Muutke küpsiste eelistusi

E-raamat: Tableau Strategies: Solving Real, Practical Problems with Data Analytics

4.67/5 (6 hinnangut Goodreads-ist)
,
  • Formaat: 578 pages
  • Ilmumisaeg: 28-Jul-2021
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781492080039
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 47,96 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Tableau Strategies: Solving Real, Practical Problems with Data AnalyticsSuurem pilt
  • Formaat: 578 pages
  • Ilmumisaeg: 28-Jul-2021
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781492080039
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

If you want to increase Tableau's value to your organization, this practical book has your back. Authors Ann Jackson and Luke Stanke guide data analysts through recipes for solving real-world analytics problems using Tableau. Starting with the basics and building toward advanced topics such as multidimensional analysis and user experience, you'll explore pragmatic and creative examples that you can apply to your own data.

Staying competitive today requires the ability to quickly analyze, visualize, and make data-driven decisions. With this guide, data practitioners and leaders alike will learn strategies for building compelling and purposeful visualizations, dashboards, and data products. Every chapter contains the why behind the solution and the technical knowledge you need to make it work.

  • Visualize different data types and tackle specific data challenges
  • Create compelling data visualizations, dashboards, and data products
  • Learn how to generate industry-specific analytics
  • Use this book as a high-value on-the-job reference guide to Tableau
  • Explore categorical and quantitative analysis and comparisons
  • Understand geospatial, dynamic, and statistical and multivariate analysis
  • Communicate the value of the Tableau platform to your team and to stakeholders
Preface xi
1 Categorical Analysis
1(30)
Bar Charts: Banco de Tableau Case Study
3(10)
Strategy: Build a Bar Chart in Tableau
4(1)
Strategy: Create a Top N Bar Chart
5(2)
Strategy: Dynamically Group Other Dimensions
7(1)
Strategy: Enhance Your Bar Chart with Color
8(1)
Strategy: Left-align Text
9(1)
Strategy: Create Bars with Labels on Top
10(2)
Strategy: Create a Percent-of-Maximum Bar Chart
12(1)
Bar-on-Bar Charts: Amplify Performance Case Study
13(7)
Strategy: Create a Bar-on-Bar Chart
15(5)
Treemaps: Amplify Performance Case Study
20(5)
Strategy: Create a Basic Treemap
21(1)
Strategy: Create Drillable Treemaps
22(1)
Strategy: Encode a Continuous Measure with Color
23(2)
Pie and Donut Charts: IT Employee Wellness Project Case Study
25(3)
Strategy: Build a Basic Pie Chart
25(1)
Strategy: Build a Donut Chart
26(2)
Strategy: Create Small Multiples
28(1)
Conclusion
28(3)
2 Quantitative Analysis
31(40)
Histograms: Office Essentials Case Study
32(9)
Strategy: Make a Simple Histogram of Purchasing Behavior
33(4)
Strategy: Create a Histogram with a Continuous Bin
37(1)
Strategy: Use LOD Expressions in Histograms
38(3)
Dot Plots and Jitterplots: Office Essentials Case Study
41(3)
Strategy: Create a Basic Dot Plot
41(1)
Strategy: Create a Jitterplot
42(2)
Ranged Dot Plots: Office Essentials Case Study
44(4)
Strategy: Create a Ranged Dot Plot
45(2)
Strategy: Use Median and Leveraged Percentiles
47(1)
Box Plots: Spear-Tukey Shipping Case Study
48(7)
Strategy: Create a Basic Box Plot
50(4)
Strategy: Combine a Box Plot and Histogram
54(1)
Line Charts: Office Essentials Case Study
55(7)
Strategy: Build a Line Chart
56(1)
Strategy: Add Reference Distributions to a Line Chart
57(2)
Strategy: Add a Standard Deviation to a Line Chart
59(2)
Strategy: Use Reference Distributions as an Alerting Tactic
61(1)
Calculated Dimensions Based on Statistics: Banco de Tableau Case Study
62(2)
Strategy: Assign a Summary Statistical Value to Data Points
63(1)
Pareto Charts: Amplify Performance (AP) Case Study
64(4)
