Why data science?

I’ve recently left my research job to pursue a full time data science bootcamp at Metis. Some people have commented to me that this seems like a shift as I’ve worked in international development research the last five years. And they’re right that I’ve been reading and writing, not coding in Python. But that doesn’t mean data science is an entirely separate undertaking. To me, data science continues many of the threads that have been present in my work and life for years.

Here are just a few:


  • With a graduate degree and jobs in policy research, I’ve done plenty of writing. Most recently, I was the main website content writer for a Stanford research center, where I spent a lot of time translating research findings from econometric jargon to pain English. Jargon is handy as a shortcut but often alienates novice readers. One editor of mine told me “never use a five dollar word when you can use a fifty cent one.” At first it may feel like you’re “dumbing down” your research, but I’m a firm believer you can write about complicated topics in an accessible way. The fanciest data analysis in the world doesn’t have any impact if you can’t communicate your results in a digestible way. It takes more work, but when it broadens your reach, I think it’s highly worth it.

Puzzles and problem solving

  • My toy drawer as a kid was all puzzles: brain teasers, puzzles where you had to slide tiles around to get a certain numerical order, or fit different sized pieces into a certain shape. Data science feels like one puzzle after another — why is this code producing this error, how do I design a function that lets me do X, how can I improve this algorithm, etc. And data science as a whole can be used to understand the greatest puzzle of all: the world around us.

Tangible impact on the world

  • One of the reasons I got a masters in international development was because development work has a tangible impact on the lives of others. Development centers on poverty alleviation through a myriad of ways — job growth, nutrition, education, etc. One of the qualms I have with development research is that publication of academic journal articles takes years and the research is not always directly applicable to policy design, at least not immediately. I believe data science holds so much potential for yielding practical and tangible insights and will be a powerful tool for international development work.

Math and numbers

  • I came into college wanting to be a math and Spanish double major. I followed through with the Spanish (and that was my entry into Latin American Studies) but I quickly got turned off by the theoretical nature of upper level math classes that felt rather futile. I turned to economics instead and enjoyed analyzing data in Stata and building visualizations (albeit in a clunky way). I’m delighted by how powerful Python is for analysis and how matplotlib and seaborn are for visualization. I’m really looking forward to these next weeks at Metis where I get to pull my math brain back out for a very practical application of math: machine learning.

Persistent perfectionist

  • Someone once told me that there are no strengths or weaknesses, just positive and negative manifestations of characteristics. At worst, this means I’m a stubborn, perfectionist who asks too many questions and is too hard on myself. At best, this makes me a persistent, detail-oriented, immensely curious person who has a critical eye that excels at debugging. I believe data science is an outlet for my best self: persistent, detail oriented, curious, critical mind.

It has been a challenging first week at Metis that has pushed me well outside my comfort zone. After all, I only started learning Python in January. It has been difficult to absorb all the information that is being shared and I’m sure some of it has slipped through my fingers and I will re-discover it at a later date. But I am delving into exactly what I want to learn. And it is a deeply satisfying way of combining so many things I love.