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所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistics-and-data-science-in-r/
课程评论：没有评论
课程名称：通过示例学习：R中的统计学与数据科学 课程概述：本课程由一位斯坦福大学教育背景的前谷歌员工以及一位印度理工学院（IIT）和印度管理学院（IIM）培养的前Flipkart首席分析师共同教授。该团队在量化交易、分析和电子商务方面拥有数十年的实践经验。课程提供了一个温和而全面的统计学与数据科学的入门，使用真实案例来帮助理解。课程内容从基本统计概念（如均值、中位数等）开始，逐步涵盖分析和准备原始数据到可视化发现的所有方面，适合没有定量或数学背景的学习者。 课程内容：课程主要介绍数据科学和统计学，采用R编程语言进行讲解。涵盖了统计概念的理论部分及其在R中的实际应用。每个概念都通过示例、案例研究及必要时的R源代码进行解释，涵盖从互联网公司的A/B测试到量化金融中的资本资产定价模型等多个主题。 主要内容包括： 1. R中的数据分析：数据类型与数据结构（向量、数组、矩阵、列表、数据框），从文件读取数据，数据框的聚合、排序与合并。 2. 线性回归：回归分析、Excel中的简单线性回归、R中的简单与多重线性回归，分类变量的回归分析，稳健回归，回归诊断图的解析。 3. R中的数据可视化：折线图、散点图、条形图、直方图、散点图矩阵、热图，以及数据可视化的R包（如Rcolorbrewer和ggplot2）。 4. 描述统计：均值、中位数、众数、四分位间距、标准差、频率分布、直方图、箱形图。 5. 推断统计：随机变量、概率分布（均匀分布、正态分布）、抽样、抽样分布、假设检验、检验统计量、显著性检验。 此课程为希望进入数据科学和统计学领域的学习者提供了坚实的基础，结合案例分析和实践应用，使学习者能够有效掌握数据分析的核心技能。
Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. Let's parse that. Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings. Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. What's Covered: Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation: Rcolorbrewer, ggplot2 Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance
