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Tell us: as a data scientist, what is your super power?

I read the question raised recently by the American Statistical Society (ASA) -   As a statistician, what is your super power - and after reading the numerous answers, all of them very geeky and not a single one mentioning producing yield, ROI, or added value, I could not resist to post this question on DSC, for data scientists. I could not believe that no one had any concern about delivering sustainable, scalable (automatable) added value, as opposed to beautiful charts and models. 

I believe that real data scientists care more about delivering value (at least solutions implemented as black box algorithms, such as automated arbitraging), as opposed to models. My answer to ASA's question was:

Delivering measurable added value in large amounts, and getting paid accordingly (that is, very well).

I believe that this is one important point that differentiates us from some statisticians and fake data scientists. Haters will claim that people like me are just using a bag of tricks to generate revenue and quality traffic. In my case, anyone can do what I do, just check out our data science apprenticeship program to learn some of those tricks. It can be done remotely, from home, and with no investment. Yet nobody has managed to successfully compete against us: companies like Gigaom burned $40 MM in VC funding, and went bankrupt; Kaggle laid off one third of its staff (they've received $10 MM in VC funding, and are managed by data scientists). The reason is simple: you simultaneously need a modern/efficient toolkit, domain expertise, automation, and business acumen to really succeed, to really be a data scientist. It is far more important than being an expert in 50 types of linear or logistic regressions. And it can be acquired after a few years of horizontal business experience.

Below is the answers to ASA's questions. What would be your answer to the question: as a data scientist, what is your super power?

Answer provided by ASA followers:

  • Abdelaoui Marwa Kadi: Statistics-mathematics
  • Amal Abdl Fattah: OLS
  • Amar Abas: time series
  • Aniqa Tasnim Hossain: The ability to deal with the people of other fields!
  • Cliff Claven: Being able to explain to subject matter experts how statistical methods will solve their problems
  • CuMhara O Called Kope: Figuring out how many animals we can afford, then back-calculating power for IACUC.
  • Eric Kawaguchi: The power to turn p-values < .05
  • Gavin Stewart: Quantifying ignorance in the face of obstinate certainty
  • Gibson Ugwu: Time series
  • Kel Zou Uniformly: Most Powerful (UMP) Tests! 
  • Lucas Bianchi: Time Series Travel.
  • Lynd Bacon: Patience.
  • Nouman Hussain: advanced probability
  • Pablo Chancalay: Help people to take good decisions
  • Raaj Kishore Biswas: The ability to change probability of any event!
  • Randy Ades: Not sure, skewed
  • Robbie Emmet: My superpower is Super P(Reject H_0 | H_a true)
  • Souvik Chakraborty: UMPU
  • Synchlavier: Sample Stochastic Process
  • Wasiu Biyi Sanyaolu: Regression heart emoticon
  • XLSTAT Software: An experiment with a beta risk of 1%
  • Yaw Desmond: how fast your mind works
  • Zakaria Benmazou: The ability to try predicting the future with less mistakes I think.

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Comment by Chad Bryant on July 24, 2015 at 8:54am

The ability to extract and combine relevant data from the noise (i.e. the massive amounts of data that are not related) and present that data in such a way as to be simple and straightforward for the layperson all the way up to other data scientists.

Comment by Eeshan Chatterjee on July 5, 2015 at 12:12pm
To abstract business needs from business wants, and and design data solutions to address the needs before looking at the wants.
Comment by Rana Usman on July 4, 2015 at 6:18am

Analytical Heart with Speedy Brain to Find superficial patterns.

Comment by Max Galka on July 3, 2015 at 8:06am
The ability to stare at numbers a computer screen for 14 hours straight and enjoy it.

Fully agree on the importance of keeping the purpose in perspective. For people that enjoy statistical / data analysis for its own sake, it is easy to fall into the trap of thinking that is the end goal.
Comment by Alastair Muir on July 2, 2015 at 3:27pm

To see data where others don't

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