15 Principles for Data Scientists

I have developed 15 principles for my daily work as a data scientist. These are the principles  that I personally follow :

1- Do not lie with data and do not bullshit: Be honest and frank about empirical evidences. And most importantly do not lie to yourself with data

2- Build everlasting tools and share them with others: Spend a portion of your daily work building tools that makes someone’s life easier. We are freaking humans, we are supposed to be tool builders!

3- Educate yourself continuously: you are a scientist for Bhudda’s sake. Read hardcore math and stats from graduate level textbooks. Never settle down for shitty explanations of a method that you receive from a coworker in the hallway. Learn fundamentals and you can do magic. Read recent papers, go to conferences, publish, and review papers. There is no shortcut for this.

4- Sharpen your skills: learn one language well so you can be called a pro. Learn other languages good enough to be able to communicate with others. Don’t forget, SQL is like English, it is spoken by every moron on this planet but if you master it you can make beautiful poetry. Learn a compiled language, an interpreted language and R. Or just learn R! It is ugly but it will give you an edge. And fuck Matlab, you are not an undergrad anymore. Learn Unix, even if you use Windows, learn sed and grep and all that. You can do wonders with bash and powershell. If you want, learn how to use Hadoop too but know that it is a crappy system.

5- Know that a data scientist has one purpose in life “Kick ass and amaze people”: Do one thing every day related to this

6- Challenge yourself often, by presenting your work to others. Do not be scared of a few douchebags who might criticize your work. Crush them, If you wanted to be scared of cockroaches you could have not even walked!

7- Be generous with your knowledge and Don’t be afraid to ask questions: some people are insecure about their knowledge and do not share it, forgive them but do not be one of them.

8- Develop some ideas first and then listen to other people’s insights, utilize what they know about the domain but do not restrict yourself to that: If they could solve the problem with what they knew the wouldn’t come to you for a solution.

9- Hang out with people, talk to them, learn how you can be useful in their projects and how their work can benefit your projects

10- Build impressive and interactive user interfaces for your bland codes: Code is our language, let your code shine with a UI.

11- Use visualization efficiently, avoid hard-to-understand graphs: The only purpose of visualization is to make data understandable not confusing

12- Learn about new technologies and strive to understand the fundamentals of classic technologies

13- Over promise and over deliver: this is how genius people work. Do not be scared of proposing creative ideas. Have you heard of “under promise and over deliver?”   that’s how shitty cubicle rats work. Don’t be one of them.

14- Stay Creative and Focused: you can win with creativity and focus (caffeine can help here but do not overdo it)

15- Be positive, work hard and if anyone wants to stop you just crush them

~ by marksalen on June 2, 2013.

9 Responses to “15 Principles for Data Scientists”

  1. I’m definitely with rules number 3 and 4. The data science field is so vague right now cause every boss seems to think of it as somewhat all encompassing. One way to overcome the expectations is just to have a great understanding of your part of the field.

    My quote to live by is to do your job so well that no one be they dead, living or yet unborn can do it better.

    Oh yeah and I just hate Matlab. Learn r or python

  2. Reblogged this on Sergey Tihon's Blog.

  3. super like :)

  4. Nice points! inspiring!

  5. I don’t think scientists of any flavor should waste their time making guis. The end result typically isn’t good for experts or non-experts.

    • I wholeheartedly disagree, the user interface that I am referring to here is for data exploration, drill down, getting more insights and allowing the scientist to come up with novel ideas. For example compare tools like R (command line) and Tableau, I think any data scientist should spend time building custom UIs for himself or others to be able to work with the data more fluidly.

  6. Wow, I love this list. Both inspiring and practical.

  7. […] read it in a blog post, and mulled it over a little in my mind. I’m always overpromising. I always say Monday, for a […]

  8. […] http://openresearch.wordpress.com/2013/06/02/15-principles-for-data-scientists/ […]

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