Tell us a bit about yourself.
I recently graduated from Pennsylvania State University with a bachelor’s in economics. I have worked in both engineering and user experience research roles at NASA, eBay, PayPal, and Facebook in the past. Currently, I am working on machine learning projects as a subcontractor for Mark Cuban Companies while I finish up my Master of Information and Data Science (MIDS) degree.
What initially attracted you to the data science field?
As an undergraduate student in economics, I was exposed to the world of econometrics and found the art of extracting insights from data to be fascinating. This fascination led me to various job roles that allowed me to work with data, including an internship at eBay where I used a data-driven approach to identify user experience pain points. The field of data science was a natural interest to me. I wanted to expand my analytical abilities in order to add value in roles that allowed me to work with larger data sets.
Why did you decide to pursue a Master of Information and Data Science?
As a data-driven person, I always knew I wanted to pursue a career that would allow me to work with data in some capacity. I knew that I needed to expand my skill set when it came to data science skills, and the MIDS program presented itself as the perfect opportunity to learn the skills that would allow me to add more value in a data-centric position.
Why did you choose datascience@berkeley?
The datascience@berkeley program offers a fantastic curriculum for aspiring data scientists that can be customized depending on a student’s interest. It also offers unparalleled flexibility for working professionals, which is a massive advantage for those who cannot afford to stall their careers in exchange for a formal data science education.
What is the I School's advantage?
The School of Information’s (I School) advantage is in its extended network of instructors, students, and faculty. Being a part of the I School network means having lifelong connections to some of the most renowned educators in the information space as well as some of the brightest minds in the field.
What has been your favorite course at the I School and why?
Experiments and Causality was my favorite course in the MIDS program. It reframed my perspective on experimental studies and allowed me to develop a much more critical understanding of what constitutes a “good experiment.”
What is an information challenge that intrigues you?
It intrigues me to consider the manner in which the increasing amount of data that is collected in traditionally intuition-driven fields, such as marketing and sports, will be leveraged for decision making and the role that machine learning will play in these fields.
Which skills and tools covered by the program do you find most appealing? Why?
I am especially interested in deep learning. It is a hot topic in the world of data science at the moment, and it is fascinating to consider the number of potential applications it has in countless industries.
How are you able to apply what you are learning to your current position?
I am directly applying the knowledge of neural networks that I gained from Natural Language Processing with Deep Learning to my day-to-day work in my current position. I also use many of the principles I learned from the Experiments and Causality course to set up meaningful experiments for the e-commerce web sites I manage.
What surprised you most about the online learning environment?
I was especially surprised at the level of engagement that can be achieved on an online platform. The MIDS live lecture experience is a near-seamless online translation of an in-person lecture experience (and in some ways, it is even better).
What advice do you have for aspiring information professionals/data scientists?
I would advise aspiring data scientists to find projects that they find interesting and to work on them as practice. I have found that there is no better way to master data science skills than by solving real-world challenges using data science methods and techniques. Kaggle is a great place to get started.