Welcome to the first book to explore the powerful tools within Power BI that can enhance and improve your analytical data exploration.
You know Power BIs reputation as a reporting, dashboarding, and data visualization tool but it might not occur to you that it has great value as a tool for data exploration. This book examines Power BIs data analysis features and shows you, through real-world examples, how Power BI can be a go-to analysis tool for business users in all domains. You will discover that Microsofts Power BI offers all the number-crunching power of Excel plus versatile and impactful visualization tools that will greatly enhance your discovery process and make it easier to communicate results. You will see that its data analysis expression (DAX) language is far richer and more powerful than Excels limited (and outdated) MDX; and its data ingestion utility is vastly superior. You will learn how to unearth unexpected trends and hidden correlations that might be elusive in the numbers but will emerge in high relief using visualization, speeding up analysis and making your data analysis far more complete. You will build analysis pages which, after you have completed a particular analysis, can be preserved along with your datasets for later use, and even passed along to others in an organization as what-if tools.
Hands-on exercises are provided that use downloadable data sources and starter configurations of Power BI files for building sample analyses. Downloadable Excel samples of those same exercises are provided for easy comparison.
What You Will Learn
Understand the exploratory methodology Build data sets and take a dive into DAX Add visualization to your analysis process Incorporate R and Python Use Power BI to extend your work
Who This Book Is For
Any business user who currently performs exploratory data analysis using tools other than Power BI, users who are not currently doing exploratory analysis but understand their data and how it is used and wish to begin studying it, managers and executives who wish to expand their organizations use of analytics and encourage new skills in their business workforce. Experience with Microsoft Excel is helpful but not essential.
Chapter 1: Exploratory Data Analysis A Quick Primer
Chapter Goal: Includes a review of the purpose, methods, and stages of
exploratory data analysis, as a summary for those with experience and an
introduction for those who have little or none.
Chapter 2: Power BI for Data Analysis
Chapter Goal: Power BIs features are most often leveraged for reporting;
this chapter articulates how its data handling tools and visualization
capabilities can be repurposed for data analysis.
Chapter 3: Building Datasets
Chapter Goal: Power BI has broad data modeling capabilities, and can
integrate data from many different sources. This chapter outlines those
capabilities and explains how data can be best configured for exploratory
analysis.
Chapter 4: A DAX Deep-Dive
Chapter Goal: The core of Power BIs utility in data analysis is its data
analysis expressions (DAX) language, the analog to Excels MDX. This deep
chapter surveys the full range of DAX as an analysis tool.
Chapter 5: Exploratory Methodology, Power BI-style
Chapter Goal: Everything from pivots to correlation to trending to regression
analysis is covered here, with detailed examples.
Chapter 6: Adding Visualization to the Analysis Process
Chapter Goal: The strengths of Power BI visualizations are their ease of use
and interactive features (slicing, drill-down, tooltips, etc). Using
visualization to accelerate discovery in the exploratory process is explained
with numerous examples, and some best-practice techniques are presented.
Chapter 7: Bringing in R and Python
Chapter Goal: Some more advanced business analysts use tools more
sophisticated than Excel, such as R and Python. Power BI can use embedded R
and Python code to combine analyses done in those languages with its rich
visualization capabilities.
Chapter 8: Using Power BI to Extend Your Work
Chapter Goal: Explain how Power BI can be used to turn exploratory results
into presentations, preliminary datasets for further work, and analysis tools
that others can use.
Scott Robinson is an IT veteran with over 20 years of experience in the data architecture and engineering workspace, in a range of industries from healthcare to insurance to supply chain. He also has been a tech writer for 20 years, writing for a broad range of popular tech websites. In addition to his architecture roles, Scott has been a developer, database designer, database administrator, data scientist, cloud technology consultant, and IT trainer. He decided to write this book while working with a group of business analysts in insurance who were frustrated that they had no real options beyond Excel for doing their exploratory work.
