If you’re just now learning python as a PhD student maybe start smaller with the less scientific more fundamentals books of dive into python and learn python the hard way. But if you still don’t know you need to realize that any advice we give you is going to be difficult because we don’t know what your preferences are. So I would recommend you read the first three chapters of each book and see which one is getting you the most knowledge per page. More on reddit.com

I’m not a fan of any of FreeCodeCamp’s Python certifications, to be honest. They are all in the “watch a 10 min video then answer a quiz question” style. The interactive content is where FCC shines, and none of the Python certs are all that interactive. More on reddit.com

If it is counter intuitive better start working on your intuition. More on reddit.com

Regarding the general workflow, I am really happy with my current setup: Whenever I do anything, I start of in a Jupyter Notebook. Once I am happy with certain pieces of code or I feel that the complexity outgrows the context of a notebook, I start to move these pieces of code into a package, I create for every big project I am working on. Such an packagage can have multiple modules and only consists of functions, classes and some helper variables. So no "running" code in there. For every new function or class I put in there, I will write a proper docstring, clean up the code and write some unittests. Inside my Notebook I will import this package and use the functions there. This can be a very fast and dynamic process. At the end I have a big package with a lot of complex code and many notebooks and smaller scripts using this code. If you are interested in this work flow, let me know and I can explain it further. Regarding general structure of functions and code: Try to write functions that are very general (e.g. do not hard code parameters, and maybe consider obvious alternative usecases, if it is not to hard to implement them). THis greatly increases the reuse value of a function. Regarding classes: There are a couple of usecases for classes even in scientific computing. The first and easiest one is as a form of structure. You can use a class to group together functions (methods) that accomplish similar things. In this very simple case all methods would be static methods and the class just an container. However, I often like to use classes to make use to generate a nice syntax for certain complex functionalities. E.g. consider some sort of mathematical filter. A filter needs the information about its parameter and it needs the needs some way to be applied to some data. Then a filter has multiple outputs: the filtered data, maybe some parameters that were calculated along the way. All in all there is a lot going in and a lot coming out. While I could write a function, taken my data and all filter parameters as inputs, and returning all my outputs. However that can get very messy if you have to use that filter a lot. A nicer way might be to implement a class for a filter. This class would have all filter parameters as class attributes and a class method, that can take your data as argument and actually applies that filter. If you want to see an amazing Implementation of that, see look at the class based classifier interface of scikitlearn (e.g. here http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares ). If you are interested, I can give/show you specfic examples were I have used this concept. General advice: Learn the scientific python stack (numpy, scipy, scikit-{image,learn}, pandas, ...). If you want to do anything serious use the tools they provide and do not try to build complex mathematical things in pure Python. These packages are awesome, but they will heavily change the way you have to write your code (e.g. avoiding loops) I apologize for this wall of text and the general lack of structure in this answer. I am little bit tiered from doing writing Python all day :P. If you did not understand something, please just ask! I will be happy clarify things or give you further details! More on reddit.com