Does Ouroboros Have a Beginning? Getting Starting with Python

April 13th, 2020

So I’ve determined the direction I’m orienting my life → towards data science.

Now it’s up to deciding upon the steps to get there. It’s very different, after having spent a lifetime in academia, to now be completely self-directed. I could see how it would be very easy to get overwhelmed by the endless amount of information out there and not even start. But I am motivated by an undercurrent of passion and determination toward a clear vision, and so we begin.

I have identified the following skills which are fundamental to data science: programming, data visualization, data analysis, interacting with databases, and collection of data from various resources. To this end, the learning plan I’ve developed begins with developing proficiency in programming with Python, then learn data visualization with MatPlotLib, data analysis with Pandas, databases with SQL, and finally learn web scraping. I am open to changes in this plan as I gain new information about industry standards and best practice, but this is an outline from which to start my education.

Why Python?

Python is the industry standard for Data Science. In exploring preference between Python and R, I still favored Python due to its more robust nature as a full programming language, more versatility, and its gateway into AI and Machine Learning. R, while potentially superior in data visualization, seems preferred mostly within academia, and I want instead skills that can translate to a wide range of problems across any industry. Furthermore, Python is known to be highly beginner-friendly, due to its straight-forward syntax.

Next came choosing an IDE. I went with PyCharm after reading many reviews of all the available options. It is made by JetBrains whose other IDEs are preferred by programmers I know and came in highest rated across many articles for 2020, its accessible to beginners, is color coded for ease of readability, its has high aesthetic value (which I’ll be honest, I care about), is highly functional with debugging available etc. and is open source. Plus, its name reminded me of the charm quark (my favorite quark, by far.)

After installing both Python3 and PyCharm, I encountered my first problem solving challenge! Setting my PATH variables such that I could execute my new “hello world” module from the Command Line, and relearning relevant cmd commands to navigate to the relevant folder to execute the program. It’s not much, but it was a good refresher and was rewarding to make work.

I decided to begin my studies by working through the textbook “A Byte of Python” by Swaroop.

One thing I had to decide upon early was quotation convention for strings. The PEP8 doesn’t have a recommendation for best stylistic practice concerning this matter and they functionally do the exact same thing — basically pick one, and stick to it. I have chosen double quotes and I’ll explain why. First of all they look more like text and I find them more aesthetically pleasing. Also, I feel its far more likely for a string to contain a single stray quotation mark, for instance as an apostrophe in a contraction, than a double quotation — so using double quotes to contain strings eliminates potential difficulty with the existence of a stray quotation trying to end the string early.

So now I’m set up with my program, textbook, and conventions, and am eager to learn!