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Visual Analytics Session 2
Where do we stand?
• Understood visual analytics fundamentals
• Understood stakeholders requirements
• Understood industry applications of Tableau
Learning goals from session 2
• Install Tableau Public Version
• Understand fundamentals of data visualizations
• Load data sets into Tableau
• Create first visualizations in Tableau
• Write first calculations in Tableau
• Build your Tableau portfolio
Agenda
Visual Analytics
in Practice
Introduction to Tableau
ecosystem
Install Tableau
Introduction to Tableau
Interface
Load data into Tableau
Create your first visualization
04
Introduction to Tableau ecosystem
• Tableau Desktop Enterprise
• Tableau Desktop Public
• Tableau Server
Image source: Tableau software
04
Introduction to Tableau Public
Image source: Tableau software
04
Introduction to Tableau Desktop
Image source: Tableau software
04
Tableau Public vs Tableau Desktop enterprise
Public Desktop enterprise
Limited access to external
databases
Connects multiple databases and cloud
services
Allows publishing your
workbooks on Tableau Public
domain
Allows saving workbooks on your desktop
Allows performing statistical
functions
Allows integrations of R, Python for Predictive
Analytics
Free version Paid Subscription (14 day trial)
Agenda
Visual Analytics
in Practice
Introduction to Tableau ecosystem
Install Tableau
Introduction to Tableau
Interface
Load data into Tableau
Create your first visualization
04
Installing Tableau Public
Image source: Tableau software
public.tableau.com
04
Installing Tableau Desktop Enterprise
Image source: Tableau software
www.tableau.com
Minimum system requirements
www.tableau.com
Intel Pentium 4 or AMD
2 GB memory
1.5 GB minimum free disk space
Windows or Mac OSX
Where to find help?
tableau.com/support/help
Agenda
Visual Analytics
in Practice
Introduction to Tableau ecosystem
Install Tableau
Introduction to Tableau
Interface
Load data into Tableau
Create your first visualizations
Load Data options
Image source: Tableau software
Introduction to Tableau interface
Tableau
resources
External Data sources
Introduction to Tableau interface
Data sources:
Excel,
CSV,
MS Access,
Image source: Tableau software
Loaded
Work Sheet Data Connection
preference
New Work Sheet
New Dashboard
Data Source
Filter
New Storyline
Introduction to Tableau interface
Loaded
Data source
Data Table
or
Worksheet
Image source: Tableau software
View pane
Data pane
Dimensions
Measures
Analytics pane
Marks
shelf
Rows and column shelves
Image source: Tableau software
Introduction to Tableau interface
View pane
Add data
source Display fit
LegendsToolbar
Shelves
Visualizations
Pane
Save
Image source: Tableau software
Introduction to Tableau interface
Agenda
Visual Analytics
in Practice
Introduction to Tableau ecosystem
Install Tableau
Introduction to Tableau
Interface
Load data into Tableau
Create your first visualization
Data types Tableau recognizes
Image source: Tableau software
Lets understand about Discrete & Continuous data
Image source: keydifferences.com
Data types are interchangeable in Tableau
Image source: Tableau software
Case Studies & Projects we will work with
• Sample, hypothetical data sets (ideal for developing basic skills)
• Compiled real world data sets (ideal for developing basic skills)
• Other Real world data sets (useful for projects,
developing skill proficiency)
Most commonly used file types and databases
Image source: proprietary logos
Where can you obtain sample data sets?
Image source: proprietary logos
public.tableau.com/en-us/s/resources
• Assists in making business decisions
• Helps in summarizing observations & analysis
• Helps people visualize & understand data quickly
• Helps in building archive of analytics projects for later use
Recap:Why is visual analytics required?
Agenda
Visual Analytics
in Practice
Introduction to Tableau ecosystem
Install Tableau
Introduction to Tableau
Interface
Load data into Tableau
Create your first visualizations
Before we create our first dashboard
• Learn best practices for creating graphs or visualizations for dashboards
• Look into common types of graphs used in dashboards
• Learn basic calculations when creating graphs
Tableau allows us to create visual reports,
especially dashboards!
