Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.
Jupyter Notebook
2,580
200 commits
updated Sep 5, 2024
Updated on Aug 2024

These lecture notes are intended for introductory linear algebra courses, suitable for university students, programmers, data analysts, algorithmic traders and etc.
The lectures notes are loosely based on several textbooks:

However, the crux of the course is not about proving theorems, but to demonstrate the practices and visualization of the concepts. Thus we will not engage in precise deduction or notation, rather we aim to clarify the elusive concepts and thanks to Python/MATLAB, the task is much easier now.
Though the lectures are for beginners, it is beneficial that attendants had certain amount of exposure to linear algebra and calculus.
And also the attendee are expected to have basic knowledge (3 days training would be enough) of
All the codes are written in an intuitive manner rather than efficient or professional coding style, therefore the codes are exceedingly straightforward, I presume barely anyone would have difficulty in understanding the codes.
I use poetry to management environment, if you happen to use VS code like me, please follow the steps below:
(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -pcd $env:APPDATA\Python\Scripts, check if poetry being installed.notepad $profile and set alias for poetry Set-Alias poetry "C:\Users\user\AppData\Roaming\Python\Scripts\poetry.exe" in notepad, I prefered this way, because sometimes setting env path not working in windows.. $profile.Set-ExecutionPolicy RemoteSigned -Scope CurrentUser to unstrict your execution policy and choose Y.Set-ExecutionPolicy Restricted -Scope CurrentUser.poetry --version, if you see the version printed, good to go.poetry update, or just manage version at your own convenience.These notes will equip you with most needed and basic knowledge for other subjects, such as Data Science, Econometrics, Mathematical Statistics, Financial Engineering, Control Theory and etc., which heavily rely on linear algebra. Please go through the tutorial patiently, you will certainly have a better grasp of the fundamental concepts of linear algebera. Then further step is to study the special matrices and their application with your domain knowledge.
Please access my webiste for better reading experience: Linear Algebra for Python
Chapter 1 - Linear Equation System
Chapter 2 - Basic Matrix Algebra
Chapter 3 - Determinant
Chapter 4 - LU Factorization
Chapter 5 - Vector Addition, Subtraction and Scalar Multiplication
Chapter 6 - Linear Combination
Chapter 7 - Linear Independence
Chapter 8 - Vector Space and Subspace
Chapter 9 - Basis and Dimension
Chapter 10 -Null Space vs Col Space, Row Space and Rank
Chapter 11 - Linear Transformation
Chapter 12 - Eigenvalues and Eigenvectors
Chapter 13b - Principal Component Analysis
Chapter 13a - Diagonalization
Chapter 14 - Applications to Dynamic System
Chapter 15 - Innear Product and Orthogonality
Chapter 16 - Gram-Schmidt Process and QR Decomposition
Chapter 17 - Symmetric Matrices , Quadratic Form and Cholesky Decomposition
Chapter 18 - The Singular Value Decomposition
Chapter 19 - Multivariate Normal Distribution

22 followers · starred Feb 2023
64 followers · starred Jul 2024
Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.
Jupyter Notebook
2,580
200 commits
updated Sep 5, 2024
Updated on Aug 2024

These lecture notes are intended for introductory linear algebra courses, suitable for university students, programmers, data analysts, algorithmic traders and etc.
The lectures notes are loosely based on several textbooks:

However, the crux of the course is not about proving theorems, but to demonstrate the practices and visualization of the concepts. Thus we will not engage in precise deduction or notation, rather we aim to clarify the elusive concepts and thanks to Python/MATLAB, the task is much easier now.
Though the lectures are for beginners, it is beneficial that attendants had certain amount of exposure to linear algebra and calculus.
And also the attendee are expected to have basic knowledge (3 days training would be enough) of
All the codes are written in an intuitive manner rather than efficient or professional coding style, therefore the codes are exceedingly straightforward, I presume barely anyone would have difficulty in understanding the codes.
I use poetry to management environment, if you happen to use VS code like me, please follow the steps below:
(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -pcd $env:APPDATA\Python\Scripts, check if poetry being installed.notepad $profile and set alias for poetry Set-Alias poetry "C:\Users\user\AppData\Roaming\Python\Scripts\poetry.exe" in notepad, I prefered this way, because sometimes setting env path not working in windows.. $profile.Set-ExecutionPolicy RemoteSigned -Scope CurrentUser to unstrict your execution policy and choose Y.Set-ExecutionPolicy Restricted -Scope CurrentUser.poetry --version, if you see the version printed, good to go.poetry update, or just manage version at your own convenience.These notes will equip you with most needed and basic knowledge for other subjects, such as Data Science, Econometrics, Mathematical Statistics, Financial Engineering, Control Theory and etc., which heavily rely on linear algebra. Please go through the tutorial patiently, you will certainly have a better grasp of the fundamental concepts of linear algebera. Then further step is to study the special matrices and their application with your domain knowledge.
Please access my webiste for better reading experience: Linear Algebra for Python
Chapter 1 - Linear Equation System
Chapter 2 - Basic Matrix Algebra
Chapter 3 - Determinant
Chapter 4 - LU Factorization
Chapter 5 - Vector Addition, Subtraction and Scalar Multiplication
Chapter 6 - Linear Combination
Chapter 7 - Linear Independence
Chapter 8 - Vector Space and Subspace
Chapter 9 - Basis and Dimension
Chapter 10 -Null Space vs Col Space, Row Space and Rank
Chapter 11 - Linear Transformation
Chapter 12 - Eigenvalues and Eigenvectors
Chapter 13b - Principal Component Analysis
Chapter 13a - Diagonalization
Chapter 14 - Applications to Dynamic System
Chapter 15 - Innear Product and Orthogonality
Chapter 16 - Gram-Schmidt Process and QR Decomposition
Chapter 17 - Symmetric Matrices , Quadratic Form and Cholesky Decomposition
Chapter 18 - The Singular Value Decomposition
Chapter 19 - Multivariate Normal Distribution

22 followers · starred Feb 2023
64 followers · starred Jul 2024