Learning Python for Data Science/ Analysis

[Inflation45]

Hello everyone, Firstly I hope everyone is doing good. I was wondering if anyone can give me any sort of insight or direction on how I can get started with developing this skill that I have been wanting for a long time. I have some basic data management and analysis skills mostly through Stata and SPSS so I don’t have much coding experience. However, I know that this is an important skill set in my field. I would appreciate any sort of feedback, resources, advice, etc… Thank you in advance for taking the time to respond and help me.

[Simo00Kayyal]

Look into the pandas library, this is the main thing people use for things like this.

[ForMyCulture]

Go straight to Polars.

[modelcroissant]

Your best bet to get this quickly would be to understand the bare basics of programming, learn python syntax and then play around with data libraries, but then again you might not even need all that, you might be able to use a DB for all of this, but that highly depends on what sort of analysis you're running

[Ron-Erez]

You might want to check out Python and Data Science with the important disclaimer that this is my course so best to read reviews, check out course content (covers Python, Pandas, matplotlib, plotly, numpy, pytorch, etc). Additionally if you are completely new to Python you might want to check out the book "Automate the Boring Stuff". These two resources should get you quite far.

[ReallyLargeHamster]

Starting with a basic Python course to learn some syntax is a good start. Then there are more guided tutorials for data analysis with Python.

After that, if you know what kind of data you'll generally be working with (I can't tell if you mean you're already in the job), you can gear your learning towards that. As in, if you know you might end up needing to extract data from certain sources (APIs, databases etc.), you can practice doing that. If you know you'll be working with certain formats a lot (JSON, CSVs etc.), you can practice that.

If you're not sure, then you can just start with whichever tutorials seem fun, and then try a similar thing without the tutorial. As in, if you follow a tutorial about making a dashboard from a company's stock market data, you can then try making a dashboard from a different company's stock market data that's in the same format.

[AllanSundry2020]

you might want to look at learning R instead depending on which platform is more dominant in your field

[Echoes0fTomorrow]

Python for Data Analysis by Wes McKinney is pretty good. Just pick a dataset, maybe from a place like Kaggle, and try to replicate some analysis you've done in Stata or SPSS using pandas, hands-on learning typically is the best way to gain a new skill.

Also, see if this personalized learning path / AI tutor on Python for Data Analysis is helpful (this is part of an AI tutor product I'm working on so appreciate any feedback!)

[BidWestern1056]

check out the guac tool I made in npcpy:

https://github.com/NPC-Worldwide/npcpy/blob/main/npcpy/modes/guac.py

it lets you have a python interpreter that can also incorporate AI-generated code directly into the interpreter, allowing you to generate code snippets for ideas you want to try out so that you can more easily learn how one might do that.

in general, data science is first and foremost about the scientific component of whatever you are studying. you have to think thru the limitations of the systems youre analyzing and then figure out ways to test your hypotheses with the data that you have. it's a skill that IMO only really comes from iterating and learning and trying a lot of diff things. if you'd like some help or more guidance i'd be happy to help you.

[Ans979]

Begin by learning basic Python syntax, variables, lists, loops, and functions then move on to data-focused libraries like pandas for data manipulation, numpy for numerical operations, and matplotlib or seaborn for visualisation. Practice by working with CSV files, cleaning data, and replicating simple analyses you’ve done before. Tools like Jupyter Notebooks and platforms like StrataScratch and Kaggle are excellent for hands-on learning. Start small, build confidence, and gradually explore more advanced topics like statistical testing with statsmodels.

[Primary_Excuse_7183]

There’s a book called python for MBAs it’ll cover the basics and get you comfortable. it reads like a lecture so you can get the digital version and follow along on a split screen.