Strategy: Use Pareto Charts to Show Categorical Data
65(3)
Conclusion
68(3)
3 Making Comparisons
71(68)
Bar Charts and Alternatives: Amplify Performance Case Study
72(14)
Strategy: Compare with a Basic Bar Chart
73(1)
Strategy: Convert a Vertical Bar Chart to Horizontal
74(2)
Strategy: Create a Lollipop Chart
76(5)
Strategy: Create a Basic Cleveland Dot Plot
81(5)
Bar Charts for Rank Changes: AP Case Study
86(10)
Strategy: Show Rank and Change of Rank on a Bar Chart
86(10)
Bump Charts for Rank Changes over Multiple Periods: AP Case Study
96(9)
Strategy: Make a Bump Chart
98(7)
Barbell Plots for Hierarchical Data: Office Essentials Case Study
105(8)
Strategy: Build a Barbell Plot
109(4)
Trellis Charts/Small Multiples: Office Essentials Case Study
113(13)
Strategy: Create a Trellis Chart for a Single Dimension
116(10)
Parallel Coordinates Plots for Multiple Measures: OE Case Study
126(11)
Strategy: Build a Parallel Coordinates Chart
128(9)
Conclusion
137(2)
4 Working with Time
139(38)
Understanding Dates and Time
141(1)
Date Parts and Date Values
142(1)
Date Calculations
142(1)
Date Hierarchies and Custom Dates
143(1)
Discrete Versus Continuous Dates
144(2)
Call Frequency: Chips and Bolts Call Center Case Study
146(8)
Strategy: Determine Total Call Time by Hour
147(1)
Strategy: Create a Plot to Measure Total Call Time by Minute
148(2)
Strategy: Create a Continuous Datetime Axis by the Second
150(1)
Strategy: Create a Continuous Datetime Axis for 15-Second Intervals
151(1)
Strategy: Create a Continuous Datetime Axis for 15-Minute Intervals
152(2)
Heatmaps (Highlight Tables)
154(3)
Strategy: Build an Essential Heatmap
154(1)
Strategy: Create a More Detailed Heatmap
155(2)
Comparing Values Year-to-Date: CaB Call Center Case Study
157(6)
Strategy: Show Progress to the Total by Using Two Bar Charts
159(2)
Strategy: Compare Similar Periods on a Line Chart
161(2)
Automated Reports
163(1)
Automating Reports for Month-over-Month and Year-over-Year Change: CaB Call Center Case Study
163(4)
Strategy: Automated Rolling Table
164(3)
Nonstandard Calendars
167(3)
Strategy: Build a Monthly Bar Chart with a June 1 Fiscal Year Start
169(1)
Visualizing the 4-5-4 Calendar: Office Essentials Case Study
170(5)
Strategy: Build a Bar Chart Using the 4-5-4 Retail Calendar
171(4)
Conclusion
175(2)
5 Key Performance Indicators
177(38)
Displaying KPIs
179(1)
Displaying KPIs: Office Essentials Case Study
180(10)
Strategy: Designing Clear KPIs
181(2)
Strategy: Create Single-Sheet KPIs
183(1)
Strategy: Automate Your KPIs
184(3)
Strategy: Create Year-over-Year Calculations
187(3)
Sparklines
190(1)
Sparklines: Office Essentials Case Study
190(9)
Strategy: Create Sparklines
190(3)
Strategy: Create Year-to-Date Sparklines
193(1)
Strategy: Create Rolling 24-Month Sparklines
194(1)
Strategy: Build Delta Spark Bars
195(1)
Strategy: Progress to Target
196(3)
Multiple KPIs as a Scorecard
199(1)
KPI Scorecard: Office Essentials Case Study
199(4)
Strategy: Create Aggregate KPIs
201(2)
Tracking Daily Changes: Office Essentials Case Study
203(9)
Strategy: Create Month-to-Date Sparklines
204(5)
Strategy: Create Month-to-Date KPIs
209(2)
Strategy: Combine KPI Visualizations in a Dashboard
211(1)
Conclusion
212(3)
6 Building Impactful Tables
215(46)
Building Great Tables
216(2)
Ensure a Clear Purpose for Your Table
218(1)
Format Your Table to Maximize Readability
218(1)
Using Color in Tables: Office Essentials Case Study
219(11)
Strategy: Create a Table Using Limited Color
219(7)
Strategy: Encode Color with Dots
226(4)
Moving Beyond Measure Names and Values: Office Essentials Case Study
230(20)
Strategy: Create a Table Body Without Measure Names or Measure Values
232(6)
Strategy: Create Table Headers Without Measure Names or Measure Values
238(2)
Strategy: Use Measures as Headers
240(10)
Problem-Solving with Subsets and Hiding the Dreaded Abe: Office Essentials Case Study
250(8)
Strategy: Show the Top Products Within Sub-Categories
251(6)
Strategy: Hide the Abe
257(1)
