Data Responsibility

Overview

Before you work with data, you must confirm that it is unbiased and credible. After all, if you start your analysis with unreliable data, you won’t be able to trust your results. In this course, you will learn to identify bias in data and to ensure your data is credible. You’ll also explore open data and the importance of data ethics and data privacy. By the end of this course, you will be able to: - Explain what is involved in reviewing data to identify bias - Discuss the difference between biased and unbiased data - Identify different types of bias including confirmation, interpretation, and observer bias - Discuss characteristics of credible sources of data including reference to untidy data - Explain the concept of open data with reference to the ongoing debate in data analytics - Define data ethics and data privacy - Explain the relationship between data ethics and data privacy - Demonstrate an understanding of the benefits of anonymizing data - Demonstrate an awareness of the accessibility issues associated with open data

Syllabus

Unbiased and objective data

As a data analyst, it is important to understand why reaching conclusions on biased data can have real-world effects. In this part of the course, you'll learn what bias is, how it affects data, the types of bias, and how to spot it.