LINEAR ALGEBRA

Course Description

The aim of this course is to introduce the basic operations in linear algebra and applications in engineering problems; matrices, matrix properties and matrix operations: Addition, scalar multiplication, multiplication, transpose, solution of system of linear equations: Elimination method, Gauss Jordan forms, inverse method to solve linear systems, row reduced echelon forms, Gaussian elimination method, inverse and determinants: solving linear equations with determinant (Cramer's rule), use one row to evaluate determinant, minor, cofactor, adjoint matrix, identity matrix, square matrix of the matrices. Real vector spaces, vectors and their properties and applications in engineering: Addition, subtractions, dot product, scalar multiplication, cross product, basis, dimensions and subspaces.

LINEAR ALGEBRA

Evaluation Tools (Active Term)

Course outcomes

1. 01 Solve linear equations
2. 02 Apply Matrix Operations
3. 03 Find determinants
4. 04 Find inverse matrix
5. 05 Find Cofactor Matrix
6. 06 Apply Cramer Rule

Course Syllabus

Reference Books & Course Materials

1. 01 Seymour Lipschutz; 2011, “Schaum's Outline of Linear Algebra; 5 th Edition, ISBN 0-07-136200-2.
2. 02 Gilbert Strang; Linear Algebra and Applications, 4 th Edition, ISBN-10: 0030105676, ISBN-13: 9780030105678.

Learning Outcomes

1. L01 By the completion of the course the students should be able to do the following:
2. L02 Solve linear equations
3. L03 Matrix operations
4. L04 Calculate determinants
5. L05 Find inverse matrix
6. L06 Understand vector space and do simple operations with vectors

Program Outcomes

1. P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix