Data Science, MS Artificial Intelligence Specialization

Advances in machine learning algorithms, growth in computer processing power, and access to large volumes of data make artificial intelligence possible. Recent advances flow from the development of deep learning methods, which are neural networks with many hidden layers. Artificial intelligence builds on machine learning, with computer programs performing many tasks formerly associated with human intelligence. Students in this specialization learn how to move from the traditional models of applied statistics to contemporary data-adaptive models employing machine learning. Students learn how to implement solutions in computer vision, natural language processing, and software robotics.


Core Courses (8 units)

Course Title
MSDS 400-DLMath For Data Scientists
MSDS 401-DLApplied Statistics with R
MSDS 402-DLData Science and Research Practice 1
or MSDS 403-DL Data Science and Digital Transformation
MSDS 420-DLDatabase Systems and Data Preparation
MSDS 422-DLPractical Machine Learning
MSDS 460-DLDecision Analytics
MSDS 475-DLProject Management
or MSDS 480-DL Business Leadership and Communications
or MSDS 485-DL Data Governance, Ethics, and Law
MSDS 498-DLCapstone Class
or MSDS 590-DL Thesis Research

Which course should students take?

  • Students without a background in data science should select MSDS 402-DL Data Science and Research Practice
  • Students with a background in data science should select MSDS 403-DL Data Science and Digital Transformation.  Students who have at least two years’ experience in the field and have or had a title, such as data scientist, data analyst, statistician, data engineer, business analyst, etc. should select this course.

Specialization Courses (4 units)

Course Title
MSDS 453-DLNatural Language Processing
MSDS 458-DLArtificial Intelligence and Deep Learning
Any two electives
Supervised Learning Methods
Unsupervised Learning Methods
Times Series Analysis and Forecasting
Python for Data Analysis
Data Engineering with Go
Foundations for Data Engineering
Analytics Application Engineering
Analytics Systems Engineering
Real-Time Interactive Processing and Analytics
Real-Time Stream Processing and Analytics
Marketing Analytics
Financial Machine Learning
Web and Network Data Science
Applied Probability and Simulation Modeling
Data Visualization
Sports Performance Analytics
Sports Management Analytics
Knowledge Engineering
Computer Vision
Intelligent Systems and Robotics
Technology Entrepreneurship
Management Consulting
Accounting and Finance for Technology Managers
Project Management
Business Process Analytics
Data Governance, Ethics, and Law
Special Topics in Data Science
Independent Study

About the Final Project

As their final course in the program, students take either a master's thesis project in an independent study format or a classroom final project class in which students integrate the knowledge they have gained in the core curriculum in a team project approved by the instructor. In both cases, students are guided by faculty in exploring the body of knowledge of data science. The master’s thesis or capstone class project count as one unit of credit.

Course Title
Choose one
Capstone Class
Thesis Research