Your Average "Hello, World!"

For those of you that don’t know, ‘Hello, World!’ is the first thing you print when you are learning a new programming language. Programming languages are just tools to get a job done quicker. Personally, my favorite tool is R. If I had the standard data science mindset I would say Python but to be honest I’m not too confident in Python just yet. I like using R for my data engineering, feature engineering, and sometimes visualization (most times I get too lazy and use Tableau). The only reason I clamp on to R so much is because one of my mentors told me, ‘Danish, would you rather be very good in one programming language or okay at multiple languages?’ This stuck to me and I chose my first very good language to be R.

When it comes to Data Science there were two ideologies that stuck with me and I will hold on to forever:

• Data Science is for everyone
• Data Science is a team sport

The first ideology comes from the boot camp that I attended in New York City with Data Science Dojo. In my boot camp I was the youngest, I was only 18 and I felt left out at first. But my instructors helped me see past that and made me realize

that with the knowledge that I have I am useful to anyone that has data. And this really impacted me when it came to my thoughts on age barriers. I’ll use some personal examples to elaborate on this ideology more:

• My sister, a bioinformatics major at Thomas Jefferson University. She wants to pursue a career in gene therapy, I immediately thought, what if I used the aspect of predictive modeling for her to figure out what gene therapy is appropriate for her future clients?
• My dad volunteers as the Medical Director for MASS Clinic, a non-profit clinic that I also volunteer my time at. This clinic is way ahead of the game when it comes to reporting and data collection. Working at other clinics I would envy my dad’s reporting system which is maintained in Salesforce. Working at front desk I took notice as to how many people don’t show up to their appointments, what if you made a predictive model to see what are the chances of a certain person not showing up to their appointment?

I could keep going with these examples but you get the point.

The second ideology comes from NLP Logix, an advanced analytics and machine learning data services company I interned at for a couple months, these guys are my roots and to this day I go back to those guys for help. Anyways, do you remember in High School when you would always be assigned a group project? Either you were the person that let the smart kid do the work or you were the smart person that everyone else relied on. In the statistics and computer science world I don’t feel that is the case, it’s like mountain climbing with your friends as you’re all interlocking arms, as hard as that analogy may sound it’s not that hard because you’re using your mouth and laptop and not your legs. Data Science is a good team sport because you constantly need that one person that critiques your ideas/methods, that one person in your mountain climbing group that is encouraging us to go this way instead of the obvious way because he knows there is a mountain lion ahead. Being on a team encourages us to keep up and work at the pace that the strongest one sets. By the stronger one I mean the fastest climber.

Well ... This is my first blog post and I hope you enjoyed it. It took me a while to think whether I wanted to do this or not, but this is more for me than it is for my reading audience. Part of me wants to come back to this blog post 10 years later and be like, ‘Ha! What a loser, this guy was so stupid.’ Another part of me made this to inspire other young programmers to get started early like I did. I was introduced to the concept of data science when I was 16, and two years later I am confident enough to call myself a Data Scientist, and I am proud of that.

~ds.