Code reuse and modularization without breaking code inspection. Avoiding unreproducible bugs caused by whoever that's testing or even the data scientist himself doing out of order executions of cells. VCS and code review through pull requests. Since you are a software engineer maybe you can explain how you are going to make ipynb files work well with I said use it for what it does well ie: simple script, iterations and EDA. I can say the same thing about people that keep trying to make Jupyter Notebook sound like the end all be all greatest new thing since bread - that they simply have never tried, got proficient or understood the points with normal development tools. Have to admit that the CLI screenshots in the book intimidated me, but I'm OK learning it since I know CLI is kind of a part of being a techy and it's probably about time I got more comfortable with it. But since I am new with learning coding languages, if Jupyter is good as a starting point, that's OK too. JUPYTERLAB VS NOTEBOOK HOW TO
I think I may just like to learn how to use Python more because I'm just a very analytical person by nature and maybe someday I'll use it to put together analyses of Coronavirus data. I do realize that doing data science with Python is probably more than would be needed in an FP&A role, and that's OK. I'd like to move into an FP&A role and I know understanding Python is sometimes advantageous. I don't use it for work or anything, yet. My goals: Well, I guess I'd just like to expand my knowledge of Python.
Before I go and install another program and head down the path of learning it, I'm wondering if this is the right tool to be using.
In this book, the author uses Jupyter Notebook. I just picked up Python for Data Science for Dummies - was in the library (yeah, open for in-person browsing!) and it looked interesting. I have very basic skills in Python using P圜harm.
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