AI and Analytics for Business



Keshav Ramji, W’24, EAS’24

What is your area of study?
B.S. Economics with concentrations in Finance and Statistics

B.S. Engineering in Computer Science, with minors in Mathematics and Data Science


What drew you to Penn?
I was excited about being able to pursue an interdisciplinary education between business at Wharton and technology at Penn Engineering. I believe that my educational experience at Penn has really allowed me to explore both my technical and management interests, and the intersection of the two.


How did you originally get involved with the Analytics Accelerator?
I became a member of the WUDAC (Wharton Undergraduate Data Analytics Club) education committee in my freshman fall, and heard about the great experience that fellow members had in the Accelerator program. I was inspired to join! I became a part of Wharton Analytics Fellows in Spring 2021, and returned this semester as a Senior Analyst for the Zillow project.


What were your biggest takeaways from the Analytics Accelerator?
I feel that a central theme from the projects that I’ve worked on is to emphasize the overarching business objective at all times. When devising novel modeling approaches, it can be easy to lose sight of the business goal at hand, but the question of “so what?” is phenomenally important in identifying and communicating the business impact of the team’s work to the client.

My other major takeaway is with regards to Exploratory Data Analysis (EDA). I believe that it is very important to be purposeful and targeted in how EDA is conducted, which also ties back to how you view the business challenge you’re faced with. The method of going about EDA is often shaped based on the scope of the project, but in the early stages, you may run into approaches that fail – and that’s okay! The crucial takeaway is to be able to derive meaningful conclusions and build upon that in further analyses.


How has your experience with AIAB shaped your career goals?
I am interested in pursuing a career in machine learning research and data science applied to industries such as finance and medicine. I feel that my experiences through the Accelerator program, as well as AIAB-affiliated student organizations such as WUDAC and AI@Penn, have given me an avenue to apply my knowledge and hone my skills. I find that there’s immense value in being able to work on complex challenges faced by data-driven companies, and the ability to devise creative solutions is a skill that will certainly translate to my future internships and jobs.


What do you wish you knew when you began your journey in data analytics?
I believe that with the increased interest in applying machine learning to various problems, it can be very easy to forget about the use-case at hand. When I began my analytics journey in high school, my mindset was more along the lines of “how do I apply machine learning to this given problem.” I have since learned that the nuanced machine learning techniques need to be carefully selected for the data and the business goal. This is extremely critical for deriving value using these techniques for real-world applications. Other considerations that I have also come to appreciate with regards to machine learning and deep learning include model robustness and explainability, as well as the theoretical underpinnings of learning techniques, although perhaps this is best gained through more experience in the field.


In what ways does your analytical perspective creep into your normal, non-analytics life?
In many ways, I feel that my life is analytical! We live in a world where there are so many choices we need to make on a daily basis, and having a data-driven perspective can be incredibly valuable to ensure we make informed decisions where possible. Whether it be for fantasy sports or personal trading portfolios, or even routine shopping – I find that smart decision making can often start with analyzing the data available to you.