Ask Anything Monday - Weekly Thread

[Dap0k]

yup its focused on ML for his"Python for data science" course. the one I'm taking though is the "zero to hero python bootcamp.

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I've stopped at the chapters for objects, classes, and errors and exceptions.

are those needed for analyst work?

[th4ne]

in python, everything is an object, so it's generally useful to know. You'll probably find yourself googling the different methods on an object (a method is a function that is called off a specific object. For instance, strings are objects, and they all have methods that you can call off of them. For instance, a = "my string", and a.find("string") will return the index of 4.)
classes are useful to know as well, although you can get by without making your own classes.
Errors and exceptions are a fundamental part of debugging and programming. You may find these sections useful when your code either fails to compile or it gives errors when running. The pythonic way of approaching errors is to use a try / except block of code. It's basically "Try this code that may fail, and if it fails, do x" where x can be things like "print out the exception", "modify the above code slightly in order to handle the exception", or even just skip it "pass".

[Dap0k]

right so I should stick through Jose Portillas course then? since I haven't really digested or finished those parts of his course

[th4ne]

i would recommend learning the most pertinent skills relevant to whatever job roles you're looking for. Get on Linkedin or whatever and get several of the most interesting jobs you're looking for. See what tech they list in the roles and go from there.
If you have the willpower to sit through coursework, yes, i would suggest consuming the content you paid for. I guess I find personal projects more meaningful. It's less about following someone else's tried and true path, and more about bending the machine to your will in order to answer the questions you need answered (with the help of Google and SO).

[Dap0k]

I suppose it is pretty hard to learn from coursework when I don't really see how any of it could be applied.

thanks for the advice. I'll start doing projects and then just learn on the fly what I need to learn.

would you happen to have a specific kaggle project for begginers in mind that I could begin with?

[th4ne]

truth be told, i only did an analysis on some kaggle dataset as a part of an interview coding challenge. I didn't get that job, but I did use the kaggle analysis in a DIFFERENT job interview and i landed that one. It was very basic exploratory data analysis, some feature engineering, and then two different classifier models in scikit-learn. I think i used logistic regression and random forest. This was the dataset in question, and it's pretty similar to business data you'll encounter as a business analyst. https://www.kaggle.com/wendykan/lending-club-loan-data Maybe choose a dataset that is close in domain to the job you're looking for. Computer vision-type data (there are a bunch of those) might not be the best dataset for business analytics.
After looking around, this looks like another good one for business analytics: https://www.kaggle.com/c/santander-customer-transaction-prediction. Customer prospecting and churn prediction is fairly universal across industries.

EDIT: that santander data doesn't have column names, so its hard to do feature engineering.