Image by H. Hach from Pixabay
Are you new to learning data science with DataQuest? Do you ever feel overwhelmed by the amount you need to learn? Are you thinking about quitting because mental gremlins keep getting in the way of your progress? They have definitely got in my way more than once!
I started my journey with Dataquest a little less than a year ago. Although I consider myself a technophile, I had no previous experience in coding and was a little apprehensive at first. However, after completing the first few missions on the Data Analyst in Python path, I felt quite confident and proud of myself for completing them without having to “cheat” and look at the hints or answers provided. Sadly, that feeling did not last.
It seemed like I couldn’t go two screens without looking at the hint or answer because the frustration of not knowing what to do was so overwhelming. Nothing was making any sense, and I was struggling to recall the syntax for commands that I had just learned or, even worse, I completely forgot the command itself! I was plagued by self-defeating and demoralizing questions like these:
- What is wrong with me? (Hint: nothing.)
- Why can’t I do this? (Hint: you can.)
- Am I just stupid? (Hint: no.)
So, I took a little hiatus from my learning with Dataquest to watch some YouTube tutorials and read some articles to see if I could figure out what I was missing. While this little adventure did help me learn some new things and fill in a few gaps, I started asking myself much more difficult and disturbing questions:
- How am I supposed to remember all this stuff?
- Will I ever know as much as other people seem to know?
- Am I incapable of learning the necessary skills for this kind of work?
- Am I the only one who feels this way?
- How long will this take?
If you’ve ever asked yourself these kinds of questions, please know that you are not alone! And I’m not just talking about you and me; many professionals working in the field also ask themselves these types of questions while struggling with feelings of imposter syndrome. These questions are being asked by what I like to call mental gremlins. I had a breakthrough moment when I realized that these gremlins feed on fear and I know fear is a liar. Fear is like water to them; it allows them to multiply out of control!
That said, how do we defeat these gremlins and move past them? The answer to every problem starts with education, so let’s take a look at each one to see how we can remove their power over us.
As much as we’d all like to be like Data from Star Trek the Next Generation, most of us don’t have perfect recall. We are human, and we are going to forget stuff. With the multitude of programming languages out there (Python being just one of them) and a never-ending list of available libraries, each updated on a regular basis, it is truly impossible for anyone to know every command and how to implement it at a moment’s notice. The good news is, we don’t have to remember everything.
There are tools out there to help us. The most obvious is Google. With enough Googling, you can find the answer to almost any question. While you might feel like this is “cheating,” we really need to disavow ourselves of this notion. Google is also great for finding the official documentation for the packages you’re using. Get comfortable reading them, and consider adding them to your bookmarks for easy reference.
Then there’s your friend StackOverflow. While it can be a little intimidating at first, this site is invaluable when it comes to the “How do I…?” type questions. Don’t be afraid if you come across code you don’t entirely understand at first. It can often lead to amazing learning opportunities. Be patient and try to modify the code you find there to fit your particular situation if you can’t simply copy/paste it into your project as is. A lot of learning can happen with a little experimentation. If all else fails, create a new post; you will be amazed how quickly you get a response.
To help you remember what you’ve learned specifically with Dataquest, I highly recommend getting a binder and filling it with the Mission Takeaways (PDF files) provided at the end of each mission. While the electronic version is great, I find having a physical one on my desk more useful. Take the time to create sections with tabs so that you can quickly find what you’re looking for. You can download all the Mission Takeaways from this link
While it is human nature to compare ourselves to others, this can be a very toxic way of thinking. When we practice this destructive behaviour, we tend to compare the worst in ourselves with the perceived best in others. It’s a completely unfair comparison. It can leave us feeling anxious and frustrated, which is not conducive to learning. Instead, try to focus on your learning rather than compare yourself to others. You know more today than you did last week, right? Good! Keep doing that.
The reality is no matter how much you learn, there will always be something new–and that’s a good thing! Learning is a journey, not a destination, so we should try to enjoy it rather than let unfair comparisons paralyze us with fear.
