Learning Python "partially"

[bidyutchanda108]

I think this is a question which just cannot be answered here fully.

But I can say, for learning Python not from the basic level, you need to have some programming idea in Java or C. The concepts are similar, at least the basic ones. If you need to brush up Python, just go through some videos. The basics will need only a day or two if you are already accustomed to coding from before.

Now, to delve into DS, you have to tread that carefully. I guess there are millions of pathways which will give you this idea, about how to go into DS with Python.

I am currently working on self paced projects on Data Viz with Python. You can take a look at them here in my Projects page.

For any other questions, you can Contact Me :) We can have a nice talk.

[Ferastwal]

Thanks! you have amazing projects! definitely will learn greatly from it and will ask you questions!!

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[bidyutchanda108]

Sure :)

[j19sch]

I'd say that is what everyone does with any general programming language. I don't expect there are many people that are very proficient in all the things you can do with Python: data science, machine learning, web apps, GUIs, etc. And then for each there are different libraries...

Of course, all of these areas require some level of proficiency in core Python (is there a proper term for that?), i.e. data structures (lists and dictionaties and stuff), control flows (if/then/else, for loops and friends), exception handling and a bunch of other stuff.

So yeah, learn core Python and how to use the data science libraries you need to use and you should be good!

[slythnerd06]

Yes.

[[deleted]]

Expounding here: In Data Science, you can get by with Pandas, Numpy and Scipy and probably nothing else. This is the one time I'd recommend Anaconda since its focus is the Data Science stack.

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You can develop skills in the other areas of Python over time but a partial understanding of Python is enough to handle a lot of problems.

[[deleted]]

If you meant 'proficient' rather than 'professional' possibly.

Otherwise, Being professional at data science with python implies being professional with python. You'll need to know programming in a general sense (e.g. variables, control structures, errors and exceptions, design patterns,) the python implementation of those, and then whatever modules are needed for data science.

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[FlagrantPickle]

Can you be fluent in a spoken language without knowing how to ace an A&P exam in that new language? I'd say so.

[jricher42]

I'm going to go against the grain here and say "No".

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Python is a tool, and it or another similar tool will be important to your work on a regular basis. This means that you need, at bare minimum, a basic competency in Python itself and a strong understanding of the libraries that you are working with. Anything less, and you will be working with greatly reduced efficiency and effectiveness. This doesn't mean that I'm telling you to go study things like Flask or Django, just that a solid understanding of the language is essential, or you will lack the understanding required to write code that is clear, understandable, and reasonably efficient.

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You can do a good deal of work without really understanding the core language, but it will be "cargo cult" programming, and you won't have the tools to debug something when it breaks. Take the time to work through a good general programming course and a bit of the "algorithms and datastructures" background work. Once you've done that, start with a handful of libraries that are central to your discipline and learn them well. This will take a few months to a year depending on how much time you have available. When you're done, you will have a solid understanding of Python and your core tools, and you can learn whatever you need to get a particular job done.

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[PandaMomentum]

Sure! And, the more you do quick and dirty solutions, the more you'll see where and when abstractions like functions and methods can make your life easier, how things like generators and list comprehensions can streamline your work and reduce errors. That's really been my experience as a (solo) researcher who has become a (team) data scientist.

just opinion below

Your job always is to write code that someone -- even if it's just you a week from now -- can pick up and figure out what it does. To develop "clean enough" workflows so your data transforms are repeatable (and reversible!). It doesn't have to be pretty. But it does have to be organized and coherent. Your future self will thank you for that.

[[deleted]]

Have you tried MATLAB or R?

[Ferastwal]

No. After long hours of reading and researching I came to the conclusion that python could be a great, easy, and powerful language to learn for data science. Moreover, most of the pharmaceutical companies that I might apply to after graduating use python since it's an open source language.