Essential Linear Algebra for Data Science
Master fundamental linear algebra concepts for data science, from matrices and vectors to eigenvalues and projections, with practical applications.
Master fundamental linear algebra concepts for data science, from matrices and vectors to eigenvalues and projections, with practical applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Expressway to Data Science: Essential Math Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
(151 ratings)
8,750 already enrolled
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What you'll learn
Solve real-world problems using matrices and understand their applications
Recognize matrix representations in n-dimensional space
Identify key properties of equation systems including independence and rank
Understand and apply projections in multiple dimensions
Master eigenvalues and eigenvectors for data analysis
Skills you'll gain
This course includes:
4.8 Hours PreRecorded video
13 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course introduces essential linear algebra concepts for data science applications. Beginning with fundamental matrix operations and Gaussian elimination, students progress through vector spaces, linear transformations, eigenvalues, and least squares methods. The curriculum emphasizes practical understanding over theoretical proofs, focusing on real-world applications in data science. Through video lectures and interactive quizzes, learners develop a strong foundation in linear algebra principles.
Linear Systems and Gaussian Elimination
Module 1 · 1 Hours to complete
Matrix Algebra
Module 2 · 1 Hours to complete
Properties of a Linear System
Module 3 · 2 Hours to complete
Determinant and Eigens
Module 4 · 1 Hours to complete
Projections and Least Squares
Module 5 · 1 Hours to complete
Fee Structure
Instructor
Instructor
Dr. James Bird is an Instructor at the University of Colorado Boulder, specializing in data science and applied mathematics. He holds a Ph.D. in Computer Science from the University of California, Santa Barbara, along with an M.S. in Statistics and an M.A. in Applied Physics/Applied Mathematics. His extensive academic background is complemented by a B.A. in Mathematics, providing him with a solid foundation in quantitative analysis.At CU Boulder, Dr. Bird teaches several courses that are integral to the data science curriculum, including "Essential Linear Algebra for Data Science," "Integral Calculus and Numerical Analysis for Data Science," and "Regression and Classification." His research interests focus on the implementation of artificial intelligence for deep space travel, reflecting his commitment to advancing knowledge in both theoretical and practical aspects of data science. Additionally, he has worked as a Statistician with Gallup since 2015, further enhancing his expertise in statistical methods and data analysis. Through his teaching and research, Dr. Bird plays a vital role in preparing students for successful careers in data science and analytics.
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4.4 course rating
151 ratings
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