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When it comes to big data insights, how do you know you’re asking the right questions? Hiring data scientists is a good start – we’re seeing their growth both on LinkedIn and at LinkedIn. But even data scientists are not immune from the myriad of hidden pitfalls that keep your key insights out of sight.
Drawing from a deceptively simple exercise that I’ve used to haze dozens of data scientists on their first day, I will discuss the good, the bad and the ugly lessons we’ve learned about asking the right questions, denominators and being a data skeptic.
Monica Rogati (@mrogati) is a leader in the field of data science. She built key data products and teams at Jawbone and LinkedIn; she is now a widely recognized advisor and speaker.
As the VP of Data, Monica built Jawbone’s data science and engineering team, focusing on developing data products that helped millions lead healthier lives. Her team also analyzed Jawbone’s data to derive novel insights about sleep, movement and food, then turned these insights into products, compelling data stories and interactive visualizations.
At LinkedIn, Monica was one of the early members of the data science team. She developed LinkedIn’s key data products for job matching and recommendations, and she doubled the effectiveness of the “people you may know” algorithm that drives the growth of LinkedIn’s connection graph.
Monica’s data stories have been published in the Wall Street Journal, New York Times, Time, NPR, and CNN. Fast Company recognized her as one of the 100 most creative people in business, and Fortune named her as one of the Big Data All-Stars.
Monica has a Ph.D. in Computer Science from CMU, where she focused on text mining and machine learning. She has eight US patents and has published numerous papers in top-tier peer-reviewed journals and conference proceedings. She is frequently invited to keynote industry and academic conferences.
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