Lecturer IV and Research Investigator
How to Describe Data
What You'll Learn
- Learn the fundamentals of data interpretation, collection, and summarization
- Learn the capabilities and limitations of data and discuss criteria for determining which statistics are reliable
- Learn to interpret and evaluate the effectiveness of data visualizations
4 Modules
8 Hours
2 hrs per module (approx.)
Rating
About How to Describe Data
How to Describe Data examines the use of data in our everyday lives, giving you the ability to assess the usefulness and relevance of the information you encounter. In this course, learn about uncertainty’s role in measurements and how you can develop a critical eye toward evaluating statistical information in places like headlines, advertisements, and research. You’ll learn the fundamentals of discussing, evaluating, and presenting a wide range of data sets, as well as how data helps us make sense of the world. This is a broad overview of statistics and is designed for those with no previous experience in data analysis. With this course, you’ll be able to spot potentially misleading statistics and better interpret claims about data you encounter in the world. Course assessments focus on your understanding of concepts rather than solving math problems.
This is the first course in Understanding Data: Navigating Statistics, Science, and AI Specialization, where you’ll gain a core foundation for statistical and data literacy and gain an understanding of the data we encounter in our everyday lives.
Skills You'll Gain
- Data Analysis
- Data Literacy
- Descriptive Statistics
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to How to Describe Data, the first course in the Understanding Data specialization. This course introduces foundational concepts for thinking, reading, and speaking about data and statistics. No prior experience, coding, or math problem-solving is required. Through videos, activities, and real-world examples, you’ll build statistical intuition, develop literacy in data interpretation, and learn to spot misleading uses of statistics.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Welcome, Introduction & What Makes a Statistic Useful?
- Video: Welcome to Understanding Data: Navigating Statistics, Science, and AI
- Reading: Meet Your Instructor
- Reading: Course Syllabus
- Discussion Prompt: Meet Your Fellow Global Learners
- Reading: Help Us Learn About You
- Video: How Things Become Statistics
- Video: Learning From Data
- Video: Why We Measure Means and Medians
- Video: Reading a Histogram
- Video: Mean or Median: What Do You Mean?
- Video: Variability Is Healthy
- Video: Making Comparisons With Data
- Discussion Prompt: Talk about a statistic you saw. Was it useful?
- Graded Assignment: Module 1 Ungraded Quiz
- Reading: Module 1 Suggested Readings
- Reading: Module 1 Bibliography
Module 2: Rethinking Certainty
- Video: Introduction to Module 2: Developing Intuition About Uncertainty
- Video: Noisy Scales
- Video: Conducting a Poll
- Video: Our Noisy World
- Video: How Should We Live With Noise?
- Video: Validity (What Are We Measuring, Anyway?)
- Discussion Prompt: Talk about a statistic you saw. How was uncertainty communicated?
- Graded Assignment: Module 2 Ungraded Quiz
- Reading: Module 2 Suggested Readings
- Reading: Module 2 Bibliography
Module 3: Talking about Numbers
- Video: Introduction to Module 3: Numbers in Storytelling
- Video: Units, Scales, and Estimates
- Video: Making Sense of Big and Small Numbers
- Video: Relative and Absolute Numbers
- Video: Zombie Statistics
- Video: The Illusion of Sciencey-ness
- Video: Strengths and Weaknesses in Data Visualization
- Discussion Prompt: Talk about a statistic. What is the claim? What else do you need to know?
- Graded Assignment: Module 3 Ungraded Quiz
- Reading: Module 3 Suggested Readings
- Reading: Week 3 Bibliography
Module 4: Statistics, Skepticism, and Trust
- Video: Introduction to Module 4: Should You Trust Statistics?
- Video: But Why?
- Video: Summaries Reduce Information
- Video: Interrogating a Statistic
- Video: Style, Authority and Trust
- Video: Being a Good Steward of Your Information Ecosystem
- Video: Course 1 Recap: How to Describe Data
- Discussion Prompt: Reflect and Respond to a News Story
- Graded Assignment: Module 4 Ungraded Quiz
- Reading: Module 4 Suggested Readings
- Reading: Module 4 Bibliography
- Reading: Post-Course Survey
Grading Policy
Course materials and assignments are available for self-paced learning. Module practice quizzes are ungraded and designed to reinforce understanding. To earn a certificate, learners must achieve at least 80% on the Comprehensive Course Test, which is worth 100% of the final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No previous knowledge of data, statistics, or AI is necessary.
