One of the most important things I’ve learned since I started studying data science is that your learning resources can make or break you.
For example, I knew that I would need to have a grasp of calculus to carry out many calculations found in data science. Coincidentally, I had to take a calculus course as part of the requirements for my university degree. Since I was having to pay for the calculus course, I decided to use it to teach myself the calculus I would need for data science. However, the learning materials from my university were so atrocious that it took me five months to learn functions, limits, and differentiation. It was soul-sucking. That is until I found the best math teacher on Youtube. Professor Leonard’s calculus lectures were life-changing, and I found myself able to teach myself calculus through these videos in record time compared to when I was using the materials my university had provided.
To keep your motivation to self-study strong, you need to use resources that are helping you learn at pace, instead of keeping your wheels spinning for weeks on end trying to understand a concept. Nothing will kill your motivation quicker than being stuck trying to understand a concept for longer than one month.
There’s no reason to stick with a learning resource if it’s not doing its job. Luckily, the internet is so incredibly full of data science learning resources that you have many options.
For example, many people have had great experiences learning data analytics through the Google Data Analytics Professional Certificate that became popular in 2021. This self-paced course is designed to keep students moving forward by using extremely well-designed learning materials that allow you to complete the program in less than 6 months with 10 hours of study every week. Codecademy is another learning resource that has also had great success in helping people learn to code with their easy-to-follow and digest modules that keep you moving forward in your studies without getting stuck.
In sum, there’s no good reason why you should stick with a learning resource if it’s draining your will to live by not being conducive to moving your studies forward. Self-studying data science should always be a form of forward progression. Yes, the forward movement may be slow at times, but there should never be a complete stop or a reverse of direction — there are too many different learning resources out there for that to happen. All you need to do is be able to admit when something isn’t working and change tactics to something that will.
It’s weird how something as simple as studying along with someone, even if they’re halfway across the world, can be so motivating.
Online study spaces, “study with me” videos, and Discord chats have seemed to take off in popularity over the last three years, with many of these resources hitting thousands of viewers and members every day.
One of my favorite study channels on Youtube is run by Merve, who also coincidentally studies data science. The channel has 822k subscribers, and posts “study with me” videos seen by millions of viewers each week. There’s just something so inspiring about “studying” with someone that also helps keep you motivated.
Study Together, StudyStream, and Studyverse, are all virtual study rooms where you can study with people from all across the world. These study rooms can help bust procrastination and keep you focused for hours at a time. Additionally, many study accounts on Instagram are using the broadcast and live features to host study sessions for all of their followers to tune in.
The other top tool to keep yourself motivated is to join a Discord server, especially one dedicated to the different aspects of learning data science. These communities are great opportunities to keep yourself motivated to study, but also to get your questions answered immediately when you get stuck on a topic. Communicating with like-minded people is also a great way to learn more about the data science industry, network, and become a more well-rounded data scientist in the future.
When you’re self-studying data science, it can be difficult to determine exactly how much you should be studying every day. This can be compounded when you don’t have other commitments, which can leave your entire day open to studying.
This is also further affected by how much you see others around you studying. Social media has made the toxic study culture even more prevalent, with many people posting about how many hours a day they study. This can put unnecessary pressure on you to also be studying 12 hours a day.
While the amount of time that everyone can study effectively is different, I can attest to the fact that you should not be studying for any longer than 6–7 hours per day. Studying is an intensive form of brain use that is completely different than how you would use your brain working an 8-hour-a-day job. For example, working 8 hours a day does not mean that you’re using your brain intensively for all of those 8 hours. Some of those hours will be spent on energy-intensive tasks, but for the most part, your day will be spent using your brain less intensively, such as going to meetings, answering emails, and taking breaks.
Comparatively, your brain is being continuously worked hard while studying. Studying requires 100% of your concentration to do it effectively (especially when you’re exploring topics such as calculus and neural networks), which is why studying for 6–7 hours should be your maximum goal every day. This also takes into account the fact that you need to take care of yourself in other ways during your study day, including rest, socialization, exercise, and nutrition.
When your brain learns that it only has to work hard for up to 6 hours a day, you’ll likely find that it becomes easier to focus for those 6 hours. You’ll no longer feel like being distracted by your phone because you know that you’ll only have 6 hours to get through your learning tasks for the day. You’ll also find that you feel more refreshed going into the next day of studying because your brain has had ample time to rest. You may also find that your retention of material learned is greater, as your brain has more time to build strong connections to the material that you’ve learned without being constantly bombarded by new information.
Let’s face it — not all topics in data science are created equal. Unfortunately, the good stuff, such as machine learning, data visualization, and real-world applications can only come after you’ve learned code, mathematics, and communication skills. With topics like these to grind through, it can be difficult to remain motivated for the good stuff yet to come.
One of my favorite techniques to get past this slump is to find the practical applications of the material that inspire me. For example, learning limits and differentiation can be pretty draining, but only if you forget that they can be used to determine the rate of change of a function which can tell you all sorts of cool things, like how climate change is quickening, costs of goods are increasing, or how access to healthcare is declining.
When you’re passionate about how you want to apply your data science knowledge (such as in healthcare, science, engineering, business, education, etc.), then it becomes easy to find the different ways that you can apply the knowledge you’re developing. For example, once you’ve mastered data analysis, you could do some pro bono work for a small business in your community to help them increase their sales. Or, you could create a predictive model of how many people would be affected by a particular natural disaster as a portfolio project.
Whatever your interests, there are always ways to apply what you’ve learned in a way that can inspire you to keep moving forward. Find what inspires you and apply data science to it.
I don’t care how much of a procrastinator you are, setting time-sensitive learning objectives works every time. It doesn’t matter if you leave it to the 11th hour as long as you complete it by the deadline.
One of the things I’ve seen people struggle with while self-teaching data science is a lack of motivation due to no structured time-sensitive goals. Many have said to me that they could never teach themselves data science because they don’t have the motivation to sit down and get their work done. However, this is simply due to a lack of structure.
One of the great benefits of going to school is that you’re in a structured environment with deadlines. Deadlines for assignments, deadlines for exams, and deadlines for graduation. You name it and there’s a deadline for it. This time-sensitive structure helps people focus and get down to work without even trying too hard. As I mentioned above, even if you leave it to the very last minute, you’re going to complete your work because you know there’s a hard deadline to abide by.
Therefore, the trick to attaining this motivation in the self-study world is to set time-sensitive learning objectives that you see as hard deadlines.
For many, this can be easy because you’re in the middle of a career change and only want to be out of work for so many months. For others, this can be more difficult because there may not be a specific time crunch that you’re working against.
Vacations, birthdays, events, and even weekends are great hard deadlines that can be used to motivate you to get your work done. Nothing feels better than getting all of your work done on Friday and knowing that your weekend is now open to do anything. The same goes for not having to worry about work during your vacation, your friend’s birthday party, or your child’s school play. Whatever the occasion, date, or end-of-week ritual, finding a hard deadline to structure your learning around can help motivate even the biggest procrastinator to teach themselves data science.