How to Create a 1-Year Data Science Self-Study Plan Using the Seasonality of Your Brain | by Madison Hunter | Jul, 2023

Brain functionality is affected by season, as much as it is by time of day. Annual rhythms of brain activity were studied in 2016 and were found to fluctuate depending on the season. The study found that the brain performed at maximum capacity in sustained attention tasks during the summer, but at minimum capacity in the same tasks during the winter. Additionally, the brain performed at maximum capacity in working memory (working memory refers to the memory needed for “planning, comprehension, reasoning, and problem solving”) tasks during the autumn, but at minimum capacity in the same tasks during the spring. While more studies are needed to solidify these findings, we can still use them to produce a one-year data science study plan that will use your brain to its maximum potential.

Winter: programming and data structures

According to the study discussed above, winter is a time when your brain isn’t exactly kicking when it comes to sustained attention tasks. However, that doesn’t mean that you can’t begin working your way through programming tutorials and becoming familiar with databases and data structures.

From experience, I can say that you shouldn’t be spending more than three hard hours a day learning to code or work with databases. There’s just something about learning to code that lends itself best to giving your all in two to three hours of lectures and then spending the rest of your time working on practice problems — which is typically where you do most of your learning anyways.

Now is the time to begin working your way through the lectures on freeCodeCamp to learn the basics of Python (and/or R), SQL, and maybe even some JavaScript.

Then, the remainder of your day should be spent adding to your own personal projects or completing Leetcode practice questions. The application process of coding is where you will learn the most. Writing code, running into errors, learning to navigate StackOverflow, and making corrections is what will solidify the concepts you learned earlier in the day.

Spring: data visualization

As the study mentioned above suggests, spring is a low point for your brain’s working memory — this means that it’s time for you to begin ripping through some data visualization concepts and trying to commit them as best you can to memory.

Data visualization can be considered the “coasting” part of learning data science and for good reason — you’re learning about accurate data representations, visualization types, and aesthetics. However, don’t be fooled into thinking that this stuff isn’t important. Quit the opposite. Data visualization is where you tell the data’s story, as well as give your predictions for the future.

You’ll want to work on establishing a workflow that makes sure you’re answering all the right questions before preparing your visualization: what is the goal of your visualization? who is your audience? how much information do you need to give in one visualization? how can you use colors and charts more effectively?

While you don’t know much in the way yet of data cleaning (that will come in the fall when you put everything together into your first full data analysis), you can begin visualizing some pre-prepared data thanks to the programming skills you developed in the winter. Check this list out for data sets you can use to begin building visualizations.

Summer: algebra, statistics, calculus

According to the study discussed above, summer is your brain’s optimum season for sustained attention tasks. This means that you want to tackle the hardest data science concepts during the summer. For most people, this means math.

The next three months are the time to break open the textbooks and tutorial videos on Youtube and begin mastering the topics of algebra, statistics, and calculus. These three areas of math are the ones that you’ll need for most general data science jobs (industry-specific requirements may require higher levels of mathematics, such as multivariable calculus, differential equations, and discrete math).

Professor Leonard is my favorite Youtube instructor for algebra, statistics, and calculus. He provides high-quality, full-length university lectures going from precalculus to differential equations. My only regret is not starting to watch his lectures earlier.

Fall: putting it all together — data analysis

Fall is when your brain is working at its maximum working memory capacity, which means that it’s time to put together everything you’ve learned in the past year and complete your first full data analysis.

Data analysis follows the steps of determining an objective for the analysis, collecting, cleaning, and analyzing the data, and finally interpreting the results and producing a conclusion. This will bring together everything you’ve previously learned, with the end goal of you being able to conduct the work of a real data scientist.

The goal here is not for you to be perfect. Heck, you’ve spent the last nine months learning the fundamentals behind data analysis — that’s not a lot of time. Instead, the goal is for you to methodically think through the steps involved in data analysis while applying what you have been able to learn in the previous year. You may not have all the answers, and there may still be some techniques that elude you from being able to produce the best analysis possible. However, you should have the basic skills necessary to draw some insightful conclusions from the data you’re working with.

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