Is your data out of shape? Why is it that you can never find the information you need? Surely you’re familiar with that sinking feeling. When you see a glaring error in the numbers, but you have no idea where it’s coming from. Your data lake is bloated beyond belief, so your team doesn’t know where to start looking. You’re now in panic mode. A looming report deadline, a shortly due presentation. How are you going to fix things in short order and maintain your credibility? Continue reading
Investing In Analytics – The Paradox Of Choice
I often get asked what industry trends excite me the most. Without doubt, it has to be the surge in amazing technologies that democratize solutions to complex data problems. Many of these are open source, or available on cloud platforms at the click of a button. This was unthinkable as recently as five years ago. I think it’s fantastic that all industries can now profit from the game changing work done by heavyweights like Google, Facebook and Amazon.
So where’s the problem?
Recruiting Lessons From The Imitation Game
Alan Turing a pioneering recruiter? You wouldn’t normally think of the father of modern computer science in those terms. It’s more natural to associate him with conversations on Artificial Intelligence – after all, he shaped our understanding of how computers solve problems, and how their ability differs from that of human intellect.
Where does recruiting fit in? Continue reading
Building a Data Science Team – A Moneyball Approach
In my last post, we saw how chasing unicorn data scientists can be an exercise in frustration. I made a case for providing data science training to your current team members who have the right domain knowledge and aptitude. But what do you do when you absolutely need to expand your team? How do you hire while competing against offers from tech gorillas like Google and Facebook?
Building A Data Science Team – Chasing Unicorns
In my conversations with companies looking to hire data scientists, I hear a recurring theme – “It’s so hard to find the right combination of industry background and technical chops.” Or. “We found the perfect fit. But there was no way we could match what Google / Facebook was offering, so we lost out. How do we build an effective team when the competition is so white hot?”
Building A Data Science Team – How to Hire The Right People
If you follow popular data science sites, you’ll notice an amusing trend. What do you think was the most popular post on KDnuggets in January? It wasn’t about the latest deep learning tools, or about Google’s recently released TensorFlow. It was: “20 Questions to Detect Fake Data Scientists”! The post clearly hit a nerve – I’ve seen similar questions crop up on many other forums. If you read some of the responses, you’ll see that people get very worked up about what makes a “real data scientist”. Continue reading
What Is Your Data Worth?
Industry commentators love gushing about how Big Data changes everything, and how it is “worth its weight in gold”. But is all data created equal? Where exactly does data get its value from? Continue reading
Big Data Science – Is Bigger Better?
There are a ton of data wrangling tools out there. But no one is asking the fundamental question – is bigger data always better? Continue reading
A Checklist Manifesto for Data Scientists
I recently read Atul Gawande’s ‘The Checklist Manifesto – How to Get Things Right’. It’s a gripping read. Dr. Gawande tells us how checklists can help manage complexity, Continue reading
Vijay Bhat 