Remember the time when you started that data science course but never got to completing it? Or the hundred other times you did the exact same thing but with courses you thought were a more “perfect match”, or as data scientists like to say…“sexier”?
Don’t panic! We’ve all been there! And no, it’s probably not your self-discipline nor your natural aptitude towards the subject matter.
These are 4 reasons that are probably stopping you from completing online courses and earning certificates:
- Information Overload! Information Overload! Information Overload!
Let’s be real here, it can be pretty overwhelming out there with the gazillion online courses, Data Science Tracks, YouTube videos, conferences, podcasts and books. It is almost impossible to know the best place to start! And even when you do, you are always asking yourself: “Did I make the right decision?”, “Is this course the best for me at this moment in time?” and of course the million-dollar question “Should I keep going?”.
For total beginners, I have one advice: dive head first into the learning process and do not overthink! If you are starting an online course offered by a well-reputed university, organization or company, chances are you will be learning the basics from experts in the field. That’s always a good thing. Do NOT overthink. Get the basics down. Practice. Practice. Practice. Move on!
If you already have the basics down, read on!
2. Ground Zero and the “Boring” Basics
Once you’ve gotten hang of the basics, you naturally want to push your skills up a notch. And so you start another course… only to quit after a few minutes/hours of making progress.
Why does that happen?
It’s quite simple actually. Most courses are designed in a linear fashion such that the first few videos/lectures/chapters focus on making sure the learner has gotten the prerequisite techniques down before introducing more complex ideas and concepts.
But you’ve already mastered those basics! And the last thing you want to hear for the hundredth time is how Python handles different data types! You already know these!
You are bored. You close that tab. The rest is history.
Starting from ground zero every single time you start a new course is just not the best way around it. You need to keep being challenged. My two cents on this is that if you already know it, skip it! Stay challenged and keep learning new stuff. You don’t need to relearn what a variable is or how Python stores variables every single time you start a new Python course!
3. Information is worth a lot less — Action is worth a lot more!
Let’s face it. You have completed a course or two in the past and you are sick and tired of overloading yourself with more information only to forget how to use it one or two weeks later.
In 2020, “knowing” is just simply not enough. There is just too much to know anyways. Stretching yourself too thin by overwhelming your “learning self” with information is probably what is stopping you.
You start a new course, but you have an itch to apply what you have learned in previous courses. Passively absorbing new information is just simply boring you out.
Stop right away! Pick up a dataset exploring a topic you are interested in, and apply everything you can remember on that dataset. It doesn’t matter how basic or advanced your data science skills are at the moment. Chances are you will learn so much more by just doing this and by “reality-checking” what you actually know how to apply instead of what you actually know.
Don’t be afraid of being on your own with that dataset! Make mistakes, get stuck, google stuff up, find examples, learn from them, make them better, apply them. Restart again and apply some more!
4. What you already know vs. what you NEED to know
This is a lot more a useful tip/exercise than a reason why you are not completing online courses.
Find a blank piece of paper. Take a pen. Sit alone at your desk. Close all notebook and books. Stay away from your phone and laptop for the duration of this exercise.
Now divide the piece of paper into 5 columns: Statistics, Python Programming (or another data science language of your choice — you can split this into other columns as well divided by libraries like pandas, Numpy, Matplotlib, Seaborn…), Machine Learning, Deep Learning, Data Tech (Tableau, Power BI, Spark or whatever it is you are trying to add to your data arsenal!)
In each column, jot down all the concepts you know/have learned in recent weeks. Think of everything you can do in each of one of these categories. Write it down while thinking about how you would actually apply it.
Look at your list! You now just became aware of your strengths and weaknesses at the particular moment in time you are doing this exercise. This list tells you exactly what you already know and are comfortable applying. These should be the things you already know HOW and WHERE to use.
What this list is missing is what you don’t yet know. Start from there! Pick a well-known data science book (plenty of good recommendations online) and check the table of content! Take small steps and tackle all missing concepts and techniques one by one.
Redo the above exercise every other week! This will help you reality-check your progress and what matters most: that is, what you know how to apply and use instead of what you have learned in a course two years ago, but have no clue how or where to use correctly and effectively.
I hope this helps you pick up your data science learning back up! Good luck learning and remember to ensure and remember that the process is actually fun!