Common types of visual reports in Analytics ecosystem
• Dashboards: Interactive Graphs + Legends + Text
• Short Data Stories (infographics): Graphs + Text
• Detailed Presentations (PowerPoint): Graphs + Text
• Consulting Reports: Graphs + Text heavy Documents
5 Important characteristics ofVisual reports
• Focus on one or two or key findings
• Describe insights in simple language
• Create a vision for your readers
• Focus on the things you want readers to remember
• Choose the points you think are timely
Graphs are constituents of visual reports,
especially dashboards!
4 Important characteristics of Graphs
• Show big picture by presenting data points
• Convey one finding or a single concept
• Highlight data by avoiding extra information and distractions
• Present logical visual patterns
Now lets examine some good and bad graphs
Example of an ideal Graph
Example of bad Graphs
Cluttered, ambiguous,
creates confusion
Now lets examine some more ideal graphs
Bar and Stacked Graphs
Pie Graphs
Scatter plots
Representing data usingTables
Representing data using Geo visualizations
Finally how to convey analytics insights?
Finally how to convey analytics insights?
Summarizing - good reporting etiquette
• Define objectives of analysis  Keep in mind stakeholder
requirements
• Break down all analysis  Explain why and how you are performing
analysis
• Stitch the story  Make a presentable outcome
Now lets examine calculations useful for visual analytics
Basics of Calculations inTableau
The type of calculation
you choose depends
on the needs of your
analysis and the
question you want to
answer
Why do we use calculations inTableau?
• To segment data
• To convert data type of a field, e.g. converting a string to a date
• To aggregate data
• To filter results
• To calculate ratios
Different Calculations types inTableau
Basic calculations - Basic calculations allow row-level calculation or at
the visualization level of detail i.e. an aggregate calculation
Level of Detail (LOD) expressions - LOD calculations give you control
on the level of granularity you want to compute
Table calculations - Table calculations allow you to transform values at
the level of detail of the visualization
What are Calculated fields inTableau?
Allow us to write formulas to execute calculations
Tableau supports many functions for calculations
The different types of functions inTableau
• Numbers
• Logical
• Type conversion
• String
• Date
• Aggregations
onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
• Install Tableau
• Load data into tableau
• Learn basic rules for creating visualizations and reporting
• Understand basics of calculations and functions used in Tableau
Recap: Session 2
Overview of session-wise exercises
Exercise Session 1 – Creating basic data visualizations and Introduction to Calculations
Exercise Session 2 – Descriptive stats on H1B Visa Data + Geo Data analysis
Exercise Session 3 – Mini project on Start-up investment analysis
Exercise Session 4 – Data Joining and Blending + Retail Sales comparison
Exercise Session 5 – Mini project - Customer segmentation & Predictive analytics using R
Exercise Session 6 & 7 – Implementing LOD calculations + Retail sales data set
Exercise Session 8 – Customer lifetime analysis + Retail Sales data set
Exercise Session 9 – Tableau final project on Data Visualization challenge
onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
Tableau Visual analytics complete deck 2
Visual Analytics Session - 3
Where do we stand?
• Understood Data types
• Created visualizations
• Created calculation fields
• Used filters
• Did basic calculations
• Developed dashboards
Learning goals from session 3
• Perform data joining and blending
• Group data
• Create static sets
• Create dynamic sets and set parameters
• Finally crunch data
Agenda
Visual Analytics
in Practice
Revision of key concepts till
session 2
Case study
Introduction to data joins
Introduction to data blends
Tableau perspective of joins and
blends
Data joining & blending
Why do we need to join or blend data?
http://wikiclipart.com
Exploring the idea of joining data
Lets assume you work
as an accountant at
Starbucks
Exploring the idea of joining data
You need to calculate
revenue data coming from all
over world
Data comes from various sources
This data might be located
in multiple data tables
connected with a common
feature
Data comes from various sources
Data joining & blending
The challenge is how to connect data
which allows analysis
http://wikiclipart.com
We can use certain common but unique features or variables or
keys to join various but similar data sources
How to connect data sources using Keys?