Interestingly, you will eventually learn enough that you will cause this feeling in others! The trick is to keep going. This advice comes from one of my all-time favourite quotes that I remind myself of when dealing with this gremlin:
If you’re going through h e l l, keep going.
— Winston Churchill
Essentially, if you find yourself feeling terrible about where you are in your learning journey, resist the urge to wallow in it. It will only decrease your motivation to learn. I have found focusing on shorter-term goals and achieving them to be the best way to combat this feeling. DataQuest’s platform is perfectly suited to achieving this, with each mission being the perfect bite-sized chunk of learning. So, the next time you feel like you don’t know as much as the next person, try completing one extra mission and remind yourself that you know more today than you did last week!
Programming is often regarded as a young person’s game. Since I am over the age of 40, I had real concerns that I was too old to learn how to become a programmer. Again, this was fear talking, and we know fear is a liar. The truth is, we are never too old to learn programming. While the advantages of starting to learn programming sooner are obvious, there are also advantages to learning data science skills later in life. Later learners have had the chance to develop emotional intelligence, real-world experience, patience, stability, and specialist knowledge.
If age is not your concern and you feel like you’re just not smart enough to learn programming, remind yourself (ironically) that even six-year-old kids can do it! Contrary to what you might think, you don’t need to be a genius to learn programming. What you need is the right mindset and attitude toward learning. I’m not saying that it will be easy, and anyone who tells you otherwise would be pulling the wool over your eyes. However, it is possible with time and effort. Just keep going and never stop learning. Try to focus on progress, not perfection.
I found a great article a while ago with some fantastic learning strategies for programmers. For the TL;DR crowd, the five strategies for learning are studying with the right frequency and duration, taking breaks, getting zoned out, finding a balance between active and passive learning, and teaching what you learn.
Teaching yourself how to code can be a lonely endeavour. It can often feel like you’re the only one in the world who is thinking and feeling a certain way. This could not be further from the truth.
Fortunately, at Dataquest there is an incredible community of learners for you to engage with. I can’t stress enough how important it is to have a community of like-minded individuals who can validate your thoughts and feelings when dealing with the stress of learning by yourself.
The community is truly your friend. Not only can you find answers and post questions specific to the material you are learning through DataQuest, you can also upload your completed guided projects to receive valuable peer feedback. Additionally, providing feedback to others is a win-win proposition; the person you’re reviewing has the chance to learn from you while you have the chance to learn from them. Check out this post for some great tips on providing constructive feedback on projects.
I’ve saved the best for last; this tends to be one of the most popular questions from new learners. It’s commonly believed that you can learn the fundamentals of data science within six to nine months by dedicating six to seven hours every day to your studies. However, becoming a good data scientist who can bring real value to a company often takes much longer than that.
The more complicated answer is that it depends. It depends on your background, how quickly you learn, your depth/breadth of study, and your end goals. Everyone’s story will be different. Avoiding burnout and maintaining a passion for learning to code are crucial to our success.
By some popular estimates, 65% of children entering primary school today will ultimately end up working in job types that don’t exist yet1. Another popular estimate states that nearly 50% of subject knowledge acquired during the first year of a four-year technical degree will be outdated by the time students graduate2. Therefore, it behooves us to focus on improving our data science skills, logic, and wisdom rather than focusing on the learning of specific code that will likely become obsolete in the near future. Good learning habits and the right attitude will never suffer such a fate. Consistency and aiming for progress, not perfection, are the keys to success.
Learning data science is fun, exciting, and extremely rewarding. At the same time, it can be a long, bumpy road filled with many challenges. However, with hard work, focus, and perseverance, we will get there. We just have to keep going. Keeping your gremlins dry helps too.
1. World Economic Forum, 2021. Chapter 1: The Future of Jobs and Skills. The Future of Jobs - Reports - World Economic Forum
2. World Economic Forum, 2021. Skills Stability. https://reports.weforum.org/future-of-jobs-2016/skills-stability/#view/fn-13