How to Use Brainwashing Techniques From the 1950s to Learn Data Science More Effectively
A unique take on an old technique that will help you learn the toughest concepts in data science
Considering data science is one of the hardest things someone could aim to teach themselves, it only makes sense that no stone should be left unturned when looking for learning techniques that may speed or improve the process.
Therefore, we look to an unlikely place, the 1950s, for inspiration to help us learn data science more effectively.
A time of war, civil rights movements, and poodle skirts, the 1950s were not especially known for their data science learning techniques. However, one war-time torture technique has the surprising grounds to be a promising data science learning method that can allow you to effectively learn concepts in data science.
Background
During the Korean War, the United States government was shocked to find that American soldiers captured and held as prisoners of war in North Korean prison camps were confessing to all kinds of crimes they hadn’t committed. These false confessions were due to brainwashing techniques the North Koreans were using against the prisoners. The brainwashing process involved prisoners being forced to stand, food and sleep deprivation, solitary confinement, and recurring exposure to Communist propaganda.
Through the decades since the war ended, extensive research was conducted to understand the brainwashing process.
It was found that brainwashing has three main pillars:
- Tension followed by relaxation: exposure to highly stressful situations followed by relaxed situations.
- Repetition: saying the same thing many times over.
- Use of slogans: reducing complex ideas to a few simple words.
Brainwashing hasn’t just been used during wartime, but is used by many groups in society, including (but not limited to) governments, armies, political parties, and religious groups.
While brainwashing is normally used to create a docile, patriotic society, it can also be used as a practical learning technique to help you learn data science more effectively.
How to use brainwashing to learn data science more effectively
Brainwashing can seem like an outlandish scheme to help you learn data science more effectively.
Understandably so.
However, when you look at the three main pillars of brainwashing and use a little imagination, you can begin to see how these pillars can be advantageous in learning problematic concepts. In reality, the three pillars are very similar to what the data science learning process should look like. The initial data science learning process should involve learning demanding concepts followed by periods of mental relaxation where you review concepts you have previously learned, allowing the challenging concepts to ruminate in your brain. Then, the process should involve practicing what you have learned repeatedly until it becomes second nature. The process should conclude by simplifying what you have learned into easy-to-understand and easy-to-explain concepts that boil down vast amounts of information into a few key phrases.
If this brainwashing technique was powerful enough to have patriotic American soldiers confessing to crimes they didn’t even commit, then it’s likely a pretty good learning technique you can use to master data science.
Here is how to use the brainwashing process to learn data science more effectively:
Step 1: Tension followed by relaxation
Learning data science is a strenuous process that will present you with numerous concepts of varying difficulty from several different disciplines that you must master.
When learning new concepts and figuring out how to tie them all together, your brain is undergoing a stressful process that involves building new synapses, which are neural junctions that allow your brain to learn and use knowledge and skills. Every time you learn how to do something new, your brain builds new synapses that allow you to access and use the knowledge or skill. The brain can build stronger synapses when it has a chance to rest periodically between knowledge uses.
While this is a very simplistic way of looking at how the brain works, the main idea is that your brain needs rest in between periods of stress or tension for its synapses to become stronger. In other words, your brain needs to rest in between learning data science concepts so that it can better use the concepts it has learned.
This means that the ideal way to begin the data science learning process is to learn a concept or skill and then rest your brain after. This can be as simple as going to sleep after a day of studying or can be as extensive as taking a day in between learning two strenuous concepts. The break in between learning two strenuous concepts could be filled with working on a project that involves using old skills or studying another concept of data science that is in a completely different area. For example, between two days of studying machine learning concepts, you could take a day to look at some fundamental ideas of statistics.
The main goal is to give your brain time to solidify its connections to the knowledge you have presented it with before piling more information on top.
Step 2: Repetition
This type of brainwashing won’t have you repeatedly chanting Communist manifestos, but will instead have you repeating the skills you have learned until they become second nature.
The idea behind this phase of brainwashing is to integrate an idea into your being such that it becomes part of you. After this phase, you should be seeing machine learning algorithms when you close your eyes and thinking about statistical principles when you have a spare moment.
The best way to carry out this phase is to practice what you have learned with hands-on experience. Nothing quite solidifies what you have learned than by doing, therefore this is the point where you should be applying your theoretical knowledge in practical situations. This could mean working on mathematical problem sets, setting up coding projects, or testing out different visualizations. This is also a great time to work on technical interview questions that will incorporate many different areas of knowledge. Not only will this help solidify your skills through repetition, but it will also give you advanced practice on what to expect in a technical interview.
The key is to keep repeating what you have learned until it becomes natural.
Step 3: Use of slogans
The final pillar of the brainwashing process is the use of slogans, otherwise known as reducing complex ideas into simple phrases.
You will know when you have completely learned something when you can distill its essence into a few simple phrases that can explain the concept without using jargon or grandiose technical ideas.
Hence, the use of slogans. More or less.
To fulfill this pillar, it helps to teach a concept to someone else, preferably someone who has no prior knowledge. A rubber duck or a cactus will also work too. During the teaching process, make a note of any time when you need to use technical jargon or a textbook explanation to describe part of the concept. Then, at the end of the teaching session, make a note of any questions from your student that you are unable to answer. These notes will guide you in refining your explanation to a point where you can explain the tough concepts without using jargon and can answer all of the questions.
You may find that this step is similar to the final phase of the Feynman Technique. While it’s unlikely that the North Koreans ever got to pick Richard Feynman’s brain about mastering concepts, it’s still quite interesting how two very different groups understood the importance of simplifying complex ideas down into simple phrases to solidify the learning process. Again, more or less.
There are two great ways to work on simplifying your understanding of data science concepts into a few simple phrases: one, is to write blog posts teaching others the concept you have just learned; and the second, is to create a project shared on GitHub that provides an in-depth explanation of how it works and the principals it uses. These options are ideal because they allow you time to formulate a simple explanation for the concepts you are explaining and they open the door for others to comment, ask questions, and critique your work. Where your cactus may be a bit lean on the questions, people on the internet will always have something to ask about what you have written or developed. This not only allows you to further refine your explanation so that anyone from any background could understand it, but it also opens the door for you to help someone understand a concept they have been struggling to learn.
Final thoughts
Key takeaways
- Begin the learning process by working your brain hard, followed by periods of relaxation to allow your brain to strengthen its connections to what you have learned.
- Repeat what you have learned until it becomes second nature.
- Reduce the complex ideas you have learned into simple phrases that can be understood by anyone to ensure that you have truly learned the concept.
With data science being such a convoluted science to learn, it only makes sense that we get creative with how we learn to yield the best results. This means drawing inspiration from the 1950s to help us cement the problematic concepts of data science more effectively in our brains. While the North Koreans probably never intended their techniques to be used for anything other than indoctrination, we can certainly use their techniques in a beneficial way that will help improve the effectiveness of our learning processes.
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