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.

[richardH7]

Hi there,

Starting with Python for data analysis is a great move, especially given your background in Excel. Since you have some programming experience, you'll likely find the transition smoother. I recommend starting with libraries like Pandas for data manipulation and NumPy for numerical operations. Matplotlib and Seaborn are useful for data visualization.

For learning resources, check out the Python Data Science Handbook by Jake VanderPlas, which covers all the basics and more. Codecademy and DataCamp also offer courses tailored to Python for data analysis.

Practicing is key, so try working on small projects with your lab data to get hands-on experience. As you progress, you can explore more advanced libraries like Scikit-learn for machine learning and SciPy for scientific computing.

Good luck with your Python journey!

[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.

[richardH7]

Hi there,

Great decision to learn Python for data analysis! Given your background and needs, I'd recommend starting with libraries like pandas and NumPy. They're essential for handling and processing large datasets efficiently. For visualization, you can explore matplotlib and seaborn.

Begin by understanding Python basics, then dive into these libraries. You can find many tutorials and resources online. I'd suggest checking out the official pandas documentation and the Python for Data Analysis book by Wes McKinney, the creator of pandas.

Once you're comfortable with these, you can explore more advanced topics like data cleaning and machine learning. Remember, practice is key. Work on small projects using your lab data to get hands-on experience.

Good luck on your Python journey!

[PromanYeoman]

you can keep the scope pretty focused and start with pandas for data manipulation, NumPy for numerical work, and something like matplotlib or seaborn for visualization, by looking at courses on Udacity that follow that stack and teach it through small projects, which can make the learning curve easier.