This is Part #5 in a series of popular articles by Randy Bartlett, Data Scientist and Author entitled, “Statistics Denial, Best Statistical Practice.” Read More
I have seen it first hand many times as a recruiting specialist in the Data Science field. Companies leap into hiring a Data Science Team before they are 100% clear on the mission and it’s costly. Guest Blogger, Greta Roberts, CEO and Co-Founder of Talent Analytics Corp offer interesting advice on how to avoid this pitfall in the article “Dear HR: Stop Hiring Data Scientists Until You’re Ready for Data Science.” Read More
This is the post #2 of series of popular articles by Randy Bartlett, Data Scientist and Author entitled, “Essays On Statistics Denial” READ MORE
This is first of series of popular articles by Randy Bartlett, Data Scientist and Author entitled, “Preparing For The Coming Flood … Of Statistical Malfeasance:
— Identifying & Understanding Statistics Problems” READ MORE
Watch 2 Lectures on building Recommender Systems using Machine Learning given by the Director of Research/Engineering at Netflix at Carnegie Mellon University this Summer.
Its 2 parts at 2 hours each, touches on what is practical and important, and worthwhile if you have an interest in personalization and recommender algorithms. Watch Lectures
FREE eBook: “Analyzing the Analyzers” An Introspective Survey of Data Scientists and Their Work By Harlan Harris, Sean Murphy, Marck Vaisman Publisher: O’Reilly There has been intense excitement in recent Read More
Machine learning is a subfield of computer science and artificial intelligence that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions (wikipedia).
If you are thinking of doing or becoming a Data Scientist or Advanced Analytics professional, you will absolutely need to master Machine Learning. These 100 Most Popular Talks on Machine Learning topics are a great resource to learn. Review List
Amid the big data boom, the in-memory database market will enjoy a 43 percent compound annual growth rate (CAGR) – leaping from $2.21 billion in 2013 to $13.23 billion in 2018, predicts Markets and Markets, a global research firm Gartner.
What’s driving that demand? Simply put, in-memory databases allow real-time analytics and situation awareness on “live” transaction data – rather than after-the-fact analysis on “stale data,” notes a recent Gartner market guide. Here are 19 in-memory database options mentioned in that Gartner market guide. Read More
Read about 10 Big Data Case Studies | by NATHAN GOLIA, CHRIS MCMAHON
These 10 insurance companies developed cross-enterprise big data strategies, hired the right data scientists and staff members, and delivered impressive results. READ MORE