of your finance team who have mastered the art of doing amazing feats with just a spreadsheet. These are the individuals who businesses go to when answers to their questions are locked in the data. 

As algorithmic business becomes the norm and organizations start to see that their future viability depends on their ability to implement advanced analytics (such as machine learning and artificial intelligence), they scramble to find resources who can help with this transformation. As they look to the market, they find that people with mature data science skills are difficult to find, costly to recruit, and hard to retain. With all of the difficulty in bringing in outside data science resources to execute your analytics transformation, the answer might be to leverage these internal data experts and nurture them into the data scientists you need.
How do you mature these resources beyond queries and spreadsheets into the data science team that will transform your business?...

Statistical and Mathematical Thinking
At the core of data science is statistics. Machine learning is not usually about finding the right answer but finding the sufficiently probabilistic optimal answer to achieve the business goals. It is also about training the system to determine whether the best answer today is the same as the best answer yesterday or if the underlying factors have changed sufficiently to alter the analytics process. This process is often termed as a heuristic approach to problem-solving.

As a starting point, data scientists will need to understand the basics of statistics. As they grow and become more involved in deep learning and neural networks, they will also need to develop an understanding of linear algebra, tensors, and calculus. 
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Source: TDWI