Self-Studying Python? Here’s What I Learned After Taking 10+ Online Courses
How to pick the right Python course for you and self-study using online courses.
Online courses helped me learn much of the Python stuff I know. I like going at my own pace and having the flexibility to study and work, so I finished most of them without any problem.
However, there were also times when I joined a course, took a couple of lectures, and never returned. Sometimes the course was too advanced for me, the instructor was too boring, or the concepts were poorly explained. After taking many online courses, I’d like to share with you my strategy for self-studying using online courses.
This guide covers things like how to pick the right Python course, how to make the most of online courses, and more.
First Things First — Answer These 3 Vital Questions
One of the key points to choosing the right course(s) for you is to know what exactly you’re looking for, why you want to learn that, and how much time you have to learn it.
Ask yourself these questions:
1. What (exactly) do I wanna learn?
2. Why do I wanna learn it?
3. How much time available do I have to learn that?
Questions #1 and #3 help you narrow down the courses, while question #2 supports your decision (keep this in mind whenever you feel you make little progress).
Say you know the basics of Python and want to learn more. There are many fields where Python can be used (data science, web development, AI programming, machine learning, etc), so you need to know what exactly you want to learn. Also, if you have little time available, you should stay away from those 50+ hour courses that you might never finish.
Note: If you don’t know how to answer question 1, jump to the next section (there’s a course that might help you make up your mind).
Here is how I answered these questions when I was an absolute beginner.
1. Data Science with Python
2. I like Python and working with data.
3. All day!
Here is how I answered these questions after learning Python and data science for some years:
1. Machine Learning with TensorFlow
2. I love ML and TensorFlow is popular among developers (it has Google’s support)
3. 1 hour on weekdays
I gave these 2 examples, so the advice in the next section can fit most situations.
Great! Now is your time to answer these questions. Once you know the answers, choosing the right course will be way easier.
How to choose the right course for you
There are hundreds of courses out there, so choosing the right course might be overwhelming.
You might ask for advice from your friends and colleagues, but, the truth is, a course defined as “perfect” by your colleague might not be perfect for you. Why? Well, he/she might have a different background, experience, and goals!
Here are some rules of thumb that you should follow when picking a course as a beginner or advanced student:
The course should have tasks, exercises, and projects. This helps put into practice what you learn
Keep an eye on the instructor(s). Smart people aren’t always the best teachers
If you want to join a course that involves coding, make sure the instructor doesn’t write code right away, but, whenever possible, makes some time to explain concepts with slides, an online whiteboard, etc.
If you’re an absolute beginner
If you’ve just started your journey, take courses that focus on core concepts, so you can build a strong foundation. Also, if you still don’t know what exactly you want to learn, take courses that cover different topics.
Say you want to start your data science journey with Python. Here’s what I get after searching “Data Science with Python” on Udemy,
After checking them out and considering the previous advice, course #3 looks like a good candidate. Why? It focuses on Python for data science and has plenty of exercises and projects.
Course #2 is good too, but isn’t only about Python but also math and statistics (this goes against my answer to question 1), On the other hand, course #4 is a bit advanced and not only focuses on Python but on R, while course #1 is kind of outdated.
If you have previous knowledge
If you already know some stuff and want to specialize in a topic, you should stay away from those “ultimate courses” that cover a lot of stuff but don’t delve into the topic you’re interested in.
Here’s what I get after searching “Machine Learning with TensorFlow” on Udemy.
This time is harder to choose a good course.
Course #1 is too broad and basic. Course #3 lacks the projects and exercises, while course #4 is more about math for ML.
If I had to pick one of those courses I’d choose course #5 (it has a couple of good projects) and course #2. But they don’t convince me completely. Course #2 has some exercises, but not so many projects and I’m afraid it can be too broad (63 hours of content!).
The good news is that there are also other platforms we can check out. I like Udacity’s courses because they’re taught not only by 1 but many industry experts who are also good at teaching. Coursera is another great option.
How to make the most of a course
Now that you selected the right course, it’s time to get your hands dirty.