Going Deep Into Data Using Visualization
Buch, Englisch, 315 Seiten, Format (B × H): 155 mm x 235 mm
ISBN: 978-1-4842-9370-6
Verlag: Apress
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 BI’s 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 BI’s 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 Microsoft’s 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You will see that its data analysis expression (DAX) language is far richer and more powerful than Excel’s limited (and outdated) MDX; and its data ingestion utility is vastly superior.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.
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 organization’s use of analytics and encourage new skills in their business workforce. Experience with Microsoft Excel is helpful but not essential.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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 BI’s 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 BI’s utility in data analysis is its data analysis expressions (DAX) language, the analog to Excel’s 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 WorkChapter 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.
