How I Leveraged the Feynman Technique to Teach Myself Data Science

Learning data science can be a daunting task and very overwhelming if we don't have a plan.

One day I heard about the Feynman Technique, named after the renowned physicist Richard Feynman.

It involves breaking down complex topics into comprehensible explanations that even a beginner can grasp.

By teaching these simplified explanations to someone else (or an imaginary student), you reinforce your own understanding and identify gaps in your knowledge.

This iterative process of simplification and refinement forms the cornerstone of effective learning.

1. Step 1: I Learned the basics intuitively
2. Step 2: I taught as if explaining to a novice
3. Step 3: I identified gaps and refine
4. Step 4: I simplified further and repeat
5. Step 5: I did practical application and practice
6. The rewards of the Feynman Technique in Data Science learning
7. Conclusion

Data science is built upon a foundation of mathematics, statistics, and programming.

First, I focused on intuitively grasping fundamental concepts before diving into technical details. This is important to avoid being overwhelmed. For instance, I tackled probability by imagining a simple coin-flipping scenario, connecting abstract ideas to the real world.

This is perfect because we can do that in real life. Then, I wrote down these concrete examples to know to forget the context of these complex concepts. You can write, and even draw! I prefer the second technique to teach.