Data are becoming the new raw material of business
The Economist

Five Tips for Future-proofing Your Business with Data Science

entrepreneur-593352_960_720We all want to be future-proof: not just prepared for unforeseen developments but positioned well to take advantage of them. Having a flexible, adaptable, and scalable technology stack is a great way to get to achieve that goal when it comes to being able to leverage data science effectively. Here are five ideas I personally think it’s crucial to keep in mind when building out your own functionality:

1. Your pipeline is only as good as its weakest link.

It’s great that your predictive modelers have come up with a thousand new features to incorporate, but have you asked your data engineers how that will affect the performance of backend queries? What about your data collection and ingestion flow? Maybe your team is frothing at the mouth for an upgrade to Spark Streaming to run their clustering algorithms in real time, but your frontend will lose responsiveness if you try to display the results as fast as they come in. The key here is not to get sucked into the hype of “scaling up” without fully recognizing the implications across your entire organization and what new demands will be placed on all those moving parts. Continue reading

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The 3 Things That Make Technical Training Worthwhile

seminar-594125_960_720Managers understand that having employees who understand the latest tools and technologies is vital to keeping a company competitive. But training employees on those tools and technologies can be a costly endeavor (corporations spent $130 billion on corporate training in general in 2014) and too often training simply doesn’t achieve the objective of giving employees the skills they need.

At The Data Incubator, we work with hundreds of clients who hire PhD data scientists from our fellowship program or enroll their employees in our big data corporate training. We’ve found in our work with these companies across industries that technical training often lacks three important things: hands-on practice, accountability, and breathing room. Continue reading

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What Kind of Data Scientist Do You Need?

An article written by Data Incubator founder Michael Li was featured on Harvard Business Review today. It can be found where it was originally posted here.

 

question-mark-1872634_960_720If you’re looking to hire a data scientist to join your company, you’re not alone. At The Data Incubator, we work with hundreds of companies that are looking to find data scientists from our Fellowship Program. In our experience, candidates usually come from one of two disciplines: computations or statistics.

Candidates with a strong science or math background usually have had rigorous statistical training in distinguishing between signal and noise and can tell when they are “overfitting” a complex model. Those with a computer science background frequently have the software engineering chops to handle large amounts of data by taking advantage of parallel and distributed computing. While all data scientists need to be functional in both, we’ve found that people coming from each of these backgrounds have quite different strengths and weaknesses. So which type of background should you look for when hiring? That will depend on your business — and whether you’re hiring for a digital or non-digital department.  Continue reading

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The Enterprisers Project features HBR article: Retaining your data scientists

Yesterday, The Enterprisers Project covered Michael’s Harvard Business Review article “Retaining Your Data Scientists.” Take a look at their coverage below and at its original source.

 

businessman-2056022_960_720It is no surprise that data scientists are in high demand. “McKinsey estimated that we will be facing a shortage of 140,000 to 190,000 data scientists by 2018.”

Managers facing this shortage are going to need to be creative to retain the data scientists they have. In this Harvard Business Review article, Michael Li, a data scientist who has worked at Google, Foursquare, and Andressen Horowitz, and has held the executive director position of The Data Incubator, provides many ways companies can keep their data scientists motivated.

One example is allowing data scientists to install and use their favorite tools. Michael says, “Data scientists don’t exist in a vacuum. They are part of a greater universe of peers collaborating in a large open source movement. Many of the tools are open source. So allow your data scientists to install and use their favorite tools.”

Michael has many recommendations to retain and excite talent. Read his article to learn about the three main categories of keeping these employees motivated: support, ownership and purpose.

 

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The Two Questions You Need to Ask Your Data Analysts

An article written by Data Incubator founder Michael Li was featured on Harvard Business Review today. It can be found where it was originally posted here.

 

question-mark-1495858_960_720Data scientists are in high demand. McKinsey predicts a need for 1.5 million new data professionals in the U.S. alone. As these droves of analysts join organizations, it’s critical that they know how to talk with managers about their findings. But the burden for good communication doesn’t just fall on them. For their part, managers – the consumers of the analysis – need to ask the right questions to be sure they understand the key concepts behind data analysis.

