University of Colorado Boulder
Communicating Business Analytics Results
University of Colorado Boulder

Communicating Business Analytics Results

This course is part of Advanced Business Analytics Specialization

Taught in English

Some content may not be translated

Manuel Laguna
Dan Zhang
David Torgerson

Instructors: Manuel Laguna

26,012 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(508 reviews)

Intermediate level
Some related experience required
7 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify the challenges of presenting analytics findings to decision makers

  • Evaluate the strengths and weaknesses of different communication methods for conveying analytics results to non-technical audiences

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(508 reviews)

Intermediate level
Some related experience required
7 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Advanced Business Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this 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.

What's included

4 videos1 quiz2 discussion prompts

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.

What's included

7 videos1 quiz2 discussion prompts

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.

What's included

7 videos1 quiz2 discussion prompts

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.

What's included

4 videos2 quizzes2 discussion prompts

Instructors

Instructor ratings
4.4 (82 ratings)
Manuel Laguna
University of Colorado Boulder
4 Courses97,116 learners

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