Introduction

Good morning! Good afternoon! Good…whatever time of day it is when you are reading this. Or perhaps I should start with that famous computer science introductory example, “Hello World!”, which in Python is print “Hello World!”

Allow me to introduce myself. I am an aspiring data scientist. I am currently taking a data science bootcamp to learn Python, and also attempting to learn R. I have a background in Economics and Mathematics, which helps, but I still consider myself “aspiring”.

If you ask me what I mean by “data scientist”, I would respond with what it means to me, which is to be someone who efficiently collects and analyzes data to generate insights that matter to the top or bottom line of a business. That insight could be whether to introduce a new coffee to the portfolio, or where to open a new store. It could be improving recommendations for what to watch next on HBO so that HBO can compete against Netflix. Essentially, I aspire to be the guy who informs, if not actively creates, the business strategy using data science as one of my toolboxes.

Other people would define data science and being a data scientist differently. I look forward to those, because I am learning.

Back to my “Hello World!”. Why am I writing? I am writing as part of my learning process. I am starting a journey towards being a data scientist, and I want to document the process for four reasons:

1. By documenting what I am learning and how I am learning, I am proving to myself that I actually learnt it. This is similar to the Feynman technique for learning that Scott Young talks about here.
2. By sharing my journey, I may inspire other people who want to embark on a similar self-improvement venture;
3. By being public, I may get feedback (positive or negative) that helps me by pointing out something I am missing, or resources I can dive into to better understand a topic I am learning.
4. By practicing writing and gaining feedback, I may even become a better writer.

I plan to write about the following topics as I learn more about data science, mainly because they fit with why I am writing in the first place:

1. Functions and pieces of code that I found useful;
2. Projects and analyses I perform, along with how I performed them so that I can get some critiques and perhaps illustrate code I learned in an interesting context;
3. Perspectives on Data Science from practitioners in a variety of functions and industries, since I plan to meet as many as I can as part of discovering more about what I can do with data science.

I also hope that these topics are interesting to people who want to know more about data science. If they are not, I have no doubt someone will say so.