So what can be a common
Feature or key?
Customer id
Unique Product id
Transaction id
Region id
For example, keys can be unique fields like
These keys exist in the data
tables coming from similar
sources
For example, keys can be unique fields like
So how to join data using keys?
These keys allow matching
related data in different tables
The process of matching is
called data joining
There exist 4 types of joins depending on your need
Types of data Joins
Inner Joins
Left Joins
Right Joins
Outer Joins
Image Source: Adapted from Packt
Output of data join
Types of data Joins
Image Source: Adapted from Packt
Inner Joins
Left Joins
Right Joins
Outer Joins
Output of data join
Types of data Joins
Image Source: Adapted from Packt
Inner Joins
Left Joins
Right Joins
Outer Joins
Output of data join
Types of data Joins
Image Source: Adapted from Packt
Inner Joins
Left Joins
Right Joins
Outer Joins
Output of data join
Now lets look at a practical example using inner join
Example of inner data Join using primary key
Image Source: w3schools
Orders Table
Customers Table
Here the key used is Customer ID to join the data tables
Image Source: w3schools
Output from Inner Join in implementation
Example of inner data Join using primary key
So in an inner join we extract only common values in both the tables
For more details on other SQL joins please visit
w3schools.com/sql/
Data Blending
Now when to blend data?
source: Phobia wiki
Blending
Imagine revenues from India & USA Starbucks are calculated on
Monthly and Quarterly basis
So you perform blending to compare numbers
To compare revenues at different aggregations
Quarterly
revenue
Monthly
revenue
But the challenge is…
To compare revenues at different aggregations
Q1 Q2 Q3 Q4Monthly
Blending allows to manually connect and calculate…
Q1
Q2
Q3
Q4
Blending allows to compare numbers
Q1
Q2
Q3
Q4
$11,510,375 $11,510,375
$9,530,188 $9,530,188
$5,920,326 $5,920,326
$3,159,155 $3,159,155
Revenues
In a fiscal year
Data Joining occurs at (row level)
blending occurs at (aggregate level)
Joins and blends using Tableau
Data Joining (row level) vs blending (aggregate level)
Summarizing
Joins Blends
Row level connections Table level connections
Tables share a primary key &
have multiple connection
points
May not necessarily have
common fields or common
primary keys, with similar level
of data aggregations
Allow connecting data tables
from similar sources
Allow connecting and
comparing data from disparate
sources
• Perform data joins and blending into tableau
• Learn basic rules for data joins and blending
• Understand basics of SQL joins
• Understand the purpose of data blending
Recap: Session 3
An overview of the practical sessions
Exercise Session 1 – Creating basic data visualizations and Introduction to Calculations
Exercise Session 2 – Descriptive stats on H1B Visa Data + Geo Data analysis
Exercise Session 3 – Mini project on Start-up investment analysis
Exercise Session 4 – Data Joining and Blending + Retail Sales comparison
Exercise Session 5 – Mini project - Customer segmentation & Predictive analytics using R
Exercise Session 6 & 7 – Implementing LOD calculations + Retail sales data set
Exercise Session 8 – Customer lifetime analysis + Retail Sales data set
Exercise Session 9 – Tableau final project on Data Visualization challenge
onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
Visual Analytics Session - 4
Where do we stand?