Conclusion
258(3)
7 Working with Geospatial Data
261(30)
Choropleth (Filled) Maps
262(1)
Displaying Customer Penetration with Choropleth Maps: Office Essentials Case Study
263(7)
Strategy: Create a Simple Choropleth Map
263(1)
Strategy: Normalize a Choropleth Map
264(2)
Strategy: Add a Custom Color Palette
266(2)
Strategy: Make a Decile Choropleth Map
268(2)
Symbol Maps
270(1)
Using Symbol Maps to Show Profitability and Channel Distribution: OE Case Study
270(4)
Strategy: Create a Symbol Map
271(2)
Strategy: Create a Map with Donut Charts
273(1)
Tile Maps (Cartograms)
274(1)
Showing Education Level with a Tile Map: Aloft Educational Services Case Study
274(5)
Strategy: Build a Square Tile Map
275(2)
Strategy: Compare Parts Against the Whole with a Tile Map
277(2)
Built-in Features and Functions
279(1)
Using Built-in Features and Functions with Marketing Data: SAGE Digital Marketing Case Study
279(10)
Strategy: Build Custom Polygons Using Groups
279(4)
Strategy: Build Custom Polygons Using Underlying Geographic Data
283(1)
Strategy: Use the Distance Function to Show Zip Codes' Distance from a Central Location
284(2)
Strategy: Use the Buffer Function to Map a Trade Area Using Radial Distance
286(2)
Strategy: Create Paths Between a Starting Point and Destinations with MakePoint and MakeLine
288(1)
Conclusion
289(2)
8 Advanced Mathematical Concepts
291(40)
Forecasting
292(1)
Using Forecasting to Predict Staffing Needs: CaB Call Center Case Study
293(4)
Strategy: Create a Monthly Forecast
293(4)
Relationships Between Two Numerical Values
297(2)
Using Scatter Plots to See Relationships Between Spend and Conversions: SAGE Digital Marketing Case Study
299(9)
Strategy: Create a Scatter Plot
299(1)
Strategy: Create a Quadrant Chart
300(5)
Strategy: Add a Trend Line to a Scatter Plot
305(3)
Cluster Analysis
308(1)
Creating Segmentation Among Employees to Assess Differences in Attrition: Banco de Tableau Case Study
309(4)
Strategy: Add Cluster Analysis to a Scatter Plot and Use Analysis in Another Chart
309(4)
Alternative Axis and Many Multiples
313(1)
Tracking Positive Cases of COVID-19 Globally: Logistics Case Study
314(7)
Strategy: Use a Logarithmic Axis to Understand Rate of Change
314(2)
Strategy: Normalize a Date Axis
316(2)
Strategy: Create a Trellis Chart
318(3)
Advanced Modeling Using Statistical Add-Ons
321(2)
Using Python Analytics Extension for Web Page A/B Testing: Squeaks Pet Supply Case Study
323(4)
Sentiment Analysis on Customer Reviews: Amazing Products Case Study
327(2)
Conclusion
329(2)
9 Constructing Dynamic Analyses
331(28)
Parameters
332(1)
Using Parameters to Change Measures and Dimensions: Office Essentials Case Study
332(5)
Strategy: Use a Parameter to Change a Measure in a Bar Chart
333(2)
Strategy: Use Regex Functions to Dynamically Format a Parameterized Metric
335(1)
Strategy: Use a Parameter to Change the Dimension in a Bar Chart
336(1)
Using Parameters to Set Time Periods and Date Aggregations in Line Charts: Office Essentials Case Study
337(4)
Strategy: Use a Parameter to Change the Date in a Line Chart
338(1)
Strategy: Add Hidden Date Filters to Prevent Truncated Time Parts
339(2)
Using Parameter Actions to Change Trended Metrics: SAGE Digital Marketing Case Study
341(3)
Strategy: Change a Metric Dynamically Using Parameter Actions and Measure Names
341(3)
Using Parameter Actions to View Daily and 7-Day Average Value: SAGE Digital Marketing Case Study
344(2)
Strategy: Compute a 7-Day Moving Average Reference Line That Dynamically Changes with a Date Selection
345(1)
Sets and Set Actions
346(1)
Using Set Actions to Drill Down and Filter Data: Digital Marketing Case Study
347(5)
Strategy: Drill from Week to Date in a Line Chart
348(2)
Strategy: Expand a Section of a Data Table
350(2)
Animation
352(1)
Competitor Analysis Study: Using Animations to Compare Ranking Performance
352(5)
Strategy: Build an Animated Bump Chart
353(2)
Strategy: Create a Racing Bar Chart
355(2)
Conclusion
357(2)
