Course Schedule and Catalog
Course Schedule - Spring Semester 2025
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
RCEL 506 001 (CRN: 25911)
STATS & DATA FOR ENGINEERS
Long Title: APPLIED STATISTICS AND DATA SCIENCE FOR ENGINEERING LEADERS
Department: Center Engineering Leadership
Instructor: Avalos Gauna, Edgar
Meeting: 3:00PM - 6:15PM W (13-JAN-2025 - 25-APR-2025)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Must be enrolled in one of the following Degree(s):
Master of Bioengineering
Master of Comp & Appl Math
Master of Civil & Env Eng
Master of Chemical Eng
Master of Computer Science
Master of Comp Sci & Eng
Master of Data Science
Master of Electrical Comp Eng
Master of Eng Mgmt & Leadershp
Master of Industrial Eng
Master of Mechanical Eng
Master Materials Sci & NanoEng
Master of Statistics
Graduate Certificate
Must be enrolled in one of the following Fields of Study:
Major: Computational & Applied Math
Major: Chemical Engineering
Major: Computer Science
Major: Computational Science & Eng
Major: Data Science
Major: Electrical & Computer Eng.
Major: Engineering Mgmt & Leadership
Certificate: Engineering Project Management
Major: Industrial Engineering
Major: Mechanical Engineering
Major: Materials Science & NanoEng
Certificate: Product Mgmt for Eng Leaders
Major: Statistics
Major: Bioengineering
Section Max Enrollment: 30
Section Enrolled: 0
Enrollment data as of: 15-NOV-2024 1:34AM
Final Exam: No Final Exam
Description: Data Science has taken the world by storm. Examples of great success can be seen everywhere. This has opened the way for myriad industries and sectors to try to develop their own best practices. Especially since the COVID pandemic, data science, machine learning, artificial intelligence, and other computational technologies have grown rapidly during the past couple of years. However, not everyone knows which, how, when, or why to implement these techniques. Many engineers, for example, oversee or work with daily streams of data and want to use them in the most efficient way possible. The main objective of this course is to provide a comprehensive introduction to statistical and computational methods for modern data problems faced by engineers and engineering managers. By using statistical frameworks and data science as main drivers, the student will survey and compare algorithms for various data applications, learning how to select the most suitable according to real life scenarios.
In order to enroll in an online section of this course, you are expected to have a working camera and microphone. During class sessions, you must be able to participate using your microphone and you are expected to have your camera on for the duration of the class so that you are visible to the instructor and other students in the class, just as you would be in an in person class.