3 Ways to Get Frustrated as a Data Science Beginner
There is no doubt whatsoever that data science happens to be a very broad field. It always looks like there are so many things to learn, and this is amplified by the rapid developments from the data science research field. For a beginner, this can be hard to cope with. There is so much math to learn, oh you have to learn statistics, learn to write code, build projects, read this paper (or that paper).
How on Earth do you ever cover all this?
It always feels like you are so close, but so far away, init? Well, this article doesn’t have any direct answers on how you can get to your desired skill level. However, it has tips that can help you pay attention to things you shouldn’t do, at the very least, to make the journey easier.
Without much ado, here are the three ways to get frustrated as a data science beginner:
- Be Consistently Inconsistent
- Try to Learn all The Fundamentals
- Attempt to Finish all Courses
Be Consistently Inconsistent
Learn for loops today, learn to try to pick up while loops three weeks after, then check up dictionaries seven weeks after. Feel attacked? If you do, then you probably already know what the problem is.
Data Science as with many other fields requires consistency, at the very least, when you are just starting to understand different concepts. By being consistently inconsistent, you easily get to lose the progress you make. Hence, you have to spend time and effort relearning those concepts instead of making them “second nature” or learning other things.
This kind of inconsistency can only get you frustrated, as there will be almost no sign of progress. But you’ve spent time, so where did all of that effort go? The answer to that question is beyond the scope of this writeup.
Truly, life throws up the unexpected. New activities, emergencies, changing priorities, infrastructural challenges… These and many more are different reasons why such inconsistency occurs, but truth be told, the only way to make progress is to be consistent. While learning doesn’t necessarily have to be every day, consistency will go a long way in tracking your progress. It will be pivotal to you solidifying various concepts, figuring out where your interests truly lie, and understanding how best you learn.
So if you want to be frustrated about your learning process, become consistently inconsistent.
Try to Learn all The Fundamentals
When you understand that you could spend a lifetime, and still not learn and master all the fundamentals, you become more strategic about how you channel limited time and effort more appropriately.
There are so many things to learn. The subfields of data science are very broad, talkless trying to “conquer” the entire field. As with many other things, the best way to learn data science is by doing.
When you can’t learn by doing, learn by studying what others have done and how they did it. If you spend all your time and effort learning the fundamentals, or the things that can be done, you will end up doing nothing.
“Knowledge is power? No. Knowledge on its own is nothing, but the application of useful knowledge, now that is powerful.”
― Rob Liano
If you have no inspiration on what you can “do”, you can research possible project ideas or even look at the works of others on their projects. By doing this, you increase your chances of having an exciting project to work on, albeit a small project.
When you work on projects, your self-confidence increases. Your learning path becomes clearer as you now know better what kind of things you’d like to do as well as the kind of knowledge you need to be skillful enough to do such things.
You can read this article on how to get smarter to get more insights on how to learn quickly. Always remember that you don’t need to know it all, you only need to know enough.
Attempt to Finish all Courses
Is it bad to try to finish a course? Well, not really. But before that, it is worse to jump from course to course without learning something that you didn’t know before, or without solidifying already existing knowledge.
It is not rare for beginners to jump from course to course, especially when the course contents become more mentally tasking. This is bad both for self-esteem and the learning process. Tutors will often have different approaches, so jumping from one course to another will usually leave a beginner confused and overwhelmed.
On the other side of the spectrum, trying to finish all courses while being a good thing can be an ineffective use of time. This buttresses the lesson from the previous point, that the best way to learn is by doing.
Take courses that you believe and feel are pointing you in the right direction to achieve your set goals on a project. This way, you can skip parts of the course that do not immediately apply to you. This doesn’t mean those concepts are useless, it just means you don’t care about them yet.
Time is a very limited resource. Trying to finish every course will very easily have you down on motivation to work on your project. Own your learning process. Learn what you need “just in time”, not “just in case”. It’s not a bad thing if you learn “just in case”, but ensure you are not learning a ton of stuff you don’t need yet.
Conclusion
Everybody has a different story i.e. different situations, locations, privileges, etc. However, the major goal for every data science beginner is to attain a certain level of competence. It will be nearly impossible to do this if you don’t put in consistent effort, learn by doing, or try to exhaust every resource out there.
The journey becomes easier when you have a small project you are excited about. This excitement pushes you to become more curious about the concepts you need, the technologies you require and gives you a clearer vision towards your goal.
“Assume nobody else has any idea what they’re doing either. A lot of people refuse to try something because they feel they don’t know enough about it or they assume other people must have already tried everything they could have thought of. Well, few people really have any idea how to do things right and even fewer are to try new things, so usually if you give your best shot at something you’ll do pretty well.”
― Aaron Swartz
