Interview: Michael Li, Data Incubator on Bridging the Data Science Skills Gap between Academia and Industry
We discuss the response from hiring companies, recommendations for aspirants, retaining data science talent, advice, and more.

At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves.
First part of interview
Here is second and last part of my interview with him:
Anmol Rajpurohit: Q5. What has been the feedback from hiring companies?

AR: Q6. For the current PhD or Master's students aspiring to be a Data Incubator fellow, what would you suggest they focus on during their degree program?

ML: On the technical side, being a data scientist is about combining math and computer science. Having a strong background in mathematics and statistics is what allows you to interpret your findings from all this data. Having a strong background in computation is what will give you the tools necessary to manipulate all this data.
AR: Q7. Once you have hired the best data scientists, the next challenge is to retain them in today's super-competitive hiring market. What strategies do you recommend to retain elite data scientists?

AR: Q8. In the recent few years, we have seen sharp increase in the number of programs and certificates offered by universities in the field of Data Science. What do you consider as the indispensable components of any data science related academic curriculum?

AR: Q9. What is the best advice you have got in your career?

AR: Q10. Which of the current trends in Big Data arena are of great interest to you? What do you think would be the most significant developments in 2015?
ML: We see a lot of interest in unstructured data across many industries and migration away from slow batch-based analyses to real-time answers -- even for large datasets. We’re following this by emphasizing much more work with topics like natural language processing and online machine-learning algorithms.
