| Resource | Strengths | Weaknesses | | :--- | :--- | :--- | | | Applied focus, statistical connections, numerical examples | Less depth in abstract pure math | | Linear Algebra by Jim Hefferon | Free, open-source, contains exercises | More wordy, less concise | | Linear Algebra by Gilbert Strang (MIT OCW) | Legendary intuition, video lectures | The companion book is not fully free (notes are free) | | Linear Algebra Done Wrong by Sergei Treil | Advanced, proof-heavy, unique perspective | Too abstract for beginners |
(also known as Matrix Algebra ) is a standout resource for anyone tackling the complexities of modern mathematics, data science, or econometrics.
You do not need to chase external references. The book assumes you know high school algebra, but it proves every linear algebra theorem from the ground up, including foundational topics like vector spaces and linear independence.