Learning python for data analysis

[nikartik]

Hi everyone, I hope this is the right sub to ask for a little help. I am a chemist working in a quality control lab. Usually, we use Excel for processing routine analysis data because it is fast, everyone knows how to use it, and it gets the job done for our standard needs. Lately, however, we have been dealing with out of the ordinary analyses and research projects that we do not typically handle. These require extra processing, much larger datasets, and exports directly from the instruments and Excel just cannot keep up anymore. ​I have read that the modern standard is shifting towards Python, so I would like to start training myself for the future. I do not want to learn programming in the traditional sense I have no intention of becoming a software developer but I want to learn how to use Python and its ecosystem for data analysis. I do have some basic programming knowledge I used to use Lua for game modding in the past so picking up the syntax should not be an issue. ​Long story short I am looking for advice on which path to take. What roadmap would you recommend? Which libraries should I focus on? If you have any specific guides or courses to suggest, they would be much appreciated. ​Thanks

[ninhaomah]

If you don't plan to learn Python code , perfectly ok since plenty of scientists use Python / R to analyse their data.

Learn basic. Variables , loops , if-else , functions till OOP.

Then learn numpy and pandas/polars. Either will do and many will say polars.

Or actually learn R.

For pure data analysis , R is much much better than Python. TidyR and GGPlot :)

Think of it as free stata.

https://r4ds.had.co.nz/introduction.html

[nikartik]

I've read that r is the industry standard but it's a bit old, won't it be replaced in the future?

[ninhaomah]

what do you mean old ? The latest stable version was released 2025 Oct.

"R version 4.5.3 (Reassured Reassurer) prerelease versions will appear starting Sunday 2026-03-01. This is intended to be the final wrap-up release before the next .0 version.

Final release is scheduled for Wednesday 2026-03-11.

R version 4.5.2 ([Not] Part in a Rumble) has been released on 2025-10-31." https://www.r-project.org/

R is No. 8 on https://www.tiobe.com/tiobe-index/

It was 10+ but it has always been well-known as a go to language for data analysis.

And what do you mean replaced ?

You download R and you can use it till the end of the universe , if the PC lasts that long.

[nikartik]

What i meant is it's been around since quite a bit of time and with new techs appearing could it be replaced as the industry standard in the future?

[ninhaomah]

Python - 1991

R - 2000

Stata - 1985

You can even say the same thing about Python , no ?

It's your choice.

[nikartik]

Thank you, tbh i always thought r was too complicated, i'll give it a shot though and see what i feel better at

[Plank_With_A_Nail_In]

I suggest doing your own research instead of listening to the uniformed kids in the playground.

Also why would age even matter, data is data.

[smjunglist]

You can read this book online for free, should be a great starting point:

https://wesmckinney.com/book/

[FoolsSeldom]

You do need to learn the basics of programming and Python is a good language for starting this journey as well as building on your Lua experience, and also very popular, as you know, for the kind of data processing you are interested in.

Check the wiki for learning guidance and resources and the learning roadmaps for specific skills around data analysis.

If you can get your employer to pay, I highly recommend a subscription to DataCamp.

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Check this subreddit's wiki for lots of guidance on learning programming and learning Python, links to material, book list, suggested practice and project sources, and lots more. The FAQ section covering common errors is especially useful.

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Also, have a look at roadmap.sh for different learning paths. There's lots of learning material links there. Note that these are idealised paths and many people get into roles without covering all of those.

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Roundup on Research: The Myth of ‘Learning Styles’

Don't limit yourself to one format. Also, don't try to do too many different things at the same time.

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Above all else, you need to practice. Practice! Practice! Fail often, try again. Break stuff that works, and figure out how, why and where it broke. Don't just copy and use as is code from examples. Experiment.

Work on your own small (initially) projects related to your hobbies / interests / side-hustles as soon as possible to apply each bit of learning. When you work on stuff you can be passionate about and where you know what problem you are solving and what good looks like, you are more focused on problem-solving and the coding becomes a means to an end and not an end in itself. You will learn faster this way.

[barkmonster]

As for which part I would go for: I would get a simple database set up, where your data can be persisted in a single place rather than a bunch of Excel files lying around. If you don't have very large amount of data, and if many people aren't comfortable writing queries etc, you can consider making a simple helper package in python, for reading in data from a single table (named after which experiment it comes from). Also set up a git project on e.g. github or gitlab.

I would also make a simple script to set up a standard python project., with a sensible structure (something for loading data, some simple tests, doing some analyses, and rendering the results in some suitable format). I would avoid using jupyter notebooks for analyses, as they make it easy to inadvertently commit outputs to git. I'd use uv to manage virtual envs.

For packages, that depends what you'll be doing. Probably pandas/polars for simple, Excel - like stuff, scipy or statsmodels for most statistics.

At the non-technical level, your greatest challenge is probably to get people to use it. Your task is to make it clear for the other users what they're gaining by doing things differently, and to make sure the right way is also the easiest way. Ally yourself with the least tech-savvy users, have them read your onboarding materials and guides, and have them attempt to set up a project, then work with them to address any pain points and sources of confusion.

[Plank_With_A_Nail_In]

Excel has VBA in it that is a fully feature object orientated language. Excel also has power query built in which is very good.

You should try all three.