• Understood Data types
• Created visualizations
• Created calculation fields
• Used filters
• Did calculations
• Developed dashboards
• Performed data joining and blending
Learning goals in session 4
• To perform data analysis
• To perform predictive analytics
• Explore statistical functions in Tableau
• Write R code using Tableau functions
Agenda
Visual Analytics
in Practice
Introduction to Data analytics
Introduction to Predictive
Analytics
Execute R functions in
Tableau
Connect Tableau and R
Descriptive & Predictive analytics process
Tableau supports extracting data from various databases
Role of Excel in data analysis
But when it comes to cleaning data,
Excel is a preferred program
Steps in Data Cleansing using MS Excel
• Backing up data
• Labelling columns
• Dealing with Duplicates
• Filtering data
• Using Quick Chart visualizations
• Replacing text
• Concatenate
• Splitting data
• Flash fill
• Exploring V-lookup functions
Source: http://www.datacleansing.net.au/
02
MS Excel functions used in Data Cleaning
Data cleaning with excel
TASK EXCEL FUNCTION
S p e l l c h e c k a n d R e m o v e
d u p l i c a t e s
Excel Spell Check And Remove Duplicate Data
Values Using Functions Located In Design And
Review Ribbons
R e m o v e s p a c e a n d n o n
p r i n t i n g c h a r a c t e r s
Clean(), Trim(), Substitute(), Code()
F i x i n g n u m b e r a n d n u m b e r
s i g n s
Dollar(), Text(), Fixed(), Value()
C h a n g i n g c a s e s o f t e x t Upper(), Lower(), Proper()
M e r g i n g a n d s p l i t t i n g
c o l u m n s
Transpose()
F i x i n g d a t e s a n d t i m e s Date(), Datevalue(), Time (), Timevalue()
M a t c h i n g d a t a v a l u e s Vlookup(), Lookup(), Index(), Match()
Reference: https://support.office.com/en-us/article/Top-ten-ways-to-clean-your-data-2844b620-677c-47a7-ac3e-c2e157d1db19
Agenda
Visual Analytics
in Practice
Introduction to Data analytics
Introduction to Predictive
Analytics
Execute R functions in
Tableau
Connect Tableau and R
The most commonly used Predictive models in Tableau
Linear Regression
Used in scenarios like
Housing price prediction
K-means clustering
Used in scenarios like
Identifying
unique customer groups
Predictive analytics using R allows us
• Allow forecasting: future revenues
• Allows deriving YES / NO answers: granting housing loans
• Allows clustering similar data: segmenting customers
• Understand unique and weird patterns: anomalies detection
Some more applications of predictive analytics
13
Why integrate R for predictive analysis?
Provides access to
10,000 analytics packages!
Predictive analytics using R allows us
• Increase Analytics power of Tableau
• Visualize Big Data calculations using Tableau
• Predict and Forecast business insights using Tableau
• Utilize statistical models and visualize output using Tableau
Why implement predictive analytics?
Agenda
Visual Analytics
in Practice
Introduction to Data analytics
Introduction to Predictive
Analytics
Execute R functions in
Tableau
Connect Tableau and R
3 functions used for executing R programs in Tableau
13
Execute R Programs in Tableau
SCRIPT_BOOL
Returns a Boolean result
i.e. the output is a YES / NO
Accept or Reject
a bank loan
13
Executing R functions in Tableau
SCRIPT_INT
Returns an Integer result
i.e. any specific number say 23
Predict number of
cars sold
13
Executing R functions in Tableau
SCRIPT_REAL
Returns a real result
i.e. any specific number
including a decimal
number
Predict average
Housing Price in
a location
13
How does sample R program code in Tableau look like?
