Summary: Picking an analytic platform when first starting out in data science almost always means working with what we’re most comfortable. But as organizations grow larger there is a need for standardization and for selecting one, or a few analytic tools.
Summary: Want to win a Kaggle competition or at least get a respectable place on the leaderboard? These days it’s all about ensembles and for a lot of practitioners that means reaching for random forests. Random forests have indeed been very successful but it’s worth remembering that there are three different categories of ensembles and some important hyper parameters tuning issues within each Here’s a brief review.
Summary: Proof of Concept projects are a popular place to start but they may be the wrong solution. To ensure success focus on Proof of Value and alignment with the company’s strategy. Get the right executive sponsor and keep them involved.
Added by William Vorhies on May 16, 2016 at 9:17am — No Comments
Summary: Continuing from out last article, we searched the web to find all of the most common myths and misconceptions about Big Data. There were a lot more than we thought. Here’s what we found. Part 2.
Summary: We searched the web to find all of the most common myths and misconceptions about Big Data. There were a lot more than we thought. Here’s what we found. Part 1.