About Me

Professional Poker
One day while attending college, I stumbled upon a nightly poker game. Having had no exposure to poker I was shocked at the analytical nature of the game. At its root, poker is about making optimal decisions based upon imperfect information. I began playing poker nightly, supplementing my political science education with poker books and poker discussions on online forums. Within a year, I was an online poker professional and have been ever since. Daily study and routine strategy evaluation are among the minimum requirements to maintain a successful poker career. Optimum decision making requires an ability to see through statistical noise; short term results can’t be the focus of poker analysis. Just as I had stumbled upon poker that day many years ago, I was fortuitously introduced to the field of data science in a conversation with a friend. Almost immediately, I knew this was the next field I wanted to take on.
Data Science Transition
Luckily, I had plenty of help in my transition. A good childhood friend had already made the seemingly unique transition from professional poker player to data scientist. I say seemingly unique, as strangely enough it seems many poker professionals have realized the similarity in data analysis and poker analysis, among the most famous FiveThiryEight’s founder Nate Silver. With the guidance of my friend, my journey from professional poker player to data scientist enthusiast began. My friend has acted as my own stack overflow the entire time, and I owe much of the success I have had in learning to him. There have been, and still are, plenty of roadblocks and wrong turns along the path. Poker prepared me well for these frustrations. After playing millions of hands, you learn to deal with statistically improbable results known as “bad beats” by focusing on the long-term. Frustrations, like those experienced in debugging a program, are inevitable. What matters is a focus on the value of the finished product.

Continuing down my path of learning, eventually I felt confident enough to complete the Data Scientist Track on DataQuest(2017), and with a strong foundation in Python I decided it was time to formalize my education. I applied and was accepted into CUNY SPS Master Data Science spring class in 2018
Graduate degree
Equipped with a strong understanding of Python I entered my first semester ready to take on the world, only to find out that neither class I would take first semester would be using Python. While python is becoming the open source language of choice all throughout the data community, academia still largely prefers the statistical capabilities of R. This was frightening, I struggled for several years devoting much effort to learning Python. Now I would need to learn an entirely new language in under a semester? In addition, I would need to allocate enough time to my foundational math class as well as my studies and work in poker. The first semester in the program was difficult, but it made me a stronger more capable data scientist. I came out of the first semester with a strong understanding of R, and a preference for using it over python.
Current Skills
Heading into my last semester, I have been exposed to all different facets of the data science community. My strengths are in R and Python with a focus on data visualization and exploratory data analysis. My education has equipped me with a strong understanding of the statistical assumptions behind many ML models with the ability to conduct cross validated algorithms with model tuning in both R and Python with Caret and scikit-learn. In addition, I have some exposure to big data via a big data class I took in Python in my Masters program. Realizing the importance of SQL, I am continuing to take additional course work on LinkedIn Learning as well as Data Camp to improve my ability to work with relational databases. I use these supplemental learning platforms to learn Tableau as well, admittedly I am jealous of the drag and drop capability of Tableau. The area that interests me the most is NLP, it seems its applications given all the social network data available are boundless.
Whats Next?
This about page is just the beginning of my journey. I am eager to start applying all the skills I have acquired to solving complex real-world problems. From here, you can check out my Portfolio page .