Innovators

Everything you wanted to know about data science but were afraid to ask

Emi Tabb

Many of us have come to expect that our favorite retailers, personal finance apps, media outlets, and social media platforms will treat us like individuals, predicting our preferences and anticipating our needs. To deliver these personalized experiences – and avoid disruption from startups – large companies have bet big on interpreting customer data.

But most organizations don’t have the technical chops (or time) to analyze that data, let alone use it to build targeted engagement strategies. To fill the gap, companies have started to compete for data science talent.

It’s a battle that’s nearly impossible to win. The number of open data science and advanced analytics positions has increased by 650 percent since 2012 and will grow by an additional 28 percent by 2020. With only 35,000 people in the US with data science, talent can have their pick of companies.

In the scramble to find data science talent, few take the time to answer the necessary questions to set their teams up for success: Would a data scientist serve our needs best, or do we actually need a statistician or analyst? If we do need a data scientist, what could we reasonably expect them to solve for us? And what kinds of interesting problems do data scientists actually want to solve? What are the top companies that made big bets on data science? What was the payoff?

Over the past year, we found answers to these and other questions from experts who cut through the information overload and delivered clear, informed perspectives on the state of data science. Here’s the primer.

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