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E-raamat: Learning Microsoft Power BI

  • Formaat: 310 pages
  • Ilmumisaeg: 20-Sep-2022
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781098112813
  • Formaat - PDF+DRM
  • Hind: 47,96 €*
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  • Formaat: 310 pages
  • Ilmumisaeg: 20-Sep-2022
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781098112813

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Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own.

This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool.

You'll learn how to:

  • Import, manipulate, visualize, and investigate data in Power BI
  • Approach solutions for both self-service and enterprise BI
  • Use Power BI in your organization's business intelligence strategy
  • Produce effective reports and dashboards
  • Create environments for sharing reports and managing data access with your team
  • Determine the right solution for using Power BI offerings based on size, security, and computational needs

Preface xi
1 Intro to Power BI
1(12)
What Is Power BI?
2(4)
Power BI Components
2(2)
Power BI Desktop
4(1)
The Power BI Service
4(1)
The Power Platform
5(1)
How Did We Get to Power BI?
6(3)
SQL Server: Microsoft's Relational Database
7(1)
SQL Server Analysis Services Multidimensional: One Small Step into BI
7(1)
SQL Server Reporting Services: Pixel-Perfect Reporting, Automated Reports, and More
7(1)
Excel: A Self-Service BI Tool
7(1)
Power Pivot
8(1)
Important New Functionality That Leads to Power BI
8(1)
Power BI Desktop Is Born
8(1)
Power BI Desktop Under the Hood
9(1)
VertiPaq: The Storage Engine
9(1)
DAX: The Formula Engine
10(1)
What Makes Power BI Different from Its Competitors?
10(2)
Conclusion
12(1)
2 The Report and Data Views
13(30)
Report View: Home Section of the Ribbon
14(11)
The Clipboard Subsection
15(1)
The Data Subsection
15(6)
The Queries Subsection
21(1)
The Insert Subsection
22(2)
The Calculations Subsection
24(1)
The Final Subsections: Sensitivity and Share
24(1)
Report View: The Insert Tab
25(3)
The Pages Subsection
25(1)
The Visuals Subsection
25(1)
The AI Visuals Subsection
25(1)
The Power Platform Subsection
26(1)
The Elements Subsection
26(2)
Report View: The Modeling Tab
28(3)
The Relationships Subsection
28(1)
The Calculations Subsection
29(1)
The Page Refresh Subsection
29(1)
The What If Subsection
29(1)
The Security Subsection
29(1)
The Q&A Subsection
30(1)
Report View: The View Tab
31(1)
The Themes Subsection
31(1)
The Scale to Fit Subsection
31(1)
The Page Options Subsection
31(1)
The Show Panes Subsection
32(1)
Report View: Help Section
32(1)
Report View: External Tools Section
33(1)
The Pane Interface of the Report View
33(5)
Visualizations Pane
34(1)
Fields and Filters Panes
35(2)
A Quick Rundown of the Other Panes
37(1)
Data View
38(3)
Conclusion
41(2)
3 Importing and Modeling Our Data
43(26)
Getting Our Data
43(4)
The Power Query Ribbon
47(10)
The Home Tab
47(5)
The Transform Tab
52(3)
The Add Column Tab
55(2)
The Model View
57(10)
What Is a Relationship?
58(7)
The Properties Pane
65(2)
Conclusion
67(2)
4 Let's Make Some Pictures (Visualizing Data 101)
69(32)
Why Visualize Data?
69(2)
The Visualizations Pane
71(3)
Fields
73(1)
Format
73(1)
Analytics
73(1)
Visual Interactivity
74(2)
Column and Bar Charts
76(7)
Stacked Bar and Column Charts
77(2)
Clustered Bar and Column Charts
79(1)
100% Stacked Bar and Column Charts
80(2)
Small Multiples
82(1)
Waterfall Chart
82(1)
Line and Area Charts
83(4)
Line Chart
83(1)
Area Chart
84(1)
Stacked Area Chart
85(1)
Line and Stacked Column Chart/Clustered Column Chart
86(1)
Ribbon Chart
87(1)
Donuts, Dots, and Maps, Oh My!
87(6)
Funnel Chart
88(1)
Scatter Chart
89(1)
Pie and Donut Chart
90(1)
Treemap
91(1)
Map Visuals
92(1)
The "Flat" Visuals
93(6)
Gauge
