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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/statistics-for-marketing
课程评论：没有评论
课程名称：市场营销统计学 课程概述：本课程深入探讨市场营销分析所基于的统计基础。首先，将通过对数据集的全面理解，帮助学员洞察数据的真实含义。接着，将学习抽样方法及如何提出特定数据问题。最后，课程将讲授如何通过分析来回答这些问题。许多市场营销分析师的错误源于对分析背后概念的理解不足，从而导致选择错误的测试或误解结果。本课程旨在为学员提供必要的背景知识，以便在实践中理解所做的事情及其原因。 课程目标： - 理解因变量和自变量的概念 - 确定待测试的变量 - 理解零假设、P值及其在假设测试中的作用 - 制定假设并将其与商业目标对齐 - 根据假设验证/反驳确定行动方案 - 解释描述性统计（均值、中位数、标准差、分布）及其应用场景 - 理解推断统计的基本概念 - 解释营销分析中的不同层次（描述性、预测性、指示性） - 使用数据创建基本的回归统计模型 - 使用历史数据和基本统计模型创建时间序列预测 - 理解线性回归的基本假设、应用场景和局限性 - 将线性回归模型拟合到数据集中，并使用Tableau和statsmodels解析结果 - 解释线性回归与多元回归的区别 - 进行细分（聚类）分析 - 描述观察方法与实验的不同 本课程面向希望学习市场营销中描述性和推断性统计及分析基础的学员。学员不需要市场营销或数据分析经验，只需具备基本的互联网导航技能，并积极参与。理想情况下，学员应已完成本项目中的第一门课程（市场营销分析基础）和第二门课程（数据分析导论）。 课程大纲： 1. 描述性统计：本周将概述市场营销统计学课程，学习描述性统计的基础及其使用时机，同时介绍贝叶斯统计。最后将概述毕业设计项目，并完成第一部分。 2. 推断统计：本周将介绍推断统计及如何为市场营销定义样本和总体，同时介绍变量的概念。最终完成毕业设计项目的第二部分。 3. 实验设计与假设测试：第三周将深入学习如何为商业目标制定和测试适当的假设，并完成毕业设计项目的第三部分。 4. 数据建模：本周将介绍各种模型家族，学习如何使用Tableau创建并解释这些模型的结果，最后完成毕业设计项目的第四部分。 5. 在实际环境中使用统计：最后一周将结合和应用课程中学到的所有知识，完成毕业设计项目，并听取市场分析师讲述如何在实际中运用课程所学原则。
Name:Descriptive Statistics
Description:This week you’ll get an overview of the Statistics for Marketing course and you will learn the basics of Descriptive Statistics and when to use them. You will also be introduced to Bayesian statistics. You will also get an overview of your capstone project and at the end of the week you will complete part one.
Name:Inferential Statistics
Description:This week you will be introduced to inferential statistics and how to define samples and populations for marketing. You’ll also be introduced to the concept of variables. At the end of the week you will complete part two of your capstone project.
Name:Designing Experiments and Testing Hypotheses
Description:In week three, you’ll dig into how to formulate and test appropriate hypotheses for your business goals. You’ll wrap up the week with part three of your capstone project.
Name:Data Modeling
Description:This week you’ll be introduced to various model families and how to create them using Tableau. You’ll also learn how to interpret the results of these models. You’ll complete the fourth and final part of your capstone project.
Name:Using Statistics in Real-World Settings
Description:This week you will combine and apply all the information you have learned throughout the course and finalize your capstone project. You’ll finish out the course by hearing from a marketing analyst about how they apply the principles you learned in this course in the real-world.
This course takes a deep dive into the statistical foundation upon which Marketing Analytics is built. The first part of this course is all about getting a thorough understanding of a dataset and gaining insight into what the data actually means. The second part of this course goes into sampling and how to ask specific questions about your data. Finally, the third part is about answering those questions with analyses. Many of the mistakes made by Marketing Analysts today are caused by not understanding the concepts behind the analytics they run, which causes them to run the wrong test or misinterpret the results. This course is specifically designed to give you the background you need to understand what you are doing and why you are doing it on a practical level. By the end of this course you will be able to: • Understand the concept of dependent and independent variables • Identify variables to test • Understand the Null Hypothesis, P-Values, and their role in testing hypotheses • Formulate a hypothesis and align hypotheses with business goals • Identify actions based on hypothesis validation/invalidation • Explain Descriptive Statistics (mean, median, standard deviation, distribution) and their use cases • Understand basic concepts from Inferential Statistics • Explain the different levels of analytics (descriptive, predictive, prescriptive) in the context of marketing • Create basic statistical models for regression using data • Create time-series forecasts using historical data and basic statistical models • Understand the basic assumptions, use cases, and limitations of Linear Regression • Fit a linear regression model to a dataset and interpret the output using Tableau and statsmodels • Explain the difference between linear and multivariate regression • Run a segmentation (cluster) analysis • Describe the difference between observational methods and experiments This course is designed for people who want to learn the basics of descriptive and inferential statistics and analytics in marketing. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally learners have already completed course 1 (Marketing Analytics Foundation) and course 2 (Introduction to Data Analytics) in this program.
