Launching and Scaling Data Science Teams: Project Summary

I used these conversations to write three best practices guide for launching and scaling data science teams and careers: one for data scientists, one for data science management, and one for business stakeholders dependent on a data science team..But, to understand today’s problems with data science teams, we have to understand how we got here.The Rise of Data ScienceThe rise of “Data Science” has been rapid and well documented..According to Google Trends, the term started to take off in 2012, which is also when Harvard Business Review named “Data Scientist” the sexiest job of the 21st century..A modern company that throws off millions of data points must have a data science team, and there exists a universally agreed upon shortage of talented labor..The HBR article describes the challenge in 2012:If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive..None of those tasks is as straightforward as it is with other, established organizational roles..Start with the fact that there are no university programs offering degrees in data science..There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.Obviously much has changed: six years later, there are now 160+ university degrees with “Data Science” in the title..But many of the challenges and opportunities remain the same..A data scientist can still come from anywhere..The process to become a data scientist is truly accessible in a way very few desirable/high-paying jobs are..In addition to the paid degrees, there’s Andrew Ng’s Coursera machine learning course, classes by MITx, HarvardX, ColumbiaX, or UCSanDiegoX, physical and web-based textbooks, Youtube channels, personal blogs, company blogs, and more.The litany of educational options by price point and diversity in background of data scientists leads to a wide range of skill sets, interests, and problem solving styles.. More details

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