When I first started studying Data Science, I would read every article I could get my hands on about people who had done what I was trying to do: become a self-taught data scientist.
These articles were full of details about people without computer science degrees who, against all odds, taught themselves to be data scientists in an absurd number of months. Naturally, these people were also then hired practically on the spot by FAANG companies.
Articles with sensationalist headlines such as "How I Became a Data Scientist in 6 Months" or "How I Became a Google Data Scientist After Studying Data Science for 1 Year While Working Full-Time", describe how the authors "hustled" and "clawed" their ways to data science positions without much prior experience at all.
These articles promise readers that if they too "hustle," that they can achieve their wildest dreams of becoming a data scientist who has achieved career satisfaction and a six-figure salary, all within a ridiculously short time period.
As someone who already has an education in software development and university level mathematics (otherwise known as someone who has some experience with some of the components of becoming a data scientist), I see a couple of glaringly obvious issues with these articles that get wallpapered with hope and the glamour of hustle as a way to get views and inspire people that they too can become data scientists in less than a year with no prior training or experience.
Advantages are everything.
What often sets these success stories apart from your average data science learning experience is an advantage.
Those individuals who taught themselves data science and were hired within six months were likely individuals who had any number of advantages bestowed upon them to make such a reality happen.
The ability to study for eight hours a day, get a college education, having access to a computer, being able to afford MOOCs and bootcamps, and living in a tech hub are all advantages that are rarely spoken of by these individuals. These people paint a picture of hustle and grind though rarely describe the advantages they have that also helped them along the way.
While I’m not saying that these people had unfair advantages, I feel that these advantages would help anyone learn data science in less than a year.
It should also be said that material advantages are not the only things that help people become data scientists after a short period of time.
Being able to interview well, having background knowledge of an industry, and simply being able to pick up abstract concepts quickly, are all ways that people have been able to get hired by FAANG companies within their first years of practicing data science.
The point of this is not to degrade the accomplishments of these individuals simply because they are advantaged. Just because they are advantaged does not mean they didn’t work hard for what they achieved or had access to.
The point is to highlight how certain people have material and nonmaterial advantages that make it possible for them to achieve such lofty goals in short periods of time.
The point is to underline how you should take these sensationalist articles with a large grain of salt in understanding that your circumstances may be different than those of the author. This will change how you pursue your data science learning and career and may alter your path compared to someone else’s.
In other words, stop beating yourself up because you weren’t able to match the timeline of someone who was hired within six months of learning data science concepts.
Just because it worked for one person doesn’t mean it will work for everyone.
Every day promises a slew of new articles published on how to learn data science. Many authors promise the perfect curriculum that will help anyone learn data science in a short period of time.
Rarely though, does one article outline the perfect way for the masses to learn. Most articles outline what worked for the author, and what they think will work for others.
The point here is to not discourage authors from writing articles about how best to learn data science from their opinion and personal experience. These articles are treasure troves of information that are great starting points for aspiring data scientists.
However, that’s what these articles should be – starting points.
Aspiring data scientists should take these articles with a huge grain of salt. Just because one method worked for one person doesn’t mean it will work for everyone.
It’s vital to take into account the varying pre-existing skill levels in coding, mathematics, and data analysis that some of these authors will have when writing these articles.
For instance, when I wrote about my personal plan for learning data science, I didn’t spend too much time listing basic coding courses as I already had a background in software development. This meant that I could jump right into the more challenging concepts such as using Python for data structures and scientific computing. Someone reading the article with no coding experience may have tried to follow the same curriculum and failed if there were not enough basic coding courses in there for them.
Following along with these curriculums can quickly become a discouraging experience as the results you expected don’t always come to fruition.
Therefore, be kind to yourself. Just because a particular method or curriculum worked for someone else doesn’t mean it will work for you. Become in tune with how you learn best and the skillsets you already possess. Take the best parts of each curriculum that will work best for you and use those to guide your learning journey.
Otherwise, reading these articles and expecting to get the same results is no better than the blind leading the blind.
Learning to become a data scientist in anything less than a year ought to be considered a miracle.
A year is not a long period of time.
Regardless of what anyone says, achieving anything in a year is impressive and nigh on impossible. Especially when it comes to becoming a data scientist.
Setting the deadline for yourself to become a data scientist in six months, or even a year, may offer you a harsh dose of reality as you discover just how long it takes to become proficient in anything.
When I was in university studying software development with no prior Programming experience other than messing around with HTML and CSS, it took me an entire semester (four months) to learn programming fundamentals in C#. Four entire months.
If your goal is to become a data scientist in six months or even a year, those four months get eaten up pretty quickly when you begin to learn something that you have no prior experience in. It can be surprising how long it can take you to get a grasp of abstract concepts and to teach your brain to think about things differently.
As the months go whistling past and your deadline is suddenly swept under the rug, it can feel discouraging that you’re not accomplishing what others have been able to do in half a year.
While deadlines are an important part of setting goals, it’s also important to understand that your learning path will not be the same as someone else’s. As mentioned above, it’s often the people with particular advantages such as the ability to study for eight hours a day or those who already have a university degree that can become data scientists in a short period of time.
When you think about it, your average developer will have a degree in software engineering or computer science which took them four years to complete. Furthermore, your average data scientist will have a master’s degree on top of that, which may have taken an additional two to four years to complete.
In my opinion, getting a job in data science for the average person will likely take at least one year of study. That would already mean working at warp speed compared to your average university graduate. Therefore, it’s important to remember that by setting nearly impossible deadlines for yourself that you’re actually potentially hindering your progress and even worse (and what’s most important) your mental health.
Ask any data scientist and they will affirm that learning data science is hard. Don’t make it harder on yourself. Instead of focusing only on the destination, learn to enjoy the journey, and learn to practice gratitude for each piece of information you learn every day. Over time, those pieces of information will transform themselves into a career as a data scientist.
Final thoughts.
Reading sensationalist articles about becoming a data scientist usually follows a trajectory that starts somewhere around inspiration and ends up somewhere around discouragement.
In the beginning, it can feel like you can do anything after you hear about every successful person who realized their dreams of changing their lives for the better and becoming a data scientist.
After a period of study, however, this feeling of inspiration can slowly degrade into feelings of frustration and discouragement as you realize just how tough the journey can be. At this point, it’s best to stop reading these articles and start focusing on your own path. Why?
Because as mentioned above, the articles written by successful individuals outline journeys where advantages were had, and success was achieved in a brief period of time. These articles do not outline how you can best learn data science.
This is not to say that authors should stop writing these articles. These articles are important for inspiring the next generation of data scientists. Without these articles, I think few individuals would be willing to give data science a shot.
Instead, this is to say that readers should take these articles with a grain of salt and read them for what they actually are: stories about personal success that only discuss one person’s experience. You will rarely read about those individuals who share stories of failure to learn data science or those who took years to accomplish their goal.
Therefore, don’t be worried if your path doesn’t match that of someone else’s. It may take you six months, or it may even take you a few years. The only important thing is that you stay the course and consider sharing your journey along the way. Because believe it or not, your story will help inspire someone to attempt what you also thought was impossible at one point: becoming a data scientist.