Dat Nguyen

Though recently established, Penn’s MSE in Data Science has gained great traction over the past few years and stood out as a very competitive and prestigious data science program.”

Contact Information: datnguyen@seas.upenn.edu
Calendar: https://calendly.com/datnguyen_upenn

Degree(s) M.S.E. in Data Science, University of Pennsylvania

Hometown: Hanoi, Vietnam

What field of Data Science are you interested in?
Recommender System, Natural Language Processing

What drew you to study Data Science at Penn?
First of all, Penn is a great name with a very strong alumni network. Second, the program offers great flexibility with what areas of Data Science you want to study. Last but not least, Penn’s professors are top notch, and your peers are very talented. Even though it is a rather new program, it is growing very fast with a decent job placement rate.

What are some classes that you have particularly enjoyed and why?
CIS 520 (either Fall or Spring version) is a very solid class that gives you enough background in machine learning for other classes and job interviews. CIS 530 is another great class if you enjoy NLP. I really like the class structure as well as the exercises/homework assignments.

What internships have you had? If you know your post graduation plans, please include here.
I did a spring internship with NBCUniversal – Peacock TV team, focus on improving their recommender system. My summer internship was with NVIDIA – GeForce NOW team. I worked on building several models/pipelines including a model to predict lagging sessions, another NLP model for clustering customers’ feedback and a customer churn model.

What advice do you have for new students?
Data science is such a large field, and you will often feel overwhelmed. I’d suggest picking one or two areas you are interested in and try to master it. Make friends and study together help to reduce stress a lot. Also, make sure you start networking and doing interview prep early.

What classes have taken?
CIS 520 (Machine Learning), CIS 545 (Big Data), CIS 550 (Databases), STAT 711 (Forecasting), CIS 530 (Computational Linguistics)

Share a little bit about yourself.
Chess, running, weightlifting, NLP, recommendation system