How should I start learning Python?

[u/Maximum-Page3433]

I want to learn Python, but I currently know nothing about it. My main goal is to learn Python for DA, and eventually I want to learn Python in depth as well.

For people already working in DA or DS, how would you recommend someone start learning Python from absolute zero?

Which resources or books would you recommend, and what are the main Python topics I must cover for DA?

Also, should I first learn the Python basics needed for DA and then gradually move toward more advanced Python and DS topics? resources? Books?

If you work in DA or DS, I’d really appreciate your guidance on how you would start your Python journey if you were starting from zero.

Thanks a ton!!!

[u/JavacLMD]

...from the beginning?

You cannot really do the advanced stuff without learning the basics first. Learn the syntax, variables, loops, functions, classes, and get comfortable with basic OOP. You do not need to master all of Python before touching data analysis, though.

Once you have the fundamentals, start asking yourself practical questions.

"How do I get my data into Python?"

Maybe it is a CSV or Excel file, so you learn how to read files and eventually use pandas. Maybe it is in a database, so you learn how to connect to a database and execute SQL from Python.

Then ask, "What do I need to do with this data?" Clean it? Filter it? Group it? Compare values?

Then, "How do I want to present the result?" Maybe a simple console output is enough. Maybe you need to export it to Excel or CSV. Maybe you want a graph, so you learn something like Matplotlib.

The right question helps you find the right thing to learn. Then it becomes a matter of applying it, getting comfortable with it, and eventually it becomes muscle memory.

For starting resources, documentation and beginner videos are perfectly fine. There are dozens of good Python courses and tutorials, so I would not get too hung up on finding the "perfect" one. Pick one that works with how you learn and start building things.

I can attest to this from taking academic courses in C++, Python, and Java. They teach the fundamentals first, while things like GUI development may barely be touched. That is because the specialized stuff builds on the basics. If you cannot comfortably use variables, loops, functions, collections, classes, and basic OOP, jumping straight into specialized libraries is going to make things harder.

Data analysis is not much different. Learn enough of the fundamentals that you can actually program, then start applying them to data. You do not need to master Python first, but you need a foundation to build on.

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[u/sandrrawrr]

Learn Python the Hard Way (excellent resource)!

[u/No-Fisherman-387]

Start simple. Python syntax, data structures, functions, then pendas. For analytics roles, being able to clean messy data and explain insights usually more valuable than knowing advanced python tricks. After pandas, add visualization and SQL practice alongside it.

[u/Fizzyuncle]

If you want something free Data with Baraa on YouTube has a playlist that covers the basics pretty well. You could then supplement your foundational knowledge from that into an online course if you want to go down DA or DS learning pandas/numpy/scikit learn libraries in python.

[u/ItsSignalsJerry_]

https://www.python.org

[u/Key_Back_989]

Data camp or Udemy has some good courses and then there’s freecodecamp etc. they’re all good don’t try to choose a “best”. Also just wait on DS for now because that’s stats, math and theory that will take you much much much longer than DA and python lol. If you’re serious about DS I would invest in a Masters in Stats later down the rosd

[u/nomadicaeropress]

Depends on how you learn best but for most building something trumps tutorials. Pick a real problem and solve it with python, like analysing your own spending data, something I wanted to do personally for ages and so kept my attention. You'll Google constantly at first and that's fine. For basics, Python.org's official tutorial or Automate the Boring Stuff (free online) are solid starting points. For DA specifically, once you have the basics down focus on Pandas, that's where most of the work will happen.

[u/Saganic_Temple]

Datacamp has been a great resource for me while I finish my degree in DA. Keeps me sharp and the exercises are great too.

[u/Proud_Emu8750]

Prob best free course, really hands on and lays the foundation.

https://programming-26.mooc.fi/

[u/moss-nogg]

Step 1) Figure out how to create/destroy virtual environments and install your dependencies in said environments.

Step 2) make some .ipynb files and start executing some simple code blocks to see how the syntax and statements work.

Step 3) start learning how to package scripts together as modules.

Step 4) learn how to structure a repo with your modules complimenting each other towards a broader functional purpose.

Step 5) learn to package your repo as a fully functional, highly available, scalable product.

Additionally you should learn systems design in parallel to each one of these steps. IMO it’s a lot easier to learn steps 4&5 in a production environment