Non-linearity and local optimum mindset

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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Not ready yet…Go!

I officially graduated from Columbia last Tuesday after my PhD thesis defense, and started to work full time immediately in the following week. This is the first week of my transition from academia to industry, and I already experience some culture shock in time management and prioritizing. Learning on the job is not only about the technical details, but also about the pattern of thinking.

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PhD转行之路 / Career paths for PhD in STEM

2021/8/14更新

关于上网课找工作的评论问题,我将不再回复。现在的工作行情跟我当时的情况已经很不一样,我对新生找工作的情况并不了解,大家最好问那些最近一两年刚找到工作的新生,得到更新的信息。

2020/10/02更新

我最近开设了一个YouTube频道,分享一些职业发展的心得和数据科学领域的知识。欢迎观看订阅!

写在前面

大概是免责声明吧,这里只是一家之言的心路历程,当然不是说这么做就一定会转行找到工作(那叫鸡汤文学),没有宣扬人人都可以做xx21世纪是xx的世纪,希望大家结合自己的实际情况,多跟业内外的人交流,多了解行业情况,对自己负责,切莫跟风:D

后续:工作一年半之后,我又写了一篇第一次跳槽的经验小结

1. 前言

拿到了人生中第一份工作offer!从无到有,从编程小白到数据科学。希望自己的经历能够给其他正在探索职业发展的同学们一点点启发。

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May the Force be with you

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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Take the audience on a journey

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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Time to Spark

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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Iterative learning

In my very first meeting with my mentor at Tapad, after an introduction to DeviceGraph and AdTech 101, we started to discuss the project I would be working on during my internship. The first week was very intense and my mentor understood my struggle in the face of information overload. He showed me his system of organizing information by writing down his incremental knowledge about certain technology as well as his spark of ideas. I adapted his system and developed my own archive to track my learning process and ideas using Google Docs (the picture above).  Now, 5 weeks have passed and I have continuously updated the documents as I learn: the more I know, the more I realize I do not know, and the more I want to learn. Learning is an iterative process.

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