Data Science at LinkedIn

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Jonathan Goldman started working for LinkedIn in June 2006. The social networking website was growing well and had close to 8 million users at the time. Despite the growing number of users, however, something was missing. Professionals weren’t networking as much as executives at Linkedin wanted. One manager likened the experience of the website to attending a conference reception where you didn’t know anyone.

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1 Jonathan Goldman started working for LinkedIn in June 2006. The social networking website was growing well and had close to 8 million users at the time. Despite the growing number of users however something was missing. Professionals weren’t networking as much as executives at Linkedin wanted. One manager likened the experience of the website to attending a conference reception where you didn’t know anyone. Goldman held a PhD in Physics from Stanford. He was curious and possessed a bent for analytics. He remained focused on the networking problem and observed how users connected. Soon he was able to gather insights. His ideas were met with skepticism at the start. But Reid Hoffman – the company’s co-founder and then-CEO – backed him and encouraged him to wield the magic of analytics. Hoffman had experienced success with analytics in the past at PayPal. He gave Goldman a great deal of autonomy and freedom to test his ideas in the form of ads on the website’s most popular pages. The rest as they say is history. Goldman’s ads which tried to guess a user’s network worked brilliantly. It had click-through rates like the company had never seen. “People You May Know” ads became a regular feature 1 ACADGILD

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on the website. Goldman refined his suggestions using predictive models like “triangle closing”. The model recommended John to Sue if they had many mutual friends. Other factors that predicted connections included tenures at schools and workplaces. It gave Linkedin millions of new page views and made it a great platform for professional networking. scientists at Google for instance work to improve the search engine and ad targeting. At Zynga they work to improve the engagement rates of and revenues from games. At Netflix they try to recommend the best movies. And at Kaplan they work to evaluate learning methods. Acadgild helps in learning data science course online for all professionals and students who are willing to pursue their career in Data science. 2 2 ACADGILD

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