10 Advanced Data Modeling
359(66)
Data Modeling
360(31)
Strategy: Create a Calendar with Data Densification
362(5)
Strategy: Create Rounded Bar Charts
367(8)
Strategy: Create an Accordion Table
375(7)
Strategy: Create a Sales Funnel
382(9)
Market Basket Analysis
391(33)
Strategy: Basket Analysis
393(8)
Strategy: Build a Multidimension Waterfall
401(12)
Strategy: Layer Marks with Maps Without Making a Map
413(11)
Conclusion
424(1)
Further Reading
424(1)
11 Advanced Interactivity
425(56)
Sheet Swapping with Parameters and Parameter Actions: Superstore Case Study
426(13)
Strategy: Use Parameter-Based Sheet Swapping
428(4)
Strategy: Use a Parameter-Action Sheet Swap
432(5)
Strategy: Automatically Deselect Marks
437(2)
Using Parameters and Parameter Actions to Create Multiple Select Parameters: Superstore Case Study
439(8)
Strategy: Create a Multiple-Select Parameter
440(7)
Swapping Metrics Using Parameter Actions: Office Essentials Case Study
447(8)
Strategy: Use Sheets as Buttons
448(7)
Adding Pagination to Tables with Parameters, Parameter Actions: Superstore Case Study
455(17)
Strategy: Add Table Pagination
457(15)
Creating Dynamic, Single-Click Drill-Throughs: Superstore Case Study
472(6)
Strategy: Build Drill-Through Interactivity
473(5)
Conclusion
478(1)
Further Reading
479(2)
12 Building Dashboards and Data Products
481(46)
Dashboard Design
482(1)
Know Your Audience
482(2)
Dashboard Types
484(5)
Mobile Dashboard Design
489(1)
Design Details
490(10)
Strategy: Display Different Visuals for Desktop and Mobile
495(5)
Accessibility
500(1)
Dyslexia
500(14)
Color and Visual Impairment
514(5)
Formatting Consistency
519(1)
Rounding Numbers
519(1)
Axes Versus Labels
520(3)
Font Styling
523(1)
Conclusion
524(3)
13 The Broader Tableau Ecosystem
527(12)
Data Preparation: Where and When
527(1)
Spreadsheets: Microsoft Excel
528(1)
Traditional Data Preparation: SQL
529(2)
Modern Data Preparation: ELT Tools
531(1)
Review
532(1)
Building a Thriving Analytics Platform
532(3)
Future Trends
535(1)
Data Journalism and the Rise of Data Storytelling
536(1)
Analytics Within Human Workflows
536(1)
Analytics as a Product
537(1)
Conclusion
538(1)
14 Industry Frameworks
539(8)
Healthcare
540(1)
Education
541(1)
Elementary, Middle, and High School
542(1)
College and University
542(1)
Logistics
543(1)
Marketing
543(1)
Sales
544(1)
Retail
545(1)
Finance
545(1)
Conclusion
546(1)
Index 547
Ann Jackson is the founder and Chief Analytics Evangelist at Jackson Two, a boutique consulting firm specializing in data analytics and visualization. Her practice focuses on enabling and empowering businesses to fully utilize their data assets. This is accomplished by making compelling visualization solutions, training team members on modern analytics tools, and building connections between multiple data sources. Ann has been recognized as a Tableau Zen Master multiple times due to her mastery of the product, participation and leadership within the global community, and her contagious passion for data analytics. She has also co-led Workout Wednesday (WoW), a weekly data visualization technical skill building challenge, since 2018. Luke co-leads an analytics consultancy firm that focuses on data visualization and advanced analytics. He has worked for some of the largest retailers. He was awarded the status of Tableau Zen Master in 2019 for his mastery of the product, willingness to share knowledge, and most desire to grow the Tableau community and solutions of tomorrow. Luke co-leads WOW a Tableau community initiative where participants challenge themselves to rebuild a dashboard from scratch using predefined requirements and a fully built dashboard as inspiration. Luke has his PhD in Educational Psychology and is regarded for his data science expertise, where he blends business-first approaches, modern data approaches, and the latest advances in machine learning.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97814920800396e.html
Märksõnad: