Data is useful in any industry whether automotive, electronics or broader manufacturing. But what about for sports franchises? With Super Bowl 50 in the rearview mirror, there were a few ways that IBM was able to put user data to work, both for sports franchises, and for the fans themselves.
Inaccurate perceptions of predictive analytics are common in the business world. In reality, predictive analytics is straightforward to understand, can leverage existing skillsets in business and IT organizations, and can deliver value in most industries and lines of business. Getting started with
Discover nine fields of study for corresponding data analysis professionals that contribute significantly to analytics projects, their key value propositions and challenges and high-level suggestions for categorizing and organizing the rapidly growing analytics resource pool.
Many of my waking hours are spent explaining to people that “big data” is not as opaque and mysterious a concept as they’ve been led to believe. To the extent that I can hold their attention for a detailed technical discussion, I can alleviate their concerns that it might all be smoke or mirrors or
We can argue till we’re blue in the face on the issue of whether a true data scientist must have academic credentials. But no one doubts that credentials mean little if you can’t actually do the work.
You can call yourself a data scientist in good conscience only if you can master the methodology.
For the past 2 months, a LinkedIn discussion group has been debating the burning question "Do You Need a PhD to Analyze Big Data?" Always itching for fresh chat, yours truly has stepped into the fray with a humble opinion or two. And I got flamed in no uncertain words. In fact, one PhD who didn't