I Taught 600,000 People Python — Here Are the 5 Mistakes Everyone Makes

By Ardit Sulce · March 2026

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After teaching Python to over 600,000 students on Udemy and seeing millions of questions, code submissions, and forum posts, I have noticed the same mistakes appearing over and over. These are not obscure gotchas. They are fundamental misunderstandings that slow learners down for weeks or months if nobody points them out.

Here are the five most common ones and how to fix them.

Mistake 1: Thinking they understand loops when they do not

This is the number one mistake I see, and it is invisible to the person making it. Here is what happens: a student watches a tutorial on for loops, follows along, and the code works. They think "I understand for loops" and move on to the next topic.

Then, two weeks later, they need to write a loop from scratch to solve a real problem, and they are completely stuck. The gap between "I can follow a loop someone else wrote" and "I can write a loop to solve a new problem" is enormous. Most learners do not realize this gap exists until they hit it.

The fix: After learning about loops, immediately solve exercises where you have to write loops from scratch with no guidance. Not "fill in the blank" exercises. Actual problems where you start with an empty editor and a description of what the code should do.

This is exactly what the for loops exercises and while loops exercises on ActiveSkill are designed for. You write the loop, AI reviews your approach, and you find out whether you actually understand it or just think you do.

Mistake 2: Misunderstanding how lists and dictionaries actually work

Almost every student hits a version of this bug early on:

a = [1, 2, 3]
b = a
b.append(4)
print(a)  # [1, 2, 3, 4] — wait, what?

They expected a to still be [1, 2, 3]. The concept of references versus copies trips up nearly everyone, and it leads to subtle bugs that are incredibly frustrating to track down.

Similarly, many students do not understand when to use a list versus a dictionary. They store data in lists of lists when a list of dictionaries would be far clearer. Or they loop through a list to find an item when a dictionary would give them instant lookup.

The fix: Practice working with lists and dictionaries in isolation before combining them with other concepts. You need to build intuition for how these data structures behave, and that only comes from hands-on experience. The lists and dictionaries exercises are designed to build exactly this intuition.

Mistake 3: Skipping the fundamentals to build projects

I blame social media for this one. "Just build projects!" is the most popular advice for learning to code, and it is the most misleading advice for beginners.

Here is what happens in practice: a beginner tries to build a web scraper. They need to understand HTTP requests, HTML parsing, loops, functions, error handling, and file I/O. They do not understand any of these solidly. So they piece together code from tutorials and Stack Overflow, get something that kind of works, and learn almost nothing about Python in the process.

Projects are essential, but they belong in the second phase of learning, not the first. The first phase is building fundamental fluency through structured practice.

The fix: Start with variables and data types. Then lists and dictionaries. Then loops. Then functions. At each stage, practice until the concepts feel natural, not just until you have seen them once. Then build projects, and you will actually understand the code you write.

Mistake 4: Writing code that works but is terrible

Beginners are (understandably) so focused on making code work that they do not think about making code good. The result is code like this:

# Beginner version
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = []
for i in x:
    if i > 5:
        y.append(i)
print(y)

# What a Python developer would write
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
result = [i for i in x if i > 5]
print(result)

Both produce the same output. But the first version tells an interviewer "this person learned Python last month." The second tells them "this person writes Python."

The problem is that when you only check if code works, you never learn to write code that is good. You need someone (or something) to review your approach, not just your output.

The fix: Practice on a platform where AI reviews your code quality, not just correctness. On ActiveSkill, when you write the beginner version of the code above, the AI feedback will tell you "this works, but here is a more Pythonic way to do it using a list comprehension." That feedback is how you go from writing code that works to writing code that is professional.

Mistake 5: Not practicing regularly

This is the simplest mistake and the most damaging. Learning Python is like learning a language: if you study Spanish for 10 hours on a Saturday and then do not touch it for two weeks, you will forget most of what you learned. Your brain needs repeated exposure over time to move knowledge from short-term to long-term memory.

I have watched the data across hundreds of thousands of students. The ones who succeed are not the ones who are most talented or who have the most free time. They are the ones who show up consistently. Even 20 minutes a day is dramatically more effective than 3 hours once a week.

The fix: Make daily practice a habit. Solve one or two exercises a day. It takes 15 to 20 minutes. ActiveSkill's free plan gives you exercises every day specifically to support this habit. The leaderboard and points system helps too, people who track their progress tend to stick with it longer.

The common thread

All five mistakes share the same root cause: not enough practice with real feedback. Watching tutorials feels productive. Reading documentation feels productive. But unless you are writing code, making mistakes, and learning from those mistakes, you are not actually learning.

The fix for every mistake on this list is the same: practice more, with feedback, on structured exercises that match your current level. It is not glamorous advice, but it is what works. I have watched it work for 600,000 students, and the pattern has not changed.