My commute to work is a 3-hour journey every day, with 1 hour on the train and half an hour on foot each way. Friends who visit me from the city always ask, “Do you commute like this every day? Aren’t you exhausted?” I thought this way too before moving out from the city, where I walked for only 10 minutes between home and school. But now, I am glad that I have 3 full hours of uninterrupted solo time every day, and the switch of environment between the fast-paced urban and the serene suburban life helps refresh my brain and reset my mind.

I prepare small “learning bento” before going to work, and spend the time on the train either watching MOOC (such as Coursera) and YouTube videos, or reading books and articles, and spend the time on foot listening to podcast or Audible. 3 hours a day adds up to 15 hours a week and 60 hours a month. And over the past few months, I have discovered a lot of great learning resources and would like to share with those who also like to have some “learning bento” for the mind while commuting on the grind.

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Disclaimer: This post represents my personal experience only, and has nothing to do with any organization or group. 

When I was a student, I read tons of articles on how to prepare for job hunting and how to get a job offer. Now, I am sitting on the other side of the table and start to participate in the talent acquisition process myself. This gives me a different perspective on the recruiting process. In particular, how I decide NOT to move forward with a candidate.  Here I am giving a few examples for new grads: how NOT to get a job offer. Follow this list, and you will likely NOT get an offer. So, try not to be a follower.

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“Which language do you use at work?” I get this question quite often. My short answer is usually “Python for research and Scala for production”. In this post, I will give more descriptive answers and examples to this question.

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Time flies. It’s been 7 months since I started to work as a full-time Data Scientist. It sometimes feels much shorter than 7 months: imaging neurons in the laboratory as a graduate student and walking on the stairs in front of Alma mater on campus was just like yesterday. It sometimes feels much longer than that: working is so vastly different from academic study, and I’ve learned so much on so many aspects of data science within this short-long period of time, that my mind and understanding of the industry and the world hardly resembles the graduate student me.

Here I am summarizing a few lessons I learned from work.

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My internship at Tapad has officially come to an end last week, after I gave a final presentation of my project (see the intro video below). It’s been a very memorable and rewarding summer. I not only learned about the latest technological development and application of machine learning and big data, but also got to experience the industrial work style and start-up culture.

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During my final round interview at Tapad, the VP of Data Science noticed my academic background in neuroscience, and we started to discuss how neuroscience research could contribute to online advertising. Later in the interview, he asked “Have you heard of reinforcement learning (RL)?” And I said no. He explained this reward-guided learning paradigm, which immediately reminded me of my undergraduate research on aversive learning in fruit flies. After the interview, he emailed me the link to RL Courses on YouTube taught by David Silver, the leading scientist of AlphaGo.

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After investigating a sample dataset using Python for a month on a local computer, I discovered some interesting patterns and optimized my algorithms to achieve a high measure of goodness. This week, I finally got the opportunity to run my algorithms on a much larger dataset – too large to be stored or processed on a single computer. Finally, it is time to Spark!

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