Book Image

Microsoft Power BI Data Analyst Certification Guide

By : Orrin Edenfield, Edward Corcoran
5 (1)
Book Image

Microsoft Power BI Data Analyst Certification Guide

5 (1)
By: Orrin Edenfield, Edward Corcoran

Overview of this book

Microsoft Power BI enables organizations to create a data-driven culture with business intelligence for all. This guide to achieving the Microsoft Power BI Data Analyst Associate certification will help you take control of your organization's data and pass the exam with confidence. From getting started with Power BI to connecting to data sources, including files, databases, cloud services, and SaaS providers, to using Power BI’s built-in tools to build data models and produce visualizations, this book will walk you through everything from setup to preparing for the certification exam. Throughout the chapters, you'll get detailed explanations and learn how to analyze your data, prepare it for consumption by business users, and maintain an enterprise environment in a secure and efficient way. By the end of this book, you'll be able to create and maintain robust reports and dashboards, enabling you to manage a data-driven enterprise, and be ready to take the PL-300 exam with confidence.
Table of Contents (25 chapters)
1
Part 1 – Preparing the Data
6
Part 2 – Modeling the Data
11
Part 3 – Visualizing the Data
15
Part 4 – Analyzing the Data
18
Part 5 – Deploying and Maintaining Deliverables
21
Part 6 – Practice Exams

Chapter 4: Cleansing, Transforming, and Shaping Data

For data to be used effectively in any kind of reporting, analytics, or AI use case, it must be clean and ready to be joined or shaped with other data. When data is viewed only in the context of the source system that creates it, we're often limited in how we can use it.

For example, if we have sales data coming from point-of-sale terminals, then we can draw conclusions about the total amount of sales completed on any given day, week, or month by simply summing the sales for a given time period. However, if we could join the sales data with weather data, then we could possibly draw conclusions about the impact weather has on sales. Perhaps we believe that rainy weather will have a negative impact on the sales for a given location. In order to test this hypothesis by correlating sales data with weather data, we'll need to ensure things such as that the date and time fields in the weather data can be joined with the date...