Many a blogs and articles are written on how to become a Data Scientist. The list normally goes like this
- Study descriptive statistics, hypothesis testing, probability
- Learn types of Machine learning algorithms – Supervised, Unsupervised
- Learn Python, R, SAS, SQL
- Apply machine learning techniques using Python, R, SAS
- Learn Data Visualization
While there is nothing wrong in the path illustrated above, it is not the sufficient way to become an efficient data scientist. Now you might ask WHY? Before I answer that, I want to talk about the ‘Feynman Technique’.
Why is the technique called ‘Feynman Technique’?
The technique is named after the great theoretical physicist Richard Feynman. He was nicknamed the ‘The Great Explainer’ for his remarkable skill of explaining even the most complex scientific topics in plain layman language.
The Feynman Technique:
Step 1: Narrow down on a topic which you find difficult to grasp. Learn about the topic.
Step 2: Explain the topic as though you are teaching it to someone in very simple terms
Step 3: Work through examples or demonstrate how it works
Step 4: Assess your knowledge about the topic, if still some concepts are unclear, learn more about them and repeat steps 2 – 4
In due process you would have developed a better understanding of the topic than you started with. Now that is the magic of the ‘Feynman Technique’
Become a ‘Great Explainer’ to become a Great Data Scientist
The Data Science domain requires constant learning. Some of the concepts might be too hard to comprehend. Feynman technique can help one to understand topics which one thought was incredibly difficult.
The need to explain to Boss, Clients or VC’s
The Analytics industry will survive only if the key decision makers see value in it. The decision makers are
Your boss – if you are having a in house analytics set up
Clients – if you are in the analytics consulting / services business
VC’s – if you are seeking investment for your ‘AI Start-up’
Most often than not your boss/client/vc’s may not have analytics background or a deep understanding of the latest analytics topics. The oneness is upon you to explain the analytics concepts in as simple as language as possible so that they see value in your proposition.
So, bottom line – Practice your Feynman Technique lest you might face the same ordeal Dilbert faces everyday with his boss as depicted below.
How I became a Data Scientist
During my MBA course, I was the only person with a statistics background and I always felt that my understanding of a statistics concept got better as I explained it to my friends. Their approval of having learnt the concept easily, gave me encouragement and an added responsibility to learn the concepts thoroughly myself so that I do not teach them wrong.
The confidence of having learnt something thoroughly allowed me to get in to the Data Science field. Even now I still follow the Feynman Technique to get a better grasp of topics which initially seem incomprehensible.
Feynman Technique in Practice – Write articles
Well I must confess, I wrote my first article about “Recommender Engine” in order to develop a better understanding of how the recommender systems worked. While I don’t claim expertise in recommender systems, I can surely say I learnt something intuitively.
Similarly in my last article “How to dockerize an R shiny app”. I have tried to explain dockers through Legos !!
Feynman Technique – A remedy for impostor syndrome
As the Data Science field has become lucrative, many want to break in to this field. Those who succeed gaining entry (without a stat/math) background, are sometimes left with an impostor syndrome. As the image depicts, “you are the easiest to fool”. The only way to get over the impostor syndrome is to really develop a strong understanding about the various Data Science topics and what better way to understand topics deeply than the Feynman Technique.
Remember: Become a Great Explainer to become a Great Data Scientist!!
If you liked me article, give it a like and you can also comment below your opinions about the article.