Minimum number of credits required to complete the program: 32
Minimum cumulative GPA required: 3.0
School: School of Engineering and Computational Sciences
Learn more about the program
The M.S. in Data Science equips graduate students with the essential hard and soft skills that are needed to start and build a successful career in the field of data science. Our curriculum emphasizes the hard skills of coding, predictive modeling, and machine learning and the soft skills of problem formulation and storytelling in an applied context.
Starting with an introduction to data science, statistics, and coding in R and Python, students learn to formulate problems, collect and explore data, and visualize results. The core of the curriculum is an emphasis on creating and validating predictive models; and using techniques of machine learning and artificial intelligence to conduct supervised and unsupervised learning. Students will apply their modeling and data analytic skills to real-life applications in areas such as the health care environment, social media/marketing, and sports analytics, and will complete a capstone project that takes a data set through the full data science lifecycle.
Program Learning Goals
Formulating Problems
Overview of what is data science, what data scientists do, what types of problems data scientists solve, and the fundamental statistical techniques that are used.
Collecting and Processing Data
Students use R, Python, and other coding languages to collect, explore, clean, wrangle, and summarize large data sets.
Presenting and Integrating Results into Action
Use industry-leading software to “tell the story of the data” by creating graphical summaries with Tableau and interactive dashboards with R Shiny.
Analyzing and Modeling Data
Predictive modelling: Fit and validate multivariate predictive models focused on estimation of continuous or categorical outcomes, emphasizing statistical bases of models.
Machine learning and artificial intelligence: Automated pattern detection approaches focused on unsupervised and supervised learning, feature engineering, classification, regression, neural networks.
Dealing with Issues of Governance and Privacy
Data capture related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing. operational and experiential aspects of data governance and differential privacy using Bayesian statistics.
Applying Data Science in Context
Students will apply their modeling and data analytic skills to real-life applications in areas such as the health care environment, social media/marketing, and sports analytics.
Mastering the Data Science Lifecycle
In this capstone experience, students take a problem through the full data science lifecycle using data provided by the instructor or a data set from an employer or internship.
Admission Requirements
The program entrance requirements are as follows:
- Undergraduate degree, with a minimum of 3.0 cumulative GPA
- Resume
- Personal statement and two letters of recommendation (or interview with program director)
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