The exponential growth of data generated by machines and humans present unprecedented challenges and opportunities.

Data Science Engineering


Degree: Master of Science in Engineering With Certificate of Specialization in Data Science Engineering

Area Directors:
Professor John & Professor Vwani

Program Description:
The exponential growth of data generated by machines and humans present unprecedented challenges and opportunities. From the analysis of this “big data”, businesses can learn key insights about their customers to make informed business decisions. Scientists can discover previously unknown patterns hidden deep inside the mountains of data. In this program, students will learn key techniques used to design and build big data systems and gain familiarity with data-mining and machine-learning techniques that are the foundations behind successful information search, predictive analysis, smart personalization, and many other technology-based solutions to important problems in business and science.

*Program Statistics

*Data accounts for students in the following programs: Data Science Engineering, Engineering Management, Mechanics of Structures, Sustainable Water Engineering, and Systems Engineering

Degree Requirements:

  • Nine courses are required (36 Units)
  • A minimum of five courses must be taken at the graduate level (excluding ENGR 299 Capstone Project course)
  • Students must meet the Comprehensive Exam Requirement (Please see comprehensive requirements below)

Core courses in Data Science Engineering (Select five courses from the list below)

COM SCI 143 – Data Management Systems
COM SCI 245 – Big Data Analytics OR EC ENGR 219 Large-Scale Data Mining: Models and Algorithms
COM SCI 247 – Advanced Data Mining
COM SCI 249 – Current Topics in Data Structures (topics may vary by term) i.e. Data Science Fundamentals
COM SCI 260 – Machine Learning Algorithms
COMSCI 260B – Algorithmic Machine Learning
COM SCI 260C – Deep Learning OR EC ENGR C247 – Neural Networks and Deep Learning (must choose one; may not take both courses)
COM SCI 260D – Large Scale Machine Learning (projected Fall 2023)
COM SCI 262A – Learning and Reasoning with Bayesian Networks
COM SCI 263 – Natural Language Processing
COM SCI 264A – Automated Reasoning: Theory and Applications
EC ENGR 232E – Large-Scale Social and Complex Networks: Design and Algorithms

As long as you take (5) core courses, the remaining courses may be chosen from the list of recommended electives below (or you may continue with core courses). 

While students are encouraged to take the recommended electives below, a maximum of two courses may be taken outside of Data Science as long as they are offered through the MSOL program. 

Recommended electives for Data Science:
COM SCI 215 – Internet of Things: Connectivity and Sensing
COM SCI 246 – Web Information Systems
EC ENGR 131A – Probability and Statistics
EC ENGR M214A – Digital Speech Processing
EC ENGR 235A – Mathematical Foundations of Data Storage Systems
EC ENGR 239AS – Reinforcement Learning: Theory and Applications (Special Topics in Signals and Systems)

Comprehensive Exam Requirement:

Students can meet the Comprehensive Exam Requirement in two ways: Choose (1 option below)

Option 1:
Take and Pass ENGR 299 Capstone Project course.

Option 2:
Take and pass three written exams for three different graduate level courses within the student’s area of specialization. The written exams are held concurrently with the final exam of the graduate level courses. Students may select which exams they would like to count towards the Comprehensive Exam requirement.

A maximum of (2) elective courses may be taken outside Data Science Engineering (i.e. other MSOL courses in Mechanical Engineering, Systems Engineering, Electrical Engineering, etc.)

Thesis Plan:

Students are expected to complete the degree within two academic years and one quarter, including two summer sessions. The maximum time allowed in this program is three academic years (nine quarters), excluding summer sessions.