At The Data Incubator, we work with hundreds of companies looking to train their workforce in modern data analytics or hire data scientists from our selective PhD fellowship. Our clients often ask us how they should engage with their newly trained or newly hired data professionals. Here are two critical questions we suggest they ask when trying to understand the results of any data analysis. Continue reading

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The Best Data Scientists Know How to Tell Stories

An article written by Data Incubator founder Michael Li was featured on Harvard Business Review today. The article can be found where it was originally posted here.

 

business-1989126_960_720When hiring data scientists, people tend to focus primarily on technical qualifications. It’s hard to find candidates who have the right mix of computational and statistical skills. But what’s even harder is finding people who have those skills and are good at communicating the story behind the data.

At The Data Incubator, we run a fellowship identifying the top 2% of STEM PhDs looking to work with our partner companies, which range from larger firms like Captial One or Genetech to smaller companies like Betterment or Yelp. Here are three attributes our partners look for in data scientists, and specific questions they use to identify those attributes: Continue reading

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3 Ways Scrappy Entrepreneurs Can Keep Data Scientists On Board and Motivated

On October 2nd, Entrepreneur Magazine featured another article written by Data Incubator founder Michael Li. The article can be found where it was originally posted here

 

entrepreneur-593378_960_720These days, there’s a lot being said about big data and the value that comes from properly utilizing it. I’ve written previously about the importance of having a data science team. The next goal is to figure out how to keep those data scientists happy.

At The Data Incubator, we’ve spoken to hundreds of companies looking to hire data scientists from our training program. They’ve ranged from large corporations like Capital One and eBay to smaller, nimbler outfits like Betterment, Upstart, and Mashable; and all have been eager for suggestions on how to retain their data scientists.

Even without the capital provided by a larger corporation, there are plenty of ways — most of them free — for scrappy entrepreneurs to keep their data scientists engaged and on board. Continue reading

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How to Get Past Buzzwords and Make Better Hires

On August 14th, Data Incubator founder Michael Li was featured on Fast Company. His article How to Get Past Buzzwords and Make Better Hires can be found below and here, where it was originally posted.


handshake-1513228_960_720In just about every industry imaginable, companies are exploring more data-backed ways of doing things. But there’s one field that remains stubbornly unscientific: recruitment and hiring. That’s not to say there aren’t procedures in place when it comes to résumé screening. For all but the newest startups, which have basically no precedents to abide at all, there’s usually some sort of protocol in place. The problem, though, is that the typical rules for screening candidates are driven by buzzwords that seldom identify real potential and too often play to our biases. Here’s what has to change and how to change it.

 

FINDING BIAS AND GETTING PAST IT

The reality that hidden biases abound in the screening process is well documented, but few companies realize just how pervasive the problem can be. A growing body of research shows we make a bunch of snap judgements starting just with people’s names—and continue from there. Continue reading

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Real data scientists have a rare hybrid of skill sets: Here’s what to look for

On July 18th, 2015, an article Michael wrote was featured on VentureBeat. The full text can be found below and where it was originally posted here

entrepreneur-593361_960_720
Over the course of the last year I’ve spoken with hundreds of employers interested in hiring data scientists – in particular, data scientists with advanced educational degrees. Many employers and hiring managers have heard that big data is the “hot new thing.” But as with all “hot new things,” there’s as much misinformation about data science as there are facts. Here are three misconceptions about big data and data science that I often encounter:

 

1. Big data is statistics and business intelligence with more data. There’s nothing new here.

This is a view often held by those with limited or no software development experience and it is plainly false. The perfect analogy for this is ice. Ice is just cold water right? There’s nothing new here. However, cooling down water doesn’t just change a quantitative property (temperature) but drastically changes its qualitative properties (transforming a liquid to a solid). Continue reading

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