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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-modeling-regression-analysis-business
课程评论：没有评论
课程名称：商业数据建模与回归分析 课程概述：本课程适合许多商业经理及已修读本专业前两门课程的学员。课程初始部分将探讨数据描述、统计推断和回归分析等工具，并将这些概念扩展到用于分类响应变量（例如竞拍中“赢”或“输”）的其他统计预测方法。在接下来的环节中，学员将学习识别数据集中重要特征的工具，这些工具可以减少复杂性、帮助识别数据的重要特征或进一步解释行为。 课程大纲： 第一部分 标题：模块0：准备与模块1：分析简介与统计推断演变 描述：本节概述了商业数据分析过程及其组成部分。我们将介绍不同的建模范式，并邀请您将问题与建模范式相匹配。模块最后，我们将概述Rattle（统计软件R的一个界面）及其在单变量分析中的应用。 第二部分 标题：模块2：数据中的关系 描述：本节重点在于使用回归模型识别因变量和自变量之间的关系。我们的目标是找到最适合数据的模型，以了解人群中变量的潜在关系。 第三部分 标题：模块3：使用留出数据进行模型开发与测试 描述：本节为学员介绍如何使用留出数据集来评估模型性能。讨论了改进模型的方法，重点关注变量选择，并探讨了建模离散预测变量和响应变量的细微差别。 第四部分 标题：模块4：维度诅咒 描述：随着传感器、数字平台及用户生成内容等技术的进步，数据生成的方式日益增加。例如，传感器持续记录数据并在后续进行分析。数据捕获过程中的冗余性问题随之而来，变量越多，问题就越复杂，可能几乎没有（或没有）增量信息。为了解决高维度带来的问题，数据转换和维度减少显得至关重要，通过检查和提取更少的维度来确保传达完整信息。
Part: 1
Title:Module 0: Get Ready & Module 1: Introduction to Analytics and Evolution of Statistical Inference
Description:This session is an overview of the business data analytics process and its components. We introduce you to different modeling paradigms and invite you to match problems to modeling paradigms. The module concludes with an overview of Rattle (an interface for the statistical package R) and its use for univariate analysis.
Part: 2
Title:Module 2: Dating with Data
Description:This session focuses on identifying relationships between dependent and independent variables using a regression model. The goal is to find the best fitted model to the data to learn about the underlying relationship of variables in the population.
Part: 3
Title:Module 3: Model Development and Testing with Holdout Data
Description:This session introduces the student to use of a holdout data set for evaluating model performance. Methods of improving the model are discussed with emphasis on variable selection. Nuances of modeling discrete predictor variables and response variables are discussed.
Part: 4
Title:Module 4: Curse of Dimensionality
Description:There has been a tremendous increase in the way data generation via sensors, digital platforms, user-generated content, etc. are being used in the industry. For example, sensors continuously record data and store it for analysis at a later point. In the way data gets captured, there can be a lot of redundancy. With more variables, comes more trouble! There may be very little (or no) incremental information gained from these sources. This is the problem of a high number of unwanted dimensions. To avoid this pitfall, data transformation and dimension reduction comes to the rescue by examining and extracting fewer dimensions while ensuring that it conveys the full information concisely.
The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior.