Agenda
Visual Analytics
in Practice
Introduction to Data analytics
Introduction to Predictive
Analytics
Execute R functions in
Tableau
Connect Tableau and R
13
• Install R programming tool
• Install R studio open source edition
• Install package: ‘Rserve ’
• Install Tableau Desktop Enterprise trial version
• Connect R and Tableau using guidelines in the
course documents
Integrate R & Tableau in 4 steps
+
What groundwork is necessary to perform Analytics using Tableau
• Learn descriptive statistics
• Learn Inferential statistics
• Learn basic R programming
The groundwork needed
The resources for learning R and business statistics
Learn basic R programming
Provides basic coding courses
on R programming
Learn business statistics from this book
Provides detailed problems
on Business statistics
• Perform predictive analysis tableau
• Learn about important predictive analytics models
• Learn integrating Tableau and R
• Learn fundamentals of R programming
Recap: Session 4
An overview of the practical sessions
Exercise Session 1 – Creating basic data visualizations and Introduction to Calculations
Exercise Session 2 – Descriptive stats on H1B Visa Data + Geo Data analysis
Exercise Session 3 – Mini project on Start-up investment analysis
Exercise Session 4 – Data Joining and Blending + Retail Sales comparison
Exercise Session 5 – Mini project - Customer segmentation & Predictive analytics using R
Exercise Session 6 & 7 – Implementing LOD calculations + Retail sales data set
Exercise Session 8 – Customer lifetime analysis + Retail Sales data set
Exercise Session 9 – Tableau final project on Data Visualization challenge
onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
Any queries ? Now or any time in future, please write to
arun@upxacademy.com
If it’s a technical issue please attach a screenshot of the problem description
and inform the exact details
Tableau Visual analytics complete deck 2

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Tableau Visual analytics complete deck 2

  • 2. Where do we stand? • Understood visual analytics fundamentals • Understood stakeholders requirements • Understood industry applications of Tableau
  • 3. Learning goals from session 2 • Install Tableau Public Version • Understand fundamentals of data visualizations • Load data sets into Tableau • Create first visualizations in Tableau • Write first calculations in Tableau • Build your Tableau portfolio
  • 4. Agenda Visual Analytics in Practice Introduction to Tableau ecosystem Install Tableau Introduction to Tableau Interface Load data into Tableau Create your first visualization
  • 5. 04 Introduction to Tableau ecosystem • Tableau Desktop Enterprise • Tableau Desktop Public • Tableau Server Image source: Tableau software
  • 6. 04 Introduction to Tableau Public Image source: Tableau software
  • 7. 04 Introduction to Tableau Desktop Image source: Tableau software
  • 8. 04 Tableau Public vs Tableau Desktop enterprise Public Desktop enterprise Limited access to external databases Connects multiple databases and cloud services Allows publishing your workbooks on Tableau Public domain Allows saving workbooks on your desktop Allows performing statistical functions Allows integrations of R, Python for Predictive Analytics Free version Paid Subscription (14 day trial)
  • 9. Agenda Visual Analytics in Practice Introduction to Tableau ecosystem Install Tableau Introduction to Tableau Interface Load data into Tableau Create your first visualization
  • 10. 04 Installing Tableau Public Image source: Tableau software public.tableau.com
  • 11. 04 Installing Tableau Desktop Enterprise Image source: Tableau software www.tableau.com
  • 12. Minimum system requirements www.tableau.com Intel Pentium 4 or AMD 2 GB memory 1.5 GB minimum free disk space Windows or Mac OSX
  • 13. Where to find help? tableau.com/support/help
  • 14. Agenda Visual Analytics in Practice Introduction to Tableau ecosystem Install Tableau Introduction to Tableau Interface Load data into Tableau Create your first visualizations
  • 15. Load Data options Image source: Tableau software Introduction to Tableau interface Tableau resources
  • 16. External Data sources Introduction to Tableau interface Data sources: Excel, CSV, MS Access, Image source: Tableau software
  • 17. Loaded Work Sheet Data Connection preference New Work Sheet New Dashboard Data Source Filter New Storyline Introduction to Tableau interface Loaded Data source Data Table or Worksheet Image source: Tableau software
  • 18. View pane Data pane Dimensions Measures Analytics pane Marks shelf Rows and column shelves Image source: Tableau software Introduction to Tableau interface
  • 19. View pane Add data source Display fit LegendsToolbar Shelves Visualizations Pane Save Image source: Tableau software Introduction to Tableau interface
  • 20. Agenda Visual Analytics in Practice Introduction to Tableau ecosystem Install Tableau Introduction to Tableau Interface Load data into Tableau Create your first visualization
  • 21. Data types Tableau recognizes Image source: Tableau software
  • 22. Lets understand about Discrete & Continuous data Image source: keydifferences.com
  • 23. Data types are interchangeable in Tableau Image source: Tableau software
  • 24. Case Studies & Projects we will work with • Sample, hypothetical data sets (ideal for developing basic skills) • Compiled real world data sets (ideal for developing basic skills) • Other Real world data sets (useful for projects, developing skill proficiency)
  • 25. Most commonly used file types and databases Image source: proprietary logos
  • 26. Where can you obtain sample data sets? Image source: proprietary logos public.tableau.com/en-us/s/resources
  • 27. • Assists in making business decisions • Helps in summarizing observations & analysis • Helps people visualize & understand data quickly • Helps in building archive of analytics projects for later use Recap:Why is visual analytics required?
