Data Science Engineering Minor

Overview

The minor is intended to expose students to the entire data science life cycle from both foundational and application perspectives. The foundational courses provide the engineering skills to collect, cleanse, and store data; analyze and draw inference from data; and take action and make decisions. A wide-ranging list of interdisciplinary … For more content click the Read More button below.

The minor is intended to expose students to the entire data science life cycle from both foundational and application perspectives. The foundational courses provide the engineering skills to collect, cleanse, and store data; analyze and draw inference from data; and take action and make decisions. A wide-ranging list of interdisciplinary courses focuses on various data-science applications using these skills.

Entry to the Minor

Admission

To apply for the minor, students must have an overall grade-point average of 3.0 or better, have completed or be in the process of completing in the present quarter the two lower-division required courses with the grade B- or better, and file a petition through Message Center. Steps to apply are outlined on the Office of Academic and Student Affairs website. Information about the minor and the application are available on the minor website.

Minor Requirements

The Minor

Required Lower-Division Courses (8 units)

Required Upper-Division Courses (12 units minimum)

Complete three courses as follows:

Probability

Data Science

Data Mining or Machine Learning

Elective Upper-Division Courses (8 units minimum)

Select two courses from following list. Electrical and Computer Engineering 183DA and 183DB must both be taken to satisfy requirement. Courses used to satisfied the required upper-division courses may not be used to satisfy the elective courses.

COM SCI M119 - Fundamentals of Embedded Networked Systems COM SCI CM121 - Introduction to Bioinformatics COM SCI C122 - Algorithms in Bioinformatics COM SCI CM124 - Machine Learning Applications in Genetics COM SCI 143 - Data Management Systems COM SCI 145 - Introduction to Data Mining COM SCI M146 - Introduction to Machine Learning COM SCI 161 - Fundamentals of Artificial Intelligence COM SCI 180 - Introduction to Algorithms and Complexity COM SCI M182 - Dynamic Biosystem Modeling and Simulation Methodology EC ENGR 102 - Systems and Signals EC ENGR 113 - Digital Signal Processing EC ENGR 114 - Speech and Image Processing Systems Design EC ENGR M119 - Fundamentals of Embedded Networked Systems EC ENGR 133A - Applied Numerical Computing EC ENGR M146 - Introduction to Machine Learning EC ENGR C147 - Neural Networks and Deep Learning EC ENGR 183DA - Design of Robotic Systems I EC ENGR 183DB - Design of Robotic Systems II MECH&AE C137 - Design and Analysis of Smart Grids MECH&AE 185 - Introduction to Radio Frequency Identification and Its Application in Manufacturing and Supply Chain STATS 100B - Introduction to Mathematical Statistics STATS 115 - Probabilistic Decision Making STATS 170 - Introduction to Time-Series Analysis STATS C180 - Introduction to Bayesian Statistics

Policies

The Minor Policies

Variable topics courses may be taken as topics apply.

Transfer credit for any of the above is subject to approval; consult with the undergraduate counselors before enrolling in any courses for the minor.

A minimum of 20 units applied toward the minor requirements must be in addition to units applied toward major requirements or another minor.

Each minor course must be taken for a letter grade, and student must have a minimum grade of C in each and an overall grade-point average of 2.0 or better in the minor. Successful completion of the minor is indicated on the transcript and diploma.