Data Science Engineering

Data Science Engineering Program (PDF)



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

Area Director: Prof. John Cho & Prof. Ali Sayed

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.

Degree Requirements: At least nine courses are required (36 Units), 4 core courses in Data Science Engineering + 4-5 courses in a technical domain, and meet Comprehensive Exam Requirement. Please see comprehensive requirements below:


Fundamental Courses in Data Science Engineering : Core Courses: Select 4 courses from the following list:

CS 143 Database Systems Prof. John Cho,  or CS 240A Databases and Knowledge Bases Prof. Carlo Zaniolo,

CS 249 Big Data Analytics Prof. Wei Wang,  or EE 205A Matrix Analysis for Scientist and Engineers Prof. Alan Laub,

CS 260 Machine Learning Algorithms Prof. Ameet Talwalkar or EE 210A Adaptation and Learning Prof Ali H. Sayed,

CS248 or EE235 Big Data: Modeling and Mining the Web and Social Media Prof. Vwami Roychowdury,

The Remaining Electives Can be selected from an specialization within Data Science (4-5 )

Option 1: Database and Data Management

CS240A, CS240B, CS244A, CS246, CS249, EE235A

Option 2: Inference and Learning from Data

CS262A, CS264A, CS269,CS289ML, CS M231,

EE210B, EE232B, EE238, STAT 218, STAT 201B, STAT 201C

Option 3: Applications to Vision, Speech and Bioinformatics

CS205, CS244, CS M221, CS M225, CS M266AB, CM299, EE214A, EE214B, STAT231A/CS266A, STAT232B/CS266B, STAT 238

Option 4: Optimization and Statistical Analysis

EE236A, EE236B, EE236C, EE210B, EE238, STAT 236, STAT 201B, STAT 202B, STAT 202C, STAT 204

Other electives:

CS 145 Introduction to Data Mining Prof. Wei Wang,

EE 131A Probability and Statistics Prof. Lara Dolecek,

CS 249 Big Data Analytics Prof. Wei Wang,

CS 260 Machine Learning Algorithms Prof. Ameet Talwalkar

CS 262A Learning and Reasoning with Bayesian Networks Prof. Adnan Darwiche,

CS 249 Big Data Systems Prof. Tyson Condie,

CS 246 Web Information Systems Prof. John Cho,

CS133, CS161, STAT 101C, STAT 102C, STAT 105, STAT C161

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.


As long as you have met the requirements above the remaining courses may be selected from  other Engineering departments. No approval is necessary

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.