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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/communicating-business-analytics-results
课程评论：没有评论
课程名称：沟通商业分析结果 课程概述：分析过程不仅仅局限于能够准确预测或为商业问题提供最佳解决方案的模型。开发这些模型并从数据中获得洞见，并不一定能够导致成功的实施，关键在于如何将结果有效地传达给决策者。在这个课程中，您将学习如何向那些不熟悉分析语言的利益相关者传达分析结果，帮助他们理解分析和数据的证据。您将能够选择适当的方式呈现定量信息，包括基于数据可视化原则的呈现方法。此外，您还将学习如何开发和传递数据分析故事，为数据提供上下文、洞见和解释。 课程大纲： 1. 课程介绍：简要回顾信息-行动价值链，并探讨分析技术在商业问题中的应用，包括经典商业问题和近期技术进步所带来的新兴问题。 2. 数据可视化最佳实践：学习多种可视化方法，理解定量信息的不同呈现工具，探讨有效和无效的数据可视化示例，以及应如何避免常见的Excel图表错误，掌握良好数据可视化的特征。 3. 数据的解释、讲述与推销：讨论如何解读数据、收集额外信息，以及基于分析结果进行建议的推销。学习避免误解和误呈数据的方法，利用实验获取更多数据，并有效地沟通结果与建议，侧重于了解听众、讲述引人入胜的故事以及创建清晰有效的沟通材料。 4. 基于数据的行动：通过两个案例研究来展示课程中涉及的概念，第一个案例展示了如何通过实验创建数据，第二个案例则提供了全面分析，展示了整个分析生命周期以及如何将定量和定性分析结合解决一个重要的战略分析问题。
Name:Introduction to the Course
Description:In this module we’ll briefly review the Information-Action Value Chain we introduced in Course 1. Then we’ll see how analytical techniques are applied in business problems, first by looking at some “classic” business problems that have been around for a long time, then by looking at some “emergent” business problems that have resulted from more recent advances in technology.
Name:Best Practices in Data Visualization
Description:In this module we’ll learn about a variety of visualizations used to illustrate and communicate data. We will start with the different vehicles used to present quantitative information. We will then look at a set of examples of data visualizations and discuss what makes them effective or ineffective. Finally, we discuss Excel charts and why most of them should be avoided. After completing this module, you will be able to better understand the characteristics of good data visualization and avoid common mistakes when creating your own graphs.
Name:Interpreting, Telling, and Selling
Description:In this module we’ll cover a number of topics around interpreting data, gathering additional data, and pitching our recommendations based on our analysis. First, we’ll discuss ways in which we misinterpret or misrepresent data and how to avoid them, such as mistaking correlation with causation, allowing cognitive biases to influence how we see data, and visualizing data in misleading ways. We’ll also learn how experimentation can help us obtain more data, including compromises we may need to make in measurement. Finally, we’ll discuss how we communicate our results and recommendations, with a focus on knowing our audience, telling compelling stories, and creating clear and effective communication materials.
Name:Acting on Data
Description:In our final module we’ll walk through two case studies and illustrate the ideas we’ve covered in the course and in the specialization as a whole. The first case shows how experimentation can be used to create data, sometimes with surprising results. The second case presents a comprehensive analysis that illustrates the entire analytic lifecycle, and shows how different methods and both quantitative and qualitative analysis can be brought together to solve one strategically important analytical problem.
The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions. Presenting findings to decision makers who are not familiar with the language of analytics presents a challenge. In this course you will learn how to communicate analytics results to stakeholders who do not understand the details of analytics but want evidence of analysis and data. You will be able to choose the right vehicles to present quantitative information, including those based on principles of data visualization. You will also learn how to develop and deliver data-analytics stories that provide context, insight, and interpretation.
