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所在平台: Udemy |
课程主页: https://www.udemy.com/course/predictive-analytics-modeling-r-minitab-spss-sas/
课程评论：没有评论
课程名称：预测分析与建模：R、Minitab、SPSS和SAS 课程概述： 欢迎参加全面的课程“预测分析与建模：使用R、Minitab、SPSS和SAS”。本课程精心设计，旨在为您提供在数据分析和预测建模方面所需的知识和技能，无论您是初学者还是经验丰富的专业人士，课程将提供深入的见解和实践经验，帮助您掌握预测分析。 第一部分：R Studio界面及R脚本基础 本部分介绍R编程环境及R Studio的基本使用。您将学习如何下载、安装和导航R Studio，以及了解R中的基本数据类型、向量、矩阵、列表和数据框。该部分还涵盖决策制定、条件语句、循环、函数和ggplot2的强大数据可视化功能。完成此部分后，您将拥有扎实的R编程基础，能够执行基本的数据处理和可视化任务。 第二部分：R项目 - 卡购买预测 在本部分中，您将参与一个实际项目，以使用R预测卡购买。项目包括导入数据集、计算信息值（IV）、绘制变量和数据分割。课程将引导您建立和优化逻辑回归模型、创建提升图，并评估模型在训练集和测试集上的性能。此外，您还将学习如何在R中保存模型、实施决策树模型，包括进行预测和评估其性能。这个实践项目旨在为您提供真实世界的预测建模经验。 第三部分：数据科学的R编程 - 完整课程 通过这一全面的部分，深入了解R编程，涵盖从R的历史到高级数据科学技术。您将探讨数据类型、基本操作、数据读取、调试、控制结构和函数等内容。此外，这一部分还包括作用域规则、循环、模拟和广泛的绘图技术，并学习日期和时间处理、正则表达式、类、方法等。本部分旨在将您培养为一个能够应对复杂数据科学挑战的熟练R程序员。 第四部分：使用Minitab进行统计分析 - 从初学者到高级 本部分集中于使用Minitab进行统计分析，指导您从初学者水平提升到高级水平。初步内容包括Minitab及数据类型介绍，随后是离散程度的衡量、描述性统计、数据排序及各种图形表示（如直方图、饼图和散点图）。该部分还涵盖概率分布、假设检验、抽样、测量系统分析、过程能力分析等。完成此部分后，您将能够使用Minitab进行全面的统计分析。 第五部分：使用Minitab进行预测分析与建模 在这一部分中，依托统计知识，深入探讨Minitab中的预测建模。您将探索非线性回归、ANOVA和控制图，并理解和解释结果。该部分包括关于描述性统计、相关技术、回归建模及多元回归的实际例子和练习。您还将学习逻辑回归、生成预测值及解释复杂数据集。本部分旨在增强您的预测建模技能，使您能够从数据中获取可行的见解。 第六部分：SPSS图形用户界面及应用 本部分将教授您SPSS的图形用户界面及其应用。您将学习SPSS的基本用法、数据集导入及理解均值和标准差。该部分还探讨各种软件菜单、用户操作概念及统计技术的实际应用。完成此部分后，您将熟练使用SPSS进行数据分析和解释。 第七部分：使用SAS进行预测分析与建模 课程的最后一部分介绍SAS企业矿工在预测分析和建模中的应用。您将学习如何选择SAS表、创建输入数据节点及利用元数据顾问选项。该部分涵盖变量选择、数据分割、变量变换及包括神经网络和回归模型在内的各种建模技术。您还将探索SAS编码并创建集成图。这一部分提供有关使用SAS进行复杂预测分析任务的全面理解。 结论： “预测分析与建模：使用R、Minitab、SPSS和SAS”是一个旨在为您提供在数据分析领域取得成功所需技能和知识的综合课程。从R编程基础到Minitab、SPSS和SAS中的高级统计分析，课程涵盖了所有必要的工具和技术。完成课程后，您将能够自信地处理现实世界的数据挑战并做出基于数据的决策。立即报名，迈出掌握预测分析的第一步！
IntroductionWelcome to the comprehensive course "Predictive Analytics & Modeling with R, Minitab, SPSS, and SAS". This course is meticulously designed to equip you with the knowledge and skills needed to excel in data analysis and predictive modeling using some of the most powerful tools in the industry. Whether you are a beginner or an experienced professional, this course offers in-depth insights and hands-on experience to help you master predictive analytics.Section 1: R Studio UI and R Script BasicsThis section introduces you to the R programming environment and the basics of using R Studio. You will learn how to download, install, and navigate R Studio, along with understanding basic data types, vectors, matrices, lists, and data frames in R. The section also covers decision making, conditional statements, loops, functions, and the power of ggplot2 for data visualization. By the end of this section, you will have a solid foundation in R programming and the ability to perform essential data manipulation and visualization tasks.Section 2: Project on R - Card Purchase PredictionIn this section, you will embark on a practical project to predict card purchases using R. The journey begins with an introduction to the project and importing the dataset. You will then delve into calculating Information Value (IV), plotting variables, and data splitting. The course guides you through building and optimizing a logistic regression model, creating a lift chart, and evaluating model performance on both training and test sets. Additionally, you will learn to save models in R and implement decision tree models, including making predictions and assessing their performance. This hands-on project is designed to provide you with real-world experience in predictive modeling with R.Section 3: R Programming for Data Science - A