Ignacio Arranz: Voices of Penn Engineering Master’s Students
This article is written by Ignacio Arranz, who is currently a first-year student in the Data Science program (DATS) and is part of the first DATS cohort. The DATS program is the newest master’s degree offering at Penn Engineering, preparing students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications.
In 1896, at the age of 30, the painter Wassily Kandinsky gave up a promising career teaching law and economics to attend art school in Munich. I like to think that I made a similar choice at the same age: after working as a management consultant for more than six years I decided to pursue a master’s in Data Science at Penn. On my 31st birthday, one week after starting classes, I was in my first hackathon ever building a wireless oximeter at PennApps with classmates Garvit Gupta, Yu-Ho Hsieh and Abhijeet Singh.
I am originally from Argentina, where I got my undergraduate degree in Industrial Engineering at Instituto Tecnológico de Buenos Aires (ITBA). I entered the world of consulting at EY shortly after graduation. After a couple of years working in Argentina and Chile, I transferred to the New York office in 2013, where I spent my last years with the company supporting the design and development of algorithm-based “robo-advisors.” There I witnessed how the data available at companies was not being utilized to its utmost potential, as most people (myself included) lacked the skills to manage it. I realized I needed to get back to my engineering roots and develop the technical skills necessary to help run these types of projects. A master’s in Data Science seemed like a perfect choice.
Data Science is a relatively new field and so are the master’s programs available. After researching many programs and scholarships I learnt that ITBA was offering a scholarship for its alumni to get a Penn Engineering master’s degree. This seemed like a unique opportunity and talking to other ITBA alumni who had done engineering degrees at Penn confirmed this, as they strongly encouraged me to apply. I applied to both Penn and the scholarship and after securing both, quit my job, got a new visa and moved to Philadelphia.
Of all engineering degrees, I chose a master’s in Data Science to understand and be able to build machine-learning solutions. The courses I am taking are essential for this objective. One of my favorite things about the curriculum is the strong presence of computer science courses (CIS519, CIS550, CIS530 and CIT590, among many others). Given that my programming experience was not strong, taking these courses with other computer science master’s students set me up for a great learning curve: having classmates like Garvit and Nanthini Balasubramanian, who patiently explained concepts to those of us without a computer science background, speaks to the collaborative environment at Penn Engineering. The Data Science program takes students from many different backgrounds and the curriculum’s flexibility allows us to mold our learning program based on our experiences and interests.
Projects are definitely my favorite part of the courses. In “Database and Information Systems” and “Intro to Machine Learning,” we had the opportunity to turn concepts into tangible products. I enjoyed building a recommender system for Airbnb listings, as well as a webapp that assists commuting through New York, combining public transportation information with CitiBike locations.
This semester I have been working on automatic text summarization and sentiment analysis of restaurant reviews for my “Computational Linguistics” and “Big Data Analytics” courses, respectively. My personal policy of finding new teammates for every new project has helped me meet students from different backgrounds and careers, making it a more enriching learning experience.
Student clubs and groups have been one of the most rewarding aspects of Penn. I was able to join the Penn Data Science Group, founded and run by Ben Lindsay, who welcomed me to join as VP of Careers, where I helped build the resume book for our group and organized a Data Science in Fintech panel, with the goal of building stronger relations with data science professionals. After a few months Ben invited me to replace him as president. Working with such a diverse team of undergraduate and graduate students from varying degrees and schools at Penn has taught me how broad the field of data science is. We host talks and panels with data science professionals, coordinate Python intro courses, organize learning projects with data science professionals, host web-scraping workshops and build teams that compete in Kaggle projects.
On top of courses and student clubs, Penn provides many additional resources for students to pursue their interests. At Penn Wharton Entrepreneurship’s VIP-C incubator, I have met a community of students working on entrepreneurial projects and pursuing business ideas. Using a database that outlined the interests of many of these students, I was able to connect with Eddie Kong, and we decided to start a project together. Eddie and I, along with my classmates Nanthini and Aditya Kashyap, have developed a chatbot that can book doctor’s appointments automatically. We are looking to continue to build and grow this project over the next year.
The Wharton Customer Analytics initiative (WCAI) was another great opportunity to put my recently learned skills into practice. There, a team of us helped Coqovins develop their wine recommender system and build algorithms to classify wines. I enjoyed even more that we had the chance to support the business of a Wharton MBA student, Quentin Chalvon Demersay. This semester I have been a technical assistant for a similar initiative, the Wharton Analytics Fellows, which has given me the opportunity to learn about many projects, and help other students overcome the hurdles I encountered on my WCAI project last year. Both of these initiatives give students the opportunity to not only work with real clients, but also understand that explaining the outcome of the algorithm is often more important than the algorithm itself.
I’m looking forward to applying what I have learned so far during my Data Scientist Internship in Facebook’s Analytics team this summer. There I will be using data science to support product decisions. Looking ahead, my main interest lies in Natural Language Processing and I hope to focus on this segment of Machine Learning during the next years of my career.
I believe that being a student at Penn is like being part of a professional incubator, giving me countless resources to become a better professional. However, my learning experience wouldn’t be the same without the great friends I have made — whether it’s bike riding next to the Schuylkill River, playing soccer at Penn park, or bunking at a friend’s place when you get locked outside of your apartment (life lesson: always give a friend a spare copy of your key), it’s the people that I am meeting what I value the most. I look forward to my remaining year here and making the most out of it.