|Issuing School||Samueli School Of Engineering|
|Description from the School||
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.
|Delivery Model||Fully Online|
|Completion Time||27 - 36 months|
|Application Deadline 1||Dec 1, 2021|
|Application Deadline 2||Aug 3, 2021|
|Credits Needed to Complete||36|
|College Funded Aid||No|
The minimum requirements for admission to graduate study at UCLA are:
In addition to the requirements above, the MS Engineering Online Program requires the following:
At least 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 four courses from the list below)
COM SCI 143 Database Systems COM SCI 249 Current Topics in Data Structures OR EC ENGR 205A Matrix Analysis for Scientists and Engineers COM SCI 249 Big Data Analytics (Winter) OR EC ENGR 219 Large-Scale Data Mining: Models and Algorithms COM SCI 260 Machine Learning Algorithms EC ENGR 232E: Large-Scale Social and Complex Networks: Design and Algorithms COM SCI 262A: Learning and Reasoning with Bayesian Networks
As long as you take FOUR core courses, the remaining courses may be chosen from the list of recommended electives below (or you may choose to take other core courses as electives).
Effective Fall 2018: While students are encouraged to take electives within their major, a maximum of two courses may be taken outside Data Science as long as they are offered through the MSOL Program. (This will also apply to students who were admitted prior to Fall 2018).
Recommended electives for Data Science:
EC ENGR 131A Probability and Statistics EC ENGR M214A Digital Speech Processing EC ENGR 214B Advanced Topics in Speech Processing EC ENGR 235A Mathematical Foundations of Data Storage Systems COM SCI 246 Web Information Systems
Comprehensive Exam Requirement:
Students can meet the Comprehensive Exam Requirement in two ways: Choose (1 option below)
Take and Pass ENGR 299 Capstone Project course.
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.)
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.