You can explore your interests and specialize your knowledge by choosing one of five concentrations offered by the M.S. in Data Science & Analytics program. You’ll complete your concentration through the required 15 elective credits.
DSAN 7000: Advanced Research Methodologies (Capstone Project)
Natural Language Processing (NLP)
This is an informal academic track.
Specialize in analyzing and processing human language data by studying key topics, including text mining, speech recognition, deep learning, machine translation, sentiment analysis and large language models (LLMs).
You will select one of these electives to complete the concentration:
DSAN 5600: Applied Time Series for Data Science
DSAN 6100: Optimization
DSAN 6650: Reinforcement Learning
DSAN 7000: Advanced Research Methodologies (Capstone Project)
Data Visualization and Communication
This is an informal academic track.
Master the creation and presentation of visual representations of data while growing your knowledge of data storytelling, information design and advanced visualization techniques.
You will select two of these electives to complete the concentration:
DSAN 5600: Applied Time Series for Data Science
DSAN 6300: Database Systems and SQL
DSAN 5700: Blockchain Technologies for Data Science
DSAN 5500: Data Structures, Objects, and Algorithms in Python
Financial
This is an informal academic track.
Understand the key role of data science in the finance industry, including roles such as financial analysts or risk managers in banking, business and tech. You’ll learn how to analyze complex financial data to help organizations make informed decisions to optimize performance.
You will select one of these electives to complete the concentration:
DSAN 5450: Ethics or DSAN 6700: ML App Deployment
DSAN 5900: Digital Storytelling
DSAN 6300: Database Systems and SQL
DSAN 6850: NLP with Large Language Models
Generalist
This is an informal academic track.
You aren’t required to choose a specific concentration; instead, you can “mix and match” electives to best suit your academic interests. You’ll get general exposure to the field, covering data collection, analysis, visualization, machine learning, Natural Language Processing (NLP) and big data technologies.
