Prior to enrolling in Flatiron School’s data science bootcamp, I was on the academic track, having completed a Master’s in Middle East and Islamic Studies and looking to pursue a PhD next.
The idea of such a drastic career shift, away from academia towards data science, seemed daunting and counterproductive at first. I was worried about sunk cost: would this career shift make my previous studies and efforts irrelevant?
Thankfully, I decided to make the jump anyway, and those worries turned out to be unfounded. The field of data science, as diverse in its applications as it is, is one where someone with my academic background can flourish and stand out, for two main reasons.
Reason #1: Overlap between the fields
Academia and data science overlap in a number of ways, but perhaps most poignantly from the perspective of research. While academic research deals a lot with qualitative analysis, deriving and refining or refuting standing theories based on comparing and contrasting case studies, the best and most compelling analysis cannot ignore the quantitative dimension.
Raw data and numbers are indispensable to a serious academic, and too often quantitative analysis falls to the wayside in favor of qualitative analysis.
An accomplished academic melds the two modes of analysis together, combining qualitative analysis through theory and ethnographic information with quantitative techniques that ground the analysis in cold, hard numbers.
Reason #2: Domain knowledge
A data scientist or analyst can only be as effective as the extent of their domain knowledge pertaining to whatever field they are engaged in.
I found that my experience in the fields of Middle East and Arab studies provided me with in-depth, nuanced, and sophisticated understanding of a region rife with complex histories and an even more complex present.
This domain knowledge gives me a competitive advantage in the field of data science, as I am better equipped to make sense of raw data thanks to my understanding of the nuances of the Middle East and Arab region.
This domain knowledge can be put to good use when it comes to ascertaining promising business opportunities in the Gulf, the Levant, or North Africa, deriving market insights from an understanding of the shifting political and economic spheres, or finding a deeper appreciation for those opportunities thanks to a historicized understanding of the present state of affairs.
Another example would be in the application of Natural Language Processing to the Arabic language, as it is a very diverse language.
You have Modern Standard Arabic used by most officials and institutions, however relying solely on this type of Arabic would yield limited results as the vast majority of the Arab world uses colloquial Arabic whose various dialects are distinct from Modern Standard Arabic, and diverge greatly from other dialects.
The same word can carry extremely different connotations when used in Egypt, Syria, Iraq, or Saudi Arabia, and this cultural understanding is crucial in Natural Language Processing, which still has a long way to be adequately developed in the field of data science.
Fortunately for me, in our increasingly interconnected and hyper-globalized society, the sky is the limit for opportunities to find synergy between Middle East and Arab studies on the one hand, and the field of data science on the other.
Mohammad Abou-Ghazala 