Methods that helped in learning R
This post is going to be an extended version of “About me”, how I think, solve issues and how I learned R language on my own. Having finished my Bacholers degree in Industrial Engineering and putting a stop to my E-sports career, I decided to re-ignite my interest in data science from my previous work. I had zero experience in coding and moderate statistical knowledge.
Since I have stopped working, I built an 8 hour schedule per day rule of studying R and I stuck to it, I tried to learn everything, even if I did not fully absorb the material I would power through, however frustrating it might be, it will be worth it when the “light bulb” in your head switches on , and you start understanding the topic better.
By no means this was easy, but having a thought out workflow makes it less hard. In no particular order, these methods aided in my journey in becoming a data scientist.
DataCamp
This was my stepping stone into the world of data, at first it might be intimidating when you are greeted with 100+ courses from different programming languages, but DataCamp offers track series such as “Data Scientist with R” and “Data Visualization with R” which are very beginner friendly, the tracks contains interactive courses that are designed to be in an order that is logical to understand. This could be different for some people, but I have noticed that retaking courses would provide insight that I have failed to catch on the first time.
Social media
I wish I knew this when I began my journey in R, there are days or even weeks that I did not code out of frustration (I do not recommend this at all, I was rusty when I got back). I found that viewing other communities work on topics that interest me would get me out of my slump. Following hashtags and connecting with data scientists on LinkedIn, and following data scientists on Twitter, are platforms that gave me a whole new funnel of information that I never had.
To-do list / bookmarks
Continuing on the previous method, after finding attractive topics I would add it to my simple Notepad To-do list that would remind me of my priorities as my memory is not the strongest. I would make sure not to remove anything until I have fully understood it. As well as bookmarking certain topics that were of interest and aggregating them based on their general topic.
Mentors
“Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” -Josh Wills, Director of Data Engineering at Slack.
Having strong knowledge in both domains is essential in data science.
In my opinion, he provides the best screencasts on his youtube channel, explaining his thought process when engaging in a dataset that he had never seen before. Desciplines such as EDA(Exploratory Data Analysis), data visualization, machine learning, statistical analysis and much more. You can find other peoples code on the internet, but I have yet to see a platform where the thought process is described along side the code as good as his.
Provides introductory statistics tutorials that are full length lessons, I enhanced my statistical knowledge due to his outstanding lessons.
Saving functions
While taking courses, watching screencasts or discovering new functions, I would mimic the code that was written and save the workflow of these functions, to have a base line to look back to when something goes wrong. Aggregating them based on functionality would direct me on where to look.
Example of an aggregated file, Visualization tips code is provided.
These methods helped me tremendously increase the rate of learning, I hope it can help you as well!
Future posts will be more technical work that contains EDA,modelling, data visualization and machine learning.