Об этом курсе: 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.
Автор: University of Colorado Boulder
Преподаватели: Manuel Laguna, Professor
Leeds School of Business
Преподаватели: Dan Zhang, Professor
Leeds School of Business
Преподаватели: David Torgerson, Instructor
| Основные сведения | Course 4 of 5 in the Advanced Business Analytics Specialization |
| Язык | English, Субтитры: Korean |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
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Программа курса
НЕДЕЛЯ 1
Introduction to the Course
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.
4 видео
- Видео: 1. Information Action Value Chain Redux
- Видео: 2. Analytics in Classic Business Problems
- Видео: 3. Analytics in Emergent Business Problems
- Discussion Prompt: Types of Analytical Problems
- Discussion Prompt: Is This a New Type of Analysis?
Оцениваемый: Week 1 Quiz
НЕДЕЛЯ 2
Best Practices in Data Visualization
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.
7 видео
- Видео: Vehicles to Present Quantitative Information
- Видео: Data Visualization Examples
- Видео: Graphs in Excel and ASP
- Видео: Graphical Excellence
- Видео: Techniques to Display Multiple Variables
- Видео: Excel Charts to Avoid
- Discussion Prompt: Share an Example of Effective or Ineffective Visualization
- Discussion Prompt: Annual Energy Review Graph
Оцениваемый: Week 2 Quiz
НЕДЕЛЯ 3
Interpreting, Telling, and Selling
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.
7 видео
- Видео: Common Cognitive Biases
- Видео: Misleading With Data
- Видео: Market Experiments: When the action is the question
- Видео: Know thy Audience
- Видео: Telling compelling stories
- Видео: Making It Real
- Discussion Prompt: What Examples of Correlation vs. Causation Have You Recently Encountered?
- Discussion Prompt: CNN Article about Four-Day Work Week. Is This a Good Data-Based Argument?
Оцениваемый: Week 3 Quiz
НЕДЕЛЯ 4
Acting on Data
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.
4 видео
- Видео: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 1
- Видео: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 2
- Видео: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 3
- Discussion Prompt: Communicating Control Group Results
- Discussion Prompt: The "Science and Art" of Analytics
Оцениваемый: Week 4 Quiz
Оцениваемый: Final Course Assignment
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Авторы
University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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Рейтинги и отзывы
I really enjoyed the materials presented in this course. I wish recommendations on reference books were provided in any of these courses.
give me an insight to how to present data into readable chart to the audience
Thank you! I love the instructor. I wish I could take his c
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Very good course, Case based learning and examples explaing use of analaytics clarifed the course a lot.