93(1)
Card/Multi-Row Card
94(1)
KPI
95(1)
Table/Matrix
96(2)
Slicer
98(1)
Conclusion
99(2)
5 Aggregations, Measures, and DAX
101(26)
A Primer on the DAX Language
101(3)
Measures
102(1)
Calculated Columns
102(1)
Calculated Tables
103(1)
Types of Functions
103(1)
Aggregations, More Than Some Sums
104(10)
Sum
104(2)
Average
106(2)
Minimum and Maximum
108(2)
Standard Deviation, Variance, and Median
110(1)
Count and Count (Distinct)
111(2)
First, Last, Earliest, and Latest
113(1)
Measures and DAX Fundamentals
114(12)
Implicit and Explicit Measures
114(2)
Dax Syntax Fundamentals
116(1)
Calculate
117(3)
We Heard You Like DAX, So We Put Some DAX in Your DAX
120(2)
Row and Filter Context
122(2)
One Final DAX Example
124(2)
Conclusion
126(1)
6 Putting the Puzzle Pieces Together: From Raw Data to Report
127(34)
Your First Data Import
127(5)
Choose and Transform the Data When You Import
128(1)
Transformations in Power Query
129(3)
Second Data Import and Wrangling
132(12)
Consolidating Tables with Append
134(4)
Using Merge to Get Columns from Other Tables
138(6)
Building Relationships
144(5)
Hiding Tables
145(1)
Identifying Our Relationship Columns
146(1)
Time to Get Building
147(2)
Lets Get Reporting!
149(12)
We Need a Name...
150(1)
Cards Help Identify Important Data Points
150(6)
Bars, Columns, and Lines
156(4)
Conclusion
160(1)
7 Advanced Reporting Topics in Power BI
161(20)
Al-Powered Visuals
161(12)
Key Influencers
162(3)
Decomposition Tree
165(2)
Q&A
167(5)
Smart Narrative
172(1)
What-If Analysis
173(4)
Parameter Setup
173(1)
DAX Integration of the Parameter
174(2)
Parameter Modification
176(1)
R and Python Integration
177(3)
Limitations of Using R and Python
177(1)
Enabling R and Python for Power BI
178(1)
R and Python in Power Query
178(1)
R and Python Visuals
179(1)
Conclusion
180(1)
8 Introduction to the Power BI Service
181(28)
The Basics of the Service: What You Need to Know
181(1)
The Navigation Menu
182(14)
Home and Browse
184(1)
Create
185(2)
Data Hub
187(3)
Settings
190(3)
Metrics
193(1)
Apps
193(2)
Deployment Pipelines
195(1)
Learn
195(1)
Publishing Your Work
196(2)
What Is a Workspace?
198(4)
My Workspace
198(1)
Shared Capacity Workspaces
198(3)
Dataflows in Shared Workspaces
201(1)
Putting Your Data in Front of Others
202(5)
Adding Users to a Workspace
202(1)
Sharing via a Link or Teams
203(2)
Sharing via SharePoint
205(1)
Creating an App
205(2)
Conclusion
207(2)
9 Licensing and Deployment Tips
209(22)
Licensing
209(6)
Pro Licensing
210(1)
Premium Per User Licensing
211(1)
Premium Per Capacity, the Big Boy
212(3)
Workspace and App Management
215(14)
Workspace Generation and Access Control
216(4)
Managing Users in a Workspace
220(2)
Adding Users to Roles for RLS Implementation
222(1)
App Creation and Management
223(5)
The Golden Dataset(s)
228(1)
Conclusion
229(2)
10 Third-Party Tools
231(28)
Get to Know Business Ops
232(9)
Add External Tools, Remove External Tools, and Modify Display Order
233(2)
Learning, Theme Generation, Visual Generation
235(4)
Additional DAX Resources
239(2)
DAX Studio
241(4)
Tabular Editor
245(6)
Creating Roles
248(1)
Table and Measure Management
249(2)
The ALM Toolkit for Power BI
251(3)
Bravo
254(4)
Analyze Model
255(1)
DAX Formatting
256(1)
Manage Dates
256(1)
Export Data
257(1)
Conclusion
258(1)
A Commonly Used DAX Expressions 259(12)
B Some Favorite Custom Visuals 271(10)
Index 281
Jeremey Arnold is the Senior Analytics Architect at Onebridge, a large Data Analytics consulting firm in Indianapolis, Indiana. Jeremey has worked in data analytics for over a decade and has been a Microsoft Power BI user since its release in 2013. His experience covers multiple industries from healthcare, finance, manufacturing, and the public sector, all with a focus on transforming data into insights and enabling truly data-driven environments.