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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-statistics-with-r-w/
课程评论：没有评论
Coursera 上的“Python 统计学入门”课程旨在为学生提供统计学的基本原理和技术，并教授如何使用 R 编程语言进行统计计算和数据分析。 课程内容涵盖三个主要部分： 1. **描述性统计学**：本部分将介绍数据类型、概率概念、常用的统计指标（如均值、中位数、方差等）、偏度和峰度，以及如何绘制数据图表来可视化数据。 2. **推断性统计学**：基于描述性统计学的知识，本部分将深入探讨中心极限定理、假设检验、置信区间、Z 统计量和 T 统计量、单样本 T 检验和双样本 T 检验等核心推断性统计概念。 3. **回归分析**：最后一部分将讲解相关性、简单线性回归和多元回归，帮助学生理解变量间的关系并进行预测。 课程的特色在于，大部分统计概念都会结合 R 编程语言的代码示例进行讲解。R 是一种广泛应用于统计计算和图形制作的免费开源软件环境，是统计学家和数据挖掘师的常用工具。 总而言之，这门课程将帮助学生建立扎实的统计学基础，同时熟练掌握 R 语言在统计分析中的应用。它适合所有希望理解统计分析及其应用，或有志于从事数据科学、机器学习领域的学习者。通过本课程，学生将提升统计分析、数据解读和沟通表达的能力，为后续更高级的统计学习和实践打下坚实基础。
Introduction to Statistics with R is an introductory-level course that provides an overview of the fundamental principles and techniques of statistics using the R programming language. This course aims to provide a comprehensive overview of statistical concepts and methods as well as skills in using statistical software. The course is segmented into three sections, Descriptive Statistics, Inferential Statistics, and Regression. In the first section, we will cover topics such as Data Types, Probability Concepts, Common Statistical Measures, Skewness & Kurtosis, and Plotting Data. In the next section on Inferential statistics, we will use our knowledge from the previous section to gain an in-depth understanding of fundamental Inferential Statistics concepts such as the Central Limit Theorem, Hypothesis Testing, Confidence Intervals, Z-statistic & T-statistic, One-sample T-tests, and Two-sample T-tests. We will then finish up the course by talking about Correlation, Simple Linear Regression, and Multiple Regression.Most of the concepts in this course are followed up with a coding example in R. R is a programming language and a free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is widely used among statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible.In conclusion, the Introduction to Statistics course is designed to give students a foundational understanding of statistics, as well as to help students become proficient in using the R programming language. This course is suitable for anyone who wants to understand statistical analysis and its applications or is looking to pursue a career in data science or machine learning. With this course, students will develop skills in statistical analysis, interpretation, and communication, making them well-prepared for further statistical education and analysis
