University of California Los Angeles Online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering, California 

Why University of California Los Angeles?

The online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering offered by the University of California Los Angeles. University of California Los Angeles,  a large public research university in Los Angeles, California Applicants to the program should hold an undergraduate degree in engineering, computer science, mathematics, physics, chemistry, or the equivalent.

GRE scores are required for applicants, and a minimum undergraduate GPA of 3.00  is expected. The TOEFL is required for international applicants.

Program Snapshot

University Name	University of California Los Angeles
Location	Los Angeles
State	California
Niche.com Grade	A+
Name of Degree	Master of Science in Engineering with Certificate of Specialization in Data Science Engineering
School or Department Administering Program	Engineering
Credits	36
Cost per credit	$1,050
Tuition for Entire Program	$37,800
Test Requirements	GRE TOEFL if applicable
Minimum GPA	3.00
Prerequisite Courses and Skills	Degree in engineering, computer science, mathematics, physics, chemistry, or the equivalent
Campus Visit Required?	No
Typical Time to Complete	Two academic years and one quarter
When Can Students Start the Program?	Fall, Spring
Program Concentrations	None
Synchronous Classes	No
Required Courses	Any 4 of the following: Database Systems  Current Topics in Data Structures or A Matrix Analysis for Scientists and Engineers  Big Data Analytics or Large-Scale Data Mining: Models and Algorithms Machine Learning Algorithms Large-Scale Social and Complex Networks: Design and Algorithms Learning and Reasoning with Bayesian Networks\ and the following electives are highly recommended:  Probability and Statistics Digital Speech Processing Advanced Topics in Speech Processing Mathematical Foundations of Data Storage Systems Web Information Systems
Does the Program Include a Capstone?	Yes
Does the Program Include a Practicum or Internship?	No
Other Features that Make the Program Unique	Engineering degree Speech processing courses Coverage of smart personalization
Program Objectives (quoted directly from the 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 Description	This 36 credit program is aimed at professional engineers with prior work experience. The data science certificate is a specialization within the Masters of Engineering program.  Students will learn fundamental techniques of designing and building big data systems and learn skills in data mining, machine learning, information search, predictive analysis, smart personalization, and many other techniques.  A capstone is required.

What will I Learn?

Throughout the program, students learn the fundamental techniques used to design and build big data systems. Techniques taught include data mining and machine learning, covering functions such as information search, predictive analysis, and smart personalization. Smart personalization is the practice of collecting consumer data and then building personalized customer experiences based on past behavior and preferences. Aspiring data scientists in the UCLA program are focused on technology-based solutions to important problems in business and science.

The UCLA curriculum assumes a high degree of quantitative and professional background since it is aimed at professional engineers. Therefore data science students have a fair amount of flexibility in course choice and dive quickly into advanced subjects. Data Science specialists must take four courses from a list of core courses.  The following courses are part of the core: Machine Learning Algorithms, Large-Scale Social and Complex Networks: Design and Algorithms, and Learning and Reasoning with Bayesian Networks. Students can also choose between Current Topics in Data Structures and a course in Matrix Analysis and Engineers, and between Big Data Analytics or Large-Scale Data Mining: Models and Algorithms.

Provided that four core courses are completed, data science students can then complete the remainder of their required 36 credits with a range of electives from across the engineering curriculum. This provides flexibility for students who want to pursue a particular specialization or to work with a specific faculty member or research team. However, the following electives are highly recommended for the data science certificate: Probability and Statistics, Digital Speech Processing,  Advanced Topics in Speech Processing, Mathematical Foundations of Data Storage Systems and Web Information Systems.

How much will the program cost?

Students finish their Degree within two academic years and one quarter and pay $1,050 per credit for a total of 36 credits, or just under $38,000.

How does the program fit into my life?

All courses are asynchronous, meaning that instructors create learning materials such as lectures, activities, quizzes, assignments, and group discussions in weekly modules, and students are responsible for completing the module on their own schedule within the given week.  The program can be completed in 2 academic years and one quarter, so it is longer than some similar programs. Students can begin the program in the Fall or Spring quarter.

Summary

The University of California-Los Angeles Online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering aims to educate professional engineers with a rigorous technological toolbox for advanced careers in data science engineering. The program will only be attractive to a small subset of all aspiring data scientists because it is aimed at professional engineers.

However, those with the necessary engineering background and experience will find the UCLA program to offer a balance between structure and flexibility, including many interesting elective options. Students have the opportunity to dive deeply into technological solutions to problems in business and science, along with a cohort of classmates that have a high level of quantitative skill and useful professional experience to share.  UCLA’s expertise in speech processing is a particularly attractive feature. The UCLA program will not be appropriate for students without professional engineering background or with students who feel less prepared in statistics, math, or computer science.