Why civil war and an immigrant mindset can make you a resilient data scientist
In life, we don’t get any do-overs, but we can certainly start over.
Getting comfortable with being uncomfortable is what starting over is all about.
Growing up in a civil war was no picnic, but as many of my Lebanese compatriots can attest, it also strengthens your resolve, fortifies your grit, and boosts your resiliency. I have been part of successful businesses and part of ones that have failed miserably, but neither success nor failure is constant.
The only constant is change. And our ability to rebound from challenging situations is directly proportional to how much resolve, grit, and resiliency we have accumulated along the way.
For the past couple of decades, I worked as a C-level executive for technology companies in the Government contracting space and the health and wellness industry. In the past year, I got the urge to get back to my technical roots (I have BS and MS degrees in Computer Science). I learned Python, dabbled with AWS’s dizzying array of cloud services with an emphasis on machine learning, and built a machine learning classifier through Udacity’s nanodegree ‘Artificial Intelligence Programming with Python’.
I was recently accepted into the Flatiron School Data Science Fellowship in Washington, DC. It is a 15-week full-time immersive program that covers exploratory data analysis, descriptive statistics, advanced data retrieval, statistical analysis, models of machine learning, big data, deep learning, and other advanced topics. Some of my classmates have Ph.D.’s in astrophysics, philosophy, engineering, public administration as well as medical doctors, while others have master’s degrees in mathematics, statistics, physics, economics and computer science.
The program is intense — it is a boot camp — but going through it with this caliber of talent is transformative. It has only been 3 weeks since I started my fellowship and I cannot believe how much I learned already.
Numpy, pandas, scipy, matplotlib, seaborn, and sklearn (some of Python’s libraries) are not for the faint of heart, but that sniper on my street prepared me for this challenge. Statistical models are a beast, but they are a breeze compared to living in three continents and immigrating 26 years ago to my new home. I have seen more conflicts with git than the entire Lebanese war :)
Data that is cleansed and harnessed holds so much power. It can be a catalyst for change as it fosters new insights and promotes new levels of understanding. We live in an era that is ushering in an unprecedented advancement in technological breakthroughs and creating along the way new types of jobs that require a unique blend of skill-set (communication, presentation, analysis, domain expertise, programming, AI/ML, analytics, business intelligence, linear algebra, statistics, and calculus).
According to an article published by Bernard Marr in Forbes Magazine, “over 90% of all the world data has been produced in the last two years alone and each day we produce 2.5 quintillion bytes of data (a quintillion is a number with fifteen zeros), but that pace is only accelerating with the growth of the Internet of Things (IoT)!
I want to be part of this new army of data scientists who are revolutionizing every field in every aspect of life. “I want to put a ding in the universe,” said the late Steve Jobs, and I, too along with my fellow data scientist are arming ourselves with the necessary skill-set to decode this daily tsunami of data using science to put our ding in the universe.
Sources:
Marr, Bernard. “How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read.” Forbes, Forbes Magazine, 11 Mar. 2019, www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#3dd740e360ba.
