Description:
Applied Linear Algebra for Data Science (with Python) is a comprehensive guide designed to provide a practical understanding of linear algebra concepts and their applications in the field of data science. Through this course, students will learn how to leverage the power of linear algebra to analyze and manipulate data efficiently using Python programming language and relevant libraries such as NumPy and Matplotlib.
Topics Covered:
Introduction to Linear Algebra: Basic concepts, vectors, matrices, and operations.
Data Representation: Representing data as vectors and matrices, orientation, and indexing.
Vector Operations: Addition, subtraction, scalar multiplication, and dot product.
Matrix Operations: Transposition, matrix-vector multiplication, and matrix-matrix multiplication.
Decompositions: Eigenvalue decomposition, singular value decomposition (SVD), and QR decomposition.
Vector Spaces: Subspaces, span, basis, and linear independence.
Applications in Data Science: Regression, classification, dimensionality reduction, and clustering.
Visualization: Plotting vectors and matrices, geometric interpretations, and data visualization techniques.
Course Objectives:
Understand fundamental concepts of linear algebra and their relevance to data science.
Gain proficiency in performing vector and matrix operations using Python.
Learn various decomposition techniques and their applications in data analysis.
Apply linear algebra concepts to solve real-world data science problems.
Develop skills in visualizing and interpreting data using geometric representations.
Prerequisites:
Basic knowledge of Python programming language.
Familiarity with fundamental mathematical concepts such as algebra and calculus.
Target Audience:
Students pursuing degrees or careers in data science, machine learning, or related fields.
Professionals seeking to enhance their skills in data analysis and visualization using linear algebra techniques.
By the end of this course, students will have a solid understanding of linear algebra principles and their practical applications in data science, enabling them to effectively analyze and interpret data to make informed decisions.
"Applied Linear Algebra for Data Science (with Python) is a comprehensive course aimed at providing students with a practical grasp of linear algebra principles and their utilization within the realm of data science. Covering a wide array of topics, including basic concepts like vectors, matrices, and operations, to more advanced techniques such as eigenvalue decomposition and singular value decomposition (SVD), this course equips learners with the necessary skills to efficiently analyze and manipulate data using the Python programming language and essential libraries like NumPy and Matplotlib. By delving into subjects like data representation, vector and matrix operations, and various decomposition methods, students will gain proficiency in applying linear algebra concepts to real-world data science tasks such as regression, classification, and clustering. With prerequisites including a basic understanding of Python and fundamental mathematical concepts, this course caters to students pursuing degrees or careers in data science, as well as professionals aiming to enhance their analytical and visualization skills. Upon completion, participants will emerge with a solid foundation in linear algebra, empowering them to interpret data effectively and make informed decisions in the data-driven world."
