The Importance of Integrity

Overview

Data integrity is critical to successful analysis. In this course, you’ll explore methods and steps that analysts take to check their data for integrity. This includes knowing what to do when you don’t have enough data. You’ll also learn about sample size and understand how to avoid sampling bias. All of these methods will help you ensure your analysis is successful. By the end of this course, learners will: - Define data integrity with reference to types and risks. - Check for data integrity. - Identify common pitfalls when cleaning data. - Describe the benefits of documenting the data cleaning process. - Describe strategies that can be used to address insufficient data. - Verify the results of cleaning data.

Syllabus

Data integrity and analytics objectives

Data integrity is critical to successful analysis. In this module, you’ll explore methods and steps that analysts take to check their data for integrity.

Overcome the challenges of insufficient data

In this module, you will learn about the importance of sample size and how to deal with insufficient data. You’ll also learn about pre-cleaning activities that must be performed before running data analysis.