This week’s roundup focuses on data science, the growing hype of it in businesses, and how strategic decision makers are implementing and outsourcing it effectively. We also explore the importance of accurately measuring data science outputs and discovering techniques on effectively managing a team of data scientists who find themselves with endless job opportunities.
by Nick Ismail, AI & Machine Learning Writer for InformationAge.com
Derek Lin, the Chief Data Scientist at Exabeam, provided his viewpoint into why artificial intelligence, machine learning, data science, cybersecurity are currently hot business-focused areas. Lin states how traditional, human-led programs are prone to errors and failures, because of human biases and lack of knowledge.
By Carl Dawson, Founder of finitesum.com featured on TowardsDataScience.com
With the hype of data science, many businesses are investing in and hoping to gain wisdom through key insights provided by data science solutions. Carl Dawson, the founder of Finite Sum, a data science consulting agency, suggests four key factors to focus on before you invest in data science solutions.
by Srishti Deoras, Senior Content Strategist for Analytics India Magazine
Recently, Pratham Hegde who heads the Marketing Science unit for Facebook India has 18 years of experience and specializes in marketing measurements sat down with Analytics India Magazine. In this conversation, he touches on the use of data science and analytics in marketing measurements at Facebook and how the role of analytics has evolved over the years.
by Kamalika Some, Data Scientist Writer for AnalyticsInsight.com
In organizations, executives are investing time into analytics projects. Analytics come in four distinct offerings, with each having its own parameters of deployment and each level supporting the next. Descriptive, diagnostic, predictive, and prescriptive are the four analytical offerings powering business enterprises today.
by Angela Ba Director of Data Science, iRobot featured on Harvard Business Review
Many managers of data science teams become managers because they were great individual contributors and not necessarily because they have the skills or training to lead a team. But management is a skill unto itself and relying on your experience as a successful individual contributor is not enough to ensure that you can retain and develop great talent while optimally delivering. If you want to retain great data scientists, you’d better commit to being a great manager.
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