2 weeks into Python fundamentals and lost - what should I learn next?

[u/Random_USER_-__]

So I’ve spent the last 2 weeks learning Python basics - loops, conditionals, functions, OOP, etc. - and I’m pretty confident with those concepts now. I’ve also built a few small projects along the way.
But now I’m seeing everyone talk about DSA, APIs, Pandas, NumPy, and honestly I’m confused:

1. What even are these? Are they libraries? Frameworks? Concepts?
   
2. Do I have to learn all of them? Or can I pick and choose based on what I want to build?
   
3. In what order should I learn them? Is there a logical sequence?
   
4. Where should I learn from? YouTube, courses, documentation?

I haven’t joined college yet (starting soon), and I want to build a strong foundation before I do. I’m unsure whether I’ll specialize in data science, cybersecurity, AI/ML, or something else—I’ll figure that out during college. So ideally I need skills that give me a good base for any direction.

Anyone been through this? What would you recommend for someone at my stage?

[u/MrMurrayOHS]

They are all basically libraries at the end of the day - how/why you implement/use them is what differs from one to the other.

DSA, Pandas and NumPy look to geared towards Science/Mathematics and Data Analysis.

APIs are available for too many things to list but they are basically libraries allowing you to bring other projects into yours. Such as using the Google Maps API to include Map functionality to your App.

Good luck, have fun. Build more(larger) projects.

[u/Random_USER_-__ → u/MrMurrayOHS]

Thanks for breaking that down! That actually makes a lot of sense—treating them as tools for different purposes rather than separate mandatory topics.
Quick follow-up: For someone still deciding between paths, should I focus on DSA + APIs first (since those seem universal), then build projects while exploring which specialization clicks for me? Or is there a risk of learning DSA too early and forgetting it if I don’t use it immediately?
Also, when you say “build larger projects,” what kind of projects would be good at this stage? Like should I be building projects that use APIs, or is that too much at once?

[u/Adrewmc → u/Random_USER_-__]

API (Application Interface) is a blanket term really for a point in which a user interacts with a program through the web, the API is a set of functions that you make and other will call on for information.

When you type a reach into Google, you’ll notice the link change to https://www.google.com/search?q=your+question&token= <<bunch of tracking stuff, tokens etc>>

This is how you access Google’s api, through that link specifically.

Websites are generally a API, we access through a browser, as well as apps (basically didn’t sorts of browsers on some levels), so concentrating on API’s is more about finding the API that useful to you and using it to its fullest, or creating functions that are the most useful and efficient.

In other words, there are hundreds of thousands of APIs and all of them are different. And most of them are not very good actually.

As for Data Structures and Algorithms are more grounded. If you are going to be studying stuff that comes with a lot of data attached to it, highly recommend numpy (number Python) and Pandas (dataframes/tables). That more grounded in mathematical theory and where the floating point stops your precision.

[u/Civil-Detective-949]

Okay I suppose you have what it takes to build upon, that's to specialize in any field. Strong Python fundamentals is what is required to conquer the college tasks. And you got that already so no need to worry about what others are saying. You heard DSA, API, and staffs, those are honestly mixed up info you heard somehow, numpy and pandas are libs for mostly data science ppl, API and DSA are for software development the most and so on.

So right now you aren't into any field so don't worry about how to know them for now until you choose a field which requires some of them.

For the specializations in python: Popular once includes; Backend development, DevOps Engineering, Software development/full stack development, Data science, AI/ML Engineering and others I couldn't mention. I believe this finds you well as well as gives you more understanding of what you were looking for.

[u/BranchLatter4294]

I think you need to take a step back and work on your Googling skills. If you get stuck on things that are very simple to look up, it's going to take you a very long time to get up to speed. By taking a step back and getting better prepared, you will speed up your journey in the long run. It's well worth the effort.

[u/mc_pm]

Do not worry about those yet. Spend a bit of time practicing those basics. Write a few things that test your ability to look at a problem and figure out how to turn it into code that works.

Learning a bunch of libraries isn't going to go well if you don't even have the language fundamentals down.

(And I don't mean "knowing what a for loop is", but when to use it and how)

Do that for a while, then come back and other libraries will make a lot more sense.

[u/Kaiser_Steve]

For now spend time on the basics. Don't worry about DSA, APIs, Pandas etc...their time will come!

[u/metric_skeptic]

Skip DSA for now — it matters a lot for software engineering interviews, barely at all for data/analytics work. Pandas and NumPy are the actually useful next step if there's any chance you go toward data/AI/ML, and they build directly on what you already know (loops and functions become .apply() and vectorized operations).

To answer your actual questions:

1. Pandas/NumPy are libraries, APIs are a concept (how programs talk to each other over the internet), DSA is a body of knowledge (data structures + algorithms) — different categories, not competing options.

2. You don't need all of them before college. Pick based on a project you actually want to build, not a checklist. A project forces you to learn the specific 20% of each tool you actually need, instead of reading documentation for things you'll never use.

3. Order that worked for a lot of people: solidify Python fundamentals (sounds like you're there) → pick ONE small project with a real, if boring, goal (clean a CSV, analyze some data you care about, build a small script that automates something) → learn Pandas/NumPy as you need them for that project → APIs when you want to pull in real data from somewhere.

4. Documentation over YouTube once you're past the very basics. Tutorials are great for the first exposure to a concept, but you build real skill by getting stuck on your own project and searching for the specific error you hit — that's a different kind of learning than watching someone else code.

Since you're not sure yet which direction (data/cyber/AI) — Python + Pandas + basic SQL is a strong, direction-agnostic base. All three show up regardless of where you specialize later.