|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-regression-in-r-for-data-scientists/
课程评论：没有评论
**课程名称：** R语言线性回归（面向数据科学家） **课程概述：** 本课程是数据科学领域的核心，旨在全面讲解线性回归模型。课程不仅会深入探讨理论基础，更侧重于在R统计软件中的实际应用，通过真实世界的数据集进行演示。R作为免费的统计软件，在工业界与学术界均被广泛使用。 通过本课程，学员将学会如何构建、解读并理解线性模型背后的计算技术细节。课程旨在为学员提供在工业界工作和进行应用研究所需的计算能力。 **课程内容亮点：** * **理论与实践相结合：** 涵盖线性回归的理论，并重点介绍R语言的实现。 * **由浅入深：** 从基础的R语言和统计概念入手，逐步深入到复杂的线性模型，包括多层次/分层模型和非线性回归。 * **行业导向：** 专注于数据科学家在商业环境中面临的典型问题，并提供面试常遇到的问题分析。 * **实战演练：** 提供大量的代码示例、真实数据集、测验和视频讲解。 * **前沿技术：** 运用最新的R包和研究成果。 * **学习资源：** 所有代码和数据均提供GitHub链接。 **学习要求：** * **视频时长：** 4小时。 * **额外时间投入：** 预计学员需要花费至少5小时进行代码、数据集和案例的练习。 * **基础知识：** 建议具备一定的统计学和R语言基础，但非强制要求。 **学习目标：** 完成本课程后，学员将能够熟练运用线性建模技术，胜任工业界/商业环境下的数据科学和统计分析工作。
Linear regression is the primary workhorse in statistics and data science. Its high degree of flexibility allows it to model very different problems. We will review the theory, and we will concentrate on the R applications using real world data (R is a free statistical software used heavily in the industry and academia). We will understand how to build a real model, how to interpret it, and the computational technical details behind it. The goal is to provide the student the computational knowledge necessary to work in the industry, and do applied research, using lineal modelling techniques. Some basic knowledge in statistics and R is recommended, but not necessary. The course complexity increases as it progresses: we review basic R and statistics concepts, we then transition into the linear model explaining the computational, mathematical and R methods available. We then move into much more advanced models: dealing with multilevel hierarchical models, and we finally concentrate on nonlinear regression. We also leverage several of the latest R packages, and latest research. We focus on typical business situations you will face as a data scientist/statistical analyst, and we provide many of the typical questions you will face interviewing for a job position. The course has lots of code examples, real datasets, quizzes, and video. The video duration is 4 hours, but the user is expected to take at least 5 extra hours working on the examples, data , and code provided. After completing this course, the user is expected to be fully proficient with these techniques in an industry/business context. All code and data available at Github.
