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所在平台: Udemy |
课程主页: https://www.udemy.com/course/business-analytics-with-r-a-comprehensive-guide/
课程评论：没有评论
课程名称：使用R进行商业分析：综合指南 课程概述： 本课程旨在教会学生如何利用R编程的力量进行商业分析。无论你是渴望成为数据科学家还是商业专业人士，本课程都将指导你从基本数据概念到复杂统计模型和机器学习技术的每一步。通过实际案例、数据处理、可视化和预测练习，帮助你建立分析商业数据的坚实基础，并用R推动决策。 课程内容： 第一部分：商业分析和R的介绍 该部分介绍商业分析的概念及其在现代商业中的演变，讨论判别分析，并引入R及其在商业分析中的应用。通过酒店数据等基本商业实例，展示分析如何应用于实际场景，了解分析中不同类型的数据及解决商业问题的决策模型。 第二部分：商业分析生命周期 深入探讨商业分析生命周期，提供分析流程结构的见解。学习模型部署的重要性及其在将模型转化为可行商业策略中的作用，同时介绍商业分析中常用的软件，指导设置R和R Studio以高效进行分析项目。 第三部分：R编程基础 R是本课程的核心工具，在此部分将全面介绍R。包括基本R函数、数据类型以及回收规则、特殊数值和逻辑联结等关键概念，学习数组、矩阵和因子的相关内容，以及数据导入和聚合的实用方法。 第四部分：数据处理与统计基础 专注于数据处理技术，例如数据合并和创建。介绍基本统计学，包括计算方差、协方差和累积频率，并通过R函数（如head()和scatterplot()）进行实际操作。此外，探讨控制流，以根据数据做出决策。 第五部分：统计学、概率与分布 覆盖商业分析所需的统计和概率核心概念，包括随机变量、离散和连续分布，以及期望值的计算。探讨二项分布和均匀随机变量，并用赌博和决策游戏（如“买或不买”）作为实例。 第六部分：使用R的商业分析 聚焦于高级商业分析，讨论统计理念，如正态分布和t分布，并学习假设检验工具。通过实际案例（如SAT分数和出生体重），理解估计、置信区间和中心极限定理，并通过R的实践经验构建置信区间。 第七部分：示例、测试与预测 强调使用R进行假设生成和检验。计算Z值，执行单侧P值检验。学习预测、时间序列分析以及ARIMA和双重指数平滑等方法，这些工具对于预测未来趋势和做出明智的商业决策至关重要。 第八部分：理解可视化 数据可视化是商业分析的强大工具，您将学习如何在R中创建有效的数据可视化。掌握可视化的原因和方法，叠加图表及气泡图等高级图形，并学习方差分析（ANOVA）和回归建模，提升构建和解释统计模型的能力。 结论： 课程结束时，你将对商业分析概念有深刻理解，并具备利用R实施的实际技能。从基本的数据处理和统计分析到高级的预测和可视化，本课程将为你准备好自信应对复杂的商业问题，并使你具备在各类商业分析角色中的成功工具。
Course IntroductionThis course is designed to teach students how to harness the power of R programming for business analytics. Whether you're an aspiring data scientist or a business professional, this course will guide you through every step-from understanding basic data concepts to implementing complex statistical models and machine learning techniques. You'll work with practical examples, data manipulation, visualization, and forecasting, giving you a solid foundation to analyze business data and drive decisions using R.Section-Wise WriteupSection 1: Introduction to Business Analytics and RThe course begins by introducing the concept of business analytics and its evolution in modern business. We start with a discussion on discriminant analysis and move into an introduction to R and its application in business analytics. This section also covers fundamental business examples, such as hotel data, to illustrate how analytics can be applied in real-world scenarios. You will learn about different types of data used in analytics, including ordinal data, and explore decision models used to solve business problems.Section 2: Business Analytics Life CycleThis section dives into the Business Analytics Life Cycle, providing insights into how analytics processes are structured. You'll learn about model deployment, which is critical for turning your models into actionable business strategies. We also explore the steps in the problem-solving process, introduce software commonly used in business analytics, and guide you through setting up R and R Studio for effective use in your analytics projects.Section 3: Understanding R ProgrammingR is the core tool used in this course, and here you'll get a comprehensive introduction to it. The section covers basic R functions, data types, and key concepts such as recycling rules, special numerical values, and logical conjunctions. You will also learn about arrays, matrices, and factors in R, along with how to work with repositories and install packages. The practical aspects of working with data, importing, and aggregating data will be demonstrated.Section 4: Data Manipulation & Statistics BasicsIn this section, you'll focus on data manipulation techniques like merging and data creation, followed by an introduction to basic statistics. You will learn how to compute variance, covariance, and cumulative frequency, while also getting hands-on experience with functions in R like head() and scatterplot(). The section also explores control flow, which helps in making decisions based on data.Section 5: Statistics, Probability & DistributionThis section covers core concepts of statistics and probability necessary for business analytics. You'll learn about random variables, discrete and continuous distributions, and how to calculate expected values. The section also explores binomial distributions and uniform random variables, alongside examples such as gambling and decision-making games like "Deal or No Deal."Section 6: Business Analytics Using RFocusing on advanced business analytics, this section delves into statistical concepts like Normal and t-distributions, along with tools for hypothesis testing. You'll work with real-world examples, such as SAT scores and birth weights, to understand estimation, confidence intervals, and central limit theorem. The section culminates in building confidence intervals and learning about kurtosis, all while gaining practical experience using R.Section 7: Examples, Testing & ForecastingThis section emphasizes hypothesis generation and testing using R. You will work with sample differences, calculate Z values, and perform one-sided P-value tests. Additionally, you will learn about forecasting, time-series analysis, and methods such as ARIMA and double exponential smoothing. These tools are essential for predicting future trends and making informed decisions in business.Section 8: Understanding VisualizationsData visualization is a powerful tool for business analytics, and in this section, you will master how to create effective visual representations of data in R. You'll learn why and how to visualize data, overlay plots, and use advanced graphs such as bubble charts. The section also covers the concept of ANOVA (Analysis of Variance) and regression modeling, providing you with the skills to build and interpret statistical models.ConclusionBy the end of this course, you will have a strong understanding of business analytics concepts and the practical skills to implement them using R. From basic data manipulation and statistical analysis to advanced forecasting and visualizations, this course will prepare you to tackle complex business problems with confidence. You'll be equipped to use R for data-driven decision-making and analysis, giving you the tools to succeed in any business analytics role.
