NCLab Linear Algebra 3 Certificate: Data Applications and Advanced Techniques

0 credit hours

Credits awarded upon completion

Self-Paced

Progress at your own speed

50 hours

Estimated learning time

About the Course

Description

Linear Algebra 3 (NCLab) Data Applications and Advanced Techniques. This is an online, self-paced, learn-by-doing course that explores advanced linear algebra concepts and their powerful applications in data science, image processing, and computational modeling. Learners study the spectral theorem, QR factorization, and least-squares methods, culminating in a practical understanding of singular value decomposition (SVD). These concepts are applied to real-world problems such as data cleaning and image compression. Learners also work with large-scale linear systems using Python libraries like NumPy and SciPy, building hands-on skills in numerical computation. This course prepares Learners to apply linear algebra in research, analytics, and high-performance computing environments.

Topics

Spectral theorem, QR factorization, least-squares problems, singular value decomposition, applications to image and data cleaning, large linear systems, using Python, Numpy, and Scipy.

Prerequisites

The REQUIRED prerequisite for this Course is NCLab's Linear Algebra 2.

Sections

Schedule

Asynchronous

Delivery method

Online

$500

Deliverables

0 Credits
Academic Excellence
Earn necessary number of credit hours for completing this content
Professional Program
Launch of Career
Linear Algebra 3 Completion Certificate

Outcomes

Upon completion, you'll have the skills and knowledge in the following topics: Spectral theorem, QR factorization, least-squares problems, singular value decomposition, applications to image and data cleaning, large linear systems, using Python, Numpy, and Scipy.