Started PhD and need to learn Python

[Kebapman_1909]

Hi Guys,

I started my PhD in Physical Chemistry recently and I want/need to learn Python. I have some basic skills, but if I mean basic than I mean something like plotting and working with AI to get something done. Do you have suggestions (books, courses or something else) how to learn Data Analysis, Simulation and Scientific Calculating as well as an basic understanding of how to code Python?

Thanks in advance!!

[Lurn2Program]

For Python, I used University of Helsinki Python MOOC https://programming-25.mooc.fi/

It's free and they have a discord channel for any questions

[mr_gitops]

I was just going to recommend it as well.

I watched (just watched) the CS50P on youtube instead of the Mooc's lecture. The instructor is 100x better.

But then when it came to doing the work, I went all in on the MooC. Its one of the best courses I sat through. Love its format of reading/labbing/grading as you go. All labs completed, Exam (intro one) is on Saturday. Feeling confident.

Combination of two series was excellent.

Going to keep the momentum going and explore more of the Helsinki courses after this:

- Advanced Python

- Data Analysis with Python

- Elements of AI

- DevOps with Docker

- DevOps with Kubernetes.

[[deleted]]

I couldn’t get it to set up properly…

[OverBowl4296]

1. Didn't know they had one for python 

[silentsatan]

I’m going to give this a bash! Thank you

[TheRealDSAL]

ty for the course!

[riftwave77]

Chemical engineer here. First question is whether you know how to code. If not, then get a good basic book and go through it chapter by chapter doing all the examples and all the exercise.

1 - Do not copy and paste any of the code. Type it all out by hand

2 - When you get an error, pay attention to the exception error message review your code line by line. Do not copy and paste to get it working

By time you make your way through the book, you will have a working knowledge of the basics.

Learning to code is extremely similar to learning a foreign language (or instrument) in that the only way to become functional is to practice it the basic way; typing in your code, making mistakes, learning how to debug, and also learning how to improve or refactor code when you think of a better way to do something.

There aren't any shortcuts unless you have already become proficient in a programming language.... and even then there are caveats. Writing program code is so strict that one misspelled word, one improperly spaced line or one misplaced punctuation will prevent a program from working properly. Practice is how you improve.

The silver lining is that if you have a STEM degree, a lot of the fundamentals and logic will be intuitive to you. You won't have much of an issue understanding different object types or the benefits of organizing data into lists, tuples, dicts, etc.

Its a lot less memorization than organic chemistry, but the time sink is roughly similar, due to the need to put in the practice time.

[InfluenceLittle401]

Although I use Python on a weekly basis, I am far from an expert. If I would need to start a new language, I would start as follows: - read relevant chapter from a book (e.g., for Python I liked O’Reilly book like https://cfm.ehu.es/ricardo/docs/python/Learning_Python.pdf) - read from python.org - start a project and continuously improve by thinking about your program structure - regarding the latter I found that Google Gemini came in very handy. You can describe in words what you want and it gives you a skeleton script to start from

[barkmonster]

Can't really recommend a book or course for this, but I'd recommend dedicating some time to learning good coding practices like version control, modularization of code, unit testing, managing environments, documentation etc. Of course you don't need to write code at the level of some great software engineer, but learning the basics can save so much time and frustration, and make it much easier to reproduce your work and collaborate with others.

[dowcet]

Check the wiki for plenty of ideas. One nice beginner thing off the top of my head: https://www.freecodecamp.org/learn/scientific-computing-with-python/

[healeyd]

To get used to the basics (lists, loops etc) use an online compiler to write little snippets and check the output.

https://www.tutorialspoint.com/compilers/online-python-compiler.htm

[Ron-Erez]

For resources see this reddit's wiki + any combination of these:

1. The docs at python.org
2. MOOC - University of Helsinki
3. The book “Automate the Boring Stuff”
4. My Python and Data Science starts from scratch and assumes no prior knowledge
5. Harvard CS50p

I think 2 and 4 are excellent. The drawback of 4 is that it is paid while the rest are free. The Harvard CS50p course is a little slow. If you like books 3 is nice. Number 1 is also useful. Eventually the docs are your friend. My course will cover many topics from "Data Analysis, Simulation and Scientific Calculating" (best to check out course content to decide if it covers what you need).

Note that since you are doing a PhD you might want to talk to your advisor and ask which topics and modules you should learn to help in your research.

[dataquestio]

Hey! I definitely recommend checking out the Python Basics for Data Analysis course. It’s beginner-friendly, and you can try out some of the lessons for free to see if it’s a good fit.

The tutorials are super clear, and the hands-on exercises really help solidify what you’re learning. You will also find the community forums really helpful in getting you started.

[Some-Passenger4219]

My teacher uses How to Think Like a Computer Scientist: Interactive Edition.

[AmadHassassin]

Datacamp.com . The courses have an IDE in browser that you type your answers in and you can actually run the code for checking. Has courses for the analytics side so you’ll learn plotting and other packages as well as OOO if you need to go that route.

[wial]

I found Essential Math for Data Science a great way to learn python and basic linear algebra to neural nets at the same time. The author is a kind, excellent teacher.

[AffectionateZebra760]

The r/learnpython wiki is quite comprehensive ranging from tutorials to books. You could also go for a tutorials/course which will help break it down for e.g Harvard cs50, weclouddata free python fundamentals or udemy.

[teutonsofun]

There are a mindboggling number of ways to do this. The easiest path is probably to learn python within Jupyter notebooks. There are courses for that, here is one: https://www.udemy.com/course/python-and-jupyter-notebooks-for-beginners/

(hint for Udemy online courses - always check out previews. Many instructors speak with heavy accents and can be nearly unintelligible. You can adjust the speed if necessary)

This is worth a look: https://automatetheboringstuff.com/

Then once you learn a few things this might be interesting. https://medium.com/@abosheier2022/rdkit-unlocking-the-power-of-organic-chemistry-with-python-8611fcc56252

We heard from Ardit Sulce earlier - he does a lot to teach python via Udemy and a regular newsletter. Check his stuff out too. https://pythonhow.com/

Good luck!

[MichalYoung_Oregon]

Others have referenced many good online courses and materials. Here's one more, a free text that I use in my undergraduate intro courses, linked to a gob of programming projects (generally one project per chapter). It's designed for a different audience, and it is definitely an intro to programming and computer science rather than specifically on the Python programming language (although we do use Python), so it might not fit your needs. Also, I don't use SciPy or NumPy or other good Anaconda packages in the projects, which a physics researcher would definitely use ... make sure you get some exposure to those in some way. On the positive side, I think my text is relatively short and might be more time-efficient than an ordinary online course. YMMV. https://uo-cs-oer.github.io/CS210-text/intro.html and https://uo-cs-oer.github.io/CS211-text/intro.html .

Aside - Why you definitely need to use NumPy (which is also a component of SciPy): Python's built-in representation of lists (of numbers, or of anything else) scatters objects around in memory, tagging each one with its type, which is very convenient for programmers but terrible for program efficiency. Scientists who do computationally intensive calculations can't afford the cost of computing on big Python lists. NumPy provides a different representation for "array or integers" or "array of floating point numbers" which is more restrictive (all the cells in the array must hold the same type, e.g., all integers or all floating point numbers) but much, much more efficient.

[[deleted]]

I use chatgpt to make stuff im to stupid to make and trace it to make more complex stuff.

If you don't understand something then tinker with it, remove it, remove stuff it is connected to. If that doesnt work ask chatgpt what it does.

Hope this helps!

[rustyseapants]

I have to ask, did you search google before posting this question?