Global education giant Pearson uses data science to make their products better especially for student outcomes. The video below features a recent Cloud episode of Google Developers Live, with Felipe Hoffa hosting Pearson’s Director of Data Science Collin Sellman, to celebrate Python Pandas release 0.13 and its Google BigQuery connector. Jacob Schaer and Sean Schaefer join them to demo its capabilities, and how Pearson uses data science to improve education.
As a practicing data scientist and big data journalist, I often find myself down in the trenches on pursuit of new trends, products, and services. Earlier this week I attended a local machine learning meetup group event and I came away with a real gem. The presenter mentioned in passing a new cloud service called “Domino” and I rushed back to my office to learn more. I wasn’t disappointed.
I’ve been monitoring a curious and lively discussion over on LinkedIn – “Is it necessary to have a Masters Degree to become a data scientist?” The comments I’ve seen have exhibited a number of points of view on the matter that I think are reflective of the questions on many people’s minds – both those wanting to become a data scientist and those wanting to hire a data scientist.
I recently got an e-mail with the salutation “Hi Data Scientist.” Pretty smart e-mail marketing campaign because, lo-and-behold, I am a data scientist and I actually was interested in the e-mail! It was from a company called DataCamp which I learned later used to be DataMind. I knew them from their R-Fiddle tool for learning R online.
Everyone’s talking about hiring data scientists but most pundits continue to focus on skills rather than the mindset required for this challenging role. Talent Analytics CEO Greta Roberts spoke about this topic to a group of data scientists attending the Boston area Big Data Analytics Meetup group on February 4th, 2014.
Paradigm4 is the company behind SciDB, a scalable array database with native complex analytics. CEO Marilyn Matz is an expert in the field of big data, after co-founding Cognex Corp in 1981. Marilyn has some interesting perspectives as to why Hadoop might not always be the correct choice for big data deployments. I recently caught up with Marilyn to discuss these views.