  • 28. Agenda Visual Analytics in Practice Introduction to Tableau ecosystem Install Tableau Introduction to Tableau Interface Load data into Tableau Create your first visualizations
  • 29. Before we create our first dashboard • Learn best practices for creating graphs or visualizations for dashboards • Look into common types of graphs used in dashboards • Learn basic calculations when creating graphs
  • 30. Tableau allows us to create visual reports, especially dashboards!
  • 31. Common types of visual reports in Analytics ecosystem • Dashboards: Interactive Graphs + Legends + Text • Short Data Stories (infographics): Graphs + Text • Detailed Presentations (PowerPoint): Graphs + Text • Consulting Reports: Graphs + Text heavy Documents
  • 32. 5 Important characteristics ofVisual reports • Focus on one or two or key findings • Describe insights in simple language • Create a vision for your readers • Focus on the things you want readers to remember • Choose the points you think are timely
  • 33. Graphs are constituents of visual reports, especially dashboards!
  • 34. 4 Important characteristics of Graphs • Show big picture by presenting data points • Convey one finding or a single concept • Highlight data by avoiding extra information and distractions • Present logical visual patterns
  • 35. Now lets examine some good and bad graphs
  • 36. Example of an ideal Graph
  • 37. Example of bad Graphs Cluttered, ambiguous, creates confusion
  • 38. Now lets examine some more ideal graphs
  • 39. Bar and Stacked Graphs
  • 43. Representing data using Geo visualizations
  • 44. Finally how to convey analytics insights?
  • 45. Finally how to convey analytics insights?
  • 46. Summarizing - good reporting etiquette • Define objectives of analysis  Keep in mind stakeholder requirements • Break down all analysis  Explain why and how you are performing analysis • Stitch the story  Make a presentable outcome
  • 47. Now lets examine calculations useful for visual analytics
  • 48. Basics of Calculations inTableau The type of calculation you choose depends on the needs of your analysis and the question you want to answer
  • 49. Why do we use calculations inTableau? • To segment data • To convert data type of a field, e.g. converting a string to a date • To aggregate data • To filter results • To calculate ratios
  • 50. Different Calculations types inTableau Basic calculations - Basic calculations allow row-level calculation or at the visualization level of detail i.e. an aggregate calculation Level of Detail (LOD) expressions - LOD calculations give you control on the level of granularity you want to compute Table calculations - Table calculations allow you to transform values at the level of detail of the visualization
  • 51. What are Calculated fields inTableau? Allow us to write formulas to execute calculations
  • 52. Tableau supports many functions for calculations
  • 53. The different types of functions inTableau • Numbers • Logical • Type conversion • String • Date • Aggregations onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
  • 54. • Install Tableau • Load data into tableau • Learn basic rules for creating visualizations and reporting • Understand basics of calculations and functions used in Tableau Recap: Session 2
  • 55. Overview of session-wise exercises Exercise Session 1 – Creating basic data visualizations and Introduction to Calculations Exercise Session 2 – Descriptive stats on H1B Visa Data + Geo Data analysis Exercise Session 3 – Mini project on Start-up investment analysis Exercise Session 4 – Data Joining and Blending + Retail Sales comparison Exercise Session 5 – Mini project - Customer segmentation & Predictive analytics using R Exercise Session 6 & 7 – Implementing LOD calculations + Retail sales data set Exercise Session 8 – Customer lifetime analysis + Retail Sales data set Exercise Session 9 – Tableau final project on Data Visualization challenge onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
  • 58. Where do we stand? • Understood Data types • Created visualizations • Created calculation fields • Used filters • Did basic calculations • Developed dashboards
  • 59. Learning goals from session 3 • Perform data joining and blending • Group data • Create static sets • Create dynamic sets and set parameters • Finally crunch data
  • 60. Agenda Visual Analytics in Practice Revision of key concepts till session 2 Case study Introduction to data joins Introduction to data blends Tableau perspective of joins and blends
  • 61. Data joining & blending Why do we need to join or blend data? http://wikiclipart.com
  • 62. Exploring the idea of joining data Lets assume you work as an accountant at Starbucks
  • 63. Exploring the idea of joining data You need to calculate revenue data coming from all over world
  • 64. Data comes from various sources
  • 65. This data might be located in multiple data tables connected with a common feature Data comes from various sources
  • 66. Data joining & blending The challenge is how to connect data which allows analysis http://wikiclipart.com
  • 67. We can use certain common but unique features or variables or keys to join various but similar data sources
  • 68. How to connect data sources using Keys? So what can be a common Feature or key?
