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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/essential-linear-algebra-for-data-science
课程评论：没有评论
课程名称：数据科学的基本线性代数 课程概述：如果你对数据科学感兴趣，但缺乏数学基础；或者数学一直是一门你避免的困难学科，那么这门课程将为你提供所需的最基本的线性代数知识，助力你的数据科学职业生涯。课程将以更简单易懂的方法和友好的概念，带你真正理解线性代数中最重要的观点，而不包含大量不必要的证明和你可能用不上的概念。可以将这视为通往数据科学的快捷通道。 本课程旨在为学员成功完成“数据科学应用的统计建模”课程做好准备，该课程是科罗拉多大学博尔德分校数据科学硕士（MS-DS）项目的一部分。 课程大纲： 1. **线性系统与高斯消元** - 在这一模块中，我们将学习矩阵的概念及其表示内容，探索如何通过矩阵将线性方程组整齐地表达，并深入了解坐标系统，以可视化的方式帮助你更全面地理解矩阵。 2. **矩阵代数** - 在这一模块中，我们将学习如何通过矩阵代数解决线性方程系统。 3. **线性系统的属性** - 本模块探讨线性系统的概念和属性，包括独立性、基、秩、行空间、列空间等内容。 4. **行列式与特征值** - 在这一模块中，我们将讨论投影的概念并学习其工作原理，建立在2维投影的基础上，逐渐探索更高维的投影。 5. **投影与最小二乘法** - 本模块将指导你计算矩阵的行列式，随后将涉及特征值和特征向量的内容。 通过这门课程，学员将能够建立起扎实的线性代数基础，为进一步深入数据科学学习奠定良好基础。
Name:Linear Systems and Gaussian Elimination
Description:In this module we will learn what a matrix is and what it represents. We will explore how a system of linear equations can be expressed in a neat package via matrices. Lastly, we will delve into coordinate systems and provide visualizations to help you understand matrices in a more well-rounded way.
Name:Matrix Algebra
Description:In this module we will learn how to solve a linear system of equations with matrix algebra.
Name:Properties of a Linear System
Description:In this module we will explore concepts and properties of linear systems. This includes independence, basis, rank, row space, column space, and much more.
Name:Determinant and Eigens
Description:In this module we will discuss projections and how they work. We will build on a foundation using 2-dimensional projections and explore the concept in higher dimensions over time.
Name:Projections and Least Squares
Description:In this module we will learn how to compute the determinant of a matrix. Afterwards, Eigenvalues and Eigenvectors will be covered.
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra. This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of Dan-Cristian Pădureț on Unsplash.com
