Far from endless number-crunching, data science is a mixture of analysis and art.
By Lin Song (Machine Learning Expert and Data Scientist, ZestFinance)
Data plays a major role in our daily lives. We create data when we shop, visit a doctor, download music, order food, track our steps and more. All those experiences produce data that can provide insights, and in turn, if analyzed correctly, provide better quality of life for all of us. The company I work for, ZestFinance, is a technology startup that uses machine learning and large-scale data analysis to underwrite with the goal of providing fair and transparent credit to everybody.
So who does the analysis, and who interprets the data? Data scientists, like me. It’s a field that is exploding.
In fact, the job of data scientist was invented just a little more than five years ago. And it’s a field that more women should be exploring.
Data Science Explained
Big data and data science have been the buzzwords of late, but what is data science? In short, data science is the process of gathering data from thousands of sources, analyzing that data,and extracting actionable knowledge from the data to make informed decisions and predictions.
The data is initially presented in raw form which can include text, images, videos and more. The field combines disciplines in analytics, math, engineering, computer science, modeling and statistics. Data scientists – or those professionals who practice data science as a career examine the data from many angles searching for new trends and insights. We’re curious and inquisitive, so we love doing this. Data scientists need to be part analyst, part artist – an analyst to crunch the numbers, and an artist to be creative in examining the data to uncover new insights.
Career Paths to Follow
There are many different career paths you can choose from in the data science field. I like to think I started on a conventional path that took an unconventional turn, which led me to where I am today.
As an undergraduate, I studied biology and chemistry and my postgraduate studies focused on biostatistics and computational genetics. It wasn’t until I started working on my dissertation — developing a new machine learning algorithm to analyze genetic data — that my ideas of a career shifted. I came to the realization that I could apply the data analysis skills I was learning from genetics to help improve the lives of others — in any field.
Fast forward a few years to when I was looking for a job. I was reading through ZestFinance’s website when I came across a video by our founder, Douglas. It detailed how his sister-in-law, who is a single mother, approached him for money to buy new tires for her car so she could continue to work her job and take care of her kids. He lent her the money but was left worrying about who would have given her the money had he not been there. That’s how ZestFinance was born.
This really resonated with me; I got excited about developing and working with big data and machine learning models to help improve business processes, and hopefully improve everyday lives.
In some regards, my career path has changed drastically, but in many ways it’s still the same. While I no longer work in genetics, I still use the data science and machine learning skills I developed in the past at my current job at ZestFinance.
We are one of the handful of companies applying data science in unconventional methods (big data and machine learning models) to traditional industries such as financial services and banking. Applying the same kind of advanced math, I’ve analyzed genetic data and credit risk, and I imagine I could apply these same techniques to a multitude of other problems and industries. Our daily lives are flooded by big data – we use big data for routine activities like logging our workout and even checking out at the grocery store.
A Shortage of Talent
While we continue to see even more applications for data science, there is a serious shortage of professionals — particularly women — in our field in the U.S. and globally. According to Accenture, nearly 80 percent of the data scientist jobs created between 2010 and 2011 still have not been filled, and that problem is only getting worse.
What’s more, the Association for Psychological Science notes that women occupy less than a quarter of the science, technology, engineering and math positions. The 2013 Burtch Works Study goes on to show that data scientists specifically are even less likely to be women. As of just two years ago, only 12 percent of data scientists were women!
We need more smart, inquisitive and driven individuals to join our field. The opportunity for women is there to make a huge difference in people’s daily lives and industries as diverse as genetics or finance.
How do you use data in your business?
Image credit: Bloomua via Shutterstock
About the guest blogger: Lin Song is a data scientist at ZestFinance, where she applies big data modeling and machine learning algorithms to reinvent underwriting and how credit decisions are made. Lin holds a Masters in Biostatistics and PhD in Computational Genetics from UCLA, and a bachelor degree from Tsinghua University in Beijing. She lives in Los Angeles with her husband and son.