About this course: 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.
Created by: University of Colorado Boulder
Taught by: Manuel Laguna, Professor
Leeds School of Business
Taught by: Dan Zhang, Professor
Leeds School of Business
Taught by: David Torgerson, Instructor
| Basic Info | Course 4 of 5 in the Advanced Business Analytics Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 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 videos
- Video: 0. Introduction to the Course
- Video: 1. Information Action Value Chain Redux
- Video: 2. Analytics in Classic Business Problems
- Video: 3. Analytics in Emergent Business Problems
- Discussion Prompt: Types of Analytical Problems
- Discussion Prompt: Is This a New Type of Analysis?
Graded: Week 1 Quiz
WEEK 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 videos
- Video: Module Introduction
- Video: Vehicles to Present Quantitative Information
- Video: Data Visualization Examples
- Video: Graphs in Excel and ASP
- Video: Graphical Excellence
- Video: Techniques to Display Multiple Variables
- Video: Excel Charts to Avoid
- Discussion Prompt: Share an Example of Effective or Ineffective Visualization
- Discussion Prompt: Annual Energy Review Graph
Graded: Week 2 Quiz
WEEK 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 videos
- Video: Correlation vs Causation
- Video: Common Cognitive Biases
- Video: Misleading With Data
- Video: Market Experiments: When the action is the question
- Video: Know thy Audience
- Video: Telling compelling stories
- Video: 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?
Graded: Week 3 Quiz
WEEK 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 videos
- Video: Case Study 1: Experiment Driven Analytics and Customer Churn
- Video: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 1
- Video: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 2
- Video: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 3
- Discussion Prompt: Communicating Control Group Results
- Discussion Prompt: The "Science and Art" of Analytics
Graded: Week 4 Quiz
Graded: Final Course Assignment
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University of Colorado Boulder
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Pricing
| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
Ratings and Reviews
The case studies used in the course point out how complicated analytics can be, but the reality check is useful to set real world expectations. Overall, this course is very valuable and
Excellent course, excellent teachers!
Great introduction , very detailed. Recommend to everyone good development practice.
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NH
Thank you! I love the instructor. I wish I could take his c