How Science Turns Data Into Knowledge teaches you how to evaluate scientific claims critically, address experimental limitations, and recognize the roles of trust and responsibility within research. During the course, you’ll explore the nuances of significance testing, scientific research methods, and science communication, emphasizing the importance of carefully interpreting statistical results.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
After learning about the scientific process, you’ll learn how science can make its way into the news cycle—and how critical context can be lost amidst the noise. By the end of the course, you’ll be able to think more critically about the media you consume and how you can view science news and information with a more nuanced perspective.
This is the second course in Understanding Data: Navigating Statistics, Science, and AI Specialization, in which you’ll gain a core foundation for statistical and data literacy and gain an understanding of the data we encounter in our everyday lives.
This course is part of the Understanding Data: Navigating Statistics, Science, and AI Specialization.
What you'll learn
- Learn the rationale and limitations of significance testing within scientific inquiry, including crafting hypotheses and interpreting p-values
- Learn how scientific experiments are proposed, designed, reviewed, and published
- Identify common biases and mistranslations in science research reporting and the challenges of conveying science research to the public effectively
- Assess the credibility of claims about research and recognize the role of replicability and generalizability in scientific progress
Syllabus
Welcome, Introduction & Significance
Module 1: Experimental Design
Module 2: How Science Becomes News
Module 3: Science and Society