Complete Course to LearnDive deeper into R programming with this comprehensive section that covers everything from the history of R to advanced data science techniques. You will explore data types, basic operations, data reading, debugging, control structures, and functions. The section also includes scoping rules, looping, simulation, and extensive plotting techniques. You will learn about date and time handling, regular expressions, classes, methods, and more. This section is designed to transform you into a proficient R programmer capable of tackling complex data science challenges.Section 4: Statistical Analysis using Minitab - Beginners to BeyondThis section focuses on statistical analysis using Minitab, guiding you from beginner to advanced levels. You will start with an introduction to Minitab and types of data, followed by measures of dispersion, descriptive statistics, data sorting, and various graphical representations like histograms, pie charts, and scatter plots. The section also covers probability distributions, hypothesis testing, sampling, measurement system analysis, process capability analysis, and more. By the end of this section, you will be adept at performing comprehensive statistical analyses using Minitab.Section 5: Predictive Analytics & Modeling using MinitabBuilding on your statistical knowledge, this section delves into predictive modeling with Minitab. You will explore non-linear regression, ANOVA, and control charts, along with understanding and interpreting results. The section includes practical examples and exercises on descriptive statistics, correlation techniques, regression modeling, and multiple regression. You will also learn about logistic regression, generating predicted values, and interpreting complex datasets. This section aims to enhance your predictive modeling skills and enable you to derive actionable insights from data.Section 6: SPSS GUI and ApplicationsIn this section, you will learn about the graphical user interface of SPSS and its applications. You will cover the basics of using SPSS, importing datasets, and understanding mean and standard deviation. The section also explores various software menus, user operating concepts, and practical implementation of statistical techniques. By the end of this section, you will be proficient in using SPSS for data analysis and interpretation.Section 7: Predictive Analytics & Modeling with SASThe final section of the course introduces you to SAS Enterprise Miner for predictive analytics and modeling. You will learn how to select SAS tables, create input data nodes, and utilize metadata advisor options. The section covers variable selection, data partitioning, transformation of variables, and various modeling techniques, including neural networks and regression models. You will also explore SAS coding and create ensemble diagrams. This section provides a thorough understanding of using SAS for complex predictive analytics tasks.Conclusion"Predictive Analytics & Modeling with R, Minitab, SPSS, and SAS" is a comprehensive course designed to provide you with the skills and knowledge needed to excel in the field of data analytics. From foundational programming in R to advanced statistical analysis in Minitab, SPSS, and SAS, this course covers all the essential tools and techniques. By the end of the course, you will be equipped to handle real-world data challenges and make data-driven decisions with confidence. Enroll now and take the first step towards mastering predictive analytics!
