Feynman Technique applied to re-leaRning Rkshitiz khanalBlockedUnblockFollowFollowingApr 20In my last post I shared my initial reflections on relearning R properly.
In this post, I will share my reflections on things I tried afterwards.
Particularly, I said that I would: 1) Note the things I don’t understand or need to understand more when learning.
2) Reflect on what I learned each session and write what I learned in simple words.
This approach bears similarity to a technique that Nobel winning witty physicist Richard Feynman is said to have used to master concepts faster.
The technique involves learning something, identifying gaps in your knowledge, and working on those gaps until you are satisfied.
Read more about the technique here.
I want to be decent at geospatial analysis in R, as I believe this skill can be really helpful in the way my career is going forward.
Following my last post, while trying exercises from the resources I am following, I have been doing the aforementioned things.
Whenever I learn something new or encounter something I don’t understand, I make a note about it as a comment in the script.
This has helped me improve.
The most important way in which it has helped is by making me more aware of the limits of my knowledge.
I know what I need to work more on.
I find myself thinking of the gaps in my knowledge and googling or discussing it with my friends.
I read somewhere that the more you engage with the learning material, as many ways as you can, the better you learn.
This has been one more way I engage with the concepts I am learning.
While following a set of tutorials, I came to learn about the importance of workflows.
I also began noticing data science and workflow mentioned in the same tweets/blogs more.
Another way I engage with learning materials is rewriting what I learn in my own words as comments in the script.
It makes me rethink what I learned.
This seems to make me remember the concepts more easily.
I did not follow a very structured set of materials in the last few weeks.
I also wanted to make sense of resources out there, try some of them and figure out what works best for me.
I have finalized two must-reads that I plan to finish cover-to-cover.
1) R for Data Science, by Garett Grolemund and Hadley Wickham2) Geocomputation with R, by Robin Lovelace, Jakub Nowosad and Jannes MuenchowI am currently in Chapter 4 out of 30 in R for Data Science.
No doubt I will continue scouring for other learning materials in addition to these.
At some point, I also want to try a mini-project.
Running scripts written by others won’t probably get me past a certain level.
Do my reflections resonate yours in learning anything, R or not R, in any way?.Please share.
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