  • 69. Customer id Unique Product id Transaction id Region id For example, keys can be unique fields like
  • 70. These keys exist in the data tables coming from similar sources For example, keys can be unique fields like
  • 71. So how to join data using keys? These keys allow matching related data in different tables The process of matching is called data joining
  • 72. There exist 4 types of joins depending on your need
  • 73. Types of data Joins Inner Joins Left Joins Right Joins Outer Joins Image Source: Adapted from Packt Output of data join
  • 74. Types of data Joins Image Source: Adapted from Packt Inner Joins Left Joins Right Joins Outer Joins Output of data join
  • 75. Types of data Joins Image Source: Adapted from Packt Inner Joins Left Joins Right Joins Outer Joins Output of data join
  • 76. Types of data Joins Image Source: Adapted from Packt Inner Joins Left Joins Right Joins Outer Joins Output of data join
  • 77. Now lets look at a practical example using inner join
  • 78. Example of inner data Join using primary key Image Source: w3schools Orders Table Customers Table
  • 79. Here the key used is Customer ID to join the data tables
  • 80. Image Source: w3schools Output from Inner Join in implementation Example of inner data Join using primary key
  • 81. So in an inner join we extract only common values in both the tables
  • 82. For more details on other SQL joins please visit w3schools.com/sql/
  • 83. Data Blending Now when to blend data? source: Phobia wiki
  • 84. Blending Imagine revenues from India & USA Starbucks are calculated on Monthly and Quarterly basis
  • 85. So you perform blending to compare numbers To compare revenues at different aggregations Quarterly revenue Monthly revenue
  • 86. But the challenge is… To compare revenues at different aggregations Q1 Q2 Q3 Q4Monthly
  • 87. Blending allows to manually connect and calculate… Q1 Q2 Q3 Q4
  • 88. Blending allows to compare numbers Q1 Q2 Q3 Q4 $11,510,375 $11,510,375 $9,530,188 $9,530,188 $5,920,326 $5,920,326 $3,159,155 $3,159,155 Revenues In a fiscal year
  • 89. Data Joining occurs at (row level) blending occurs at (aggregate level) Joins and blends using Tableau
  • 90. Data Joining (row level) vs blending (aggregate level) Summarizing Joins Blends Row level connections Table level connections Tables share a primary key & have multiple connection points May not necessarily have common fields or common primary keys, with similar level of data aggregations Allow connecting data tables from similar sources Allow connecting and comparing data from disparate sources
  • 91. • Perform data joins and blending into tableau • Learn basic rules for data joins and blending • Understand basics of SQL joins • Understand the purpose of data blending Recap: Session 3
  • 92. An overview of the practical sessions Exercise Session 1 – Creating basic data visualizations and Introduction to Calculations Exercise Session 2 – Descriptive stats on H1B Visa Data + Geo Data analysis Exercise Session 3 – Mini project on Start-up investment analysis Exercise Session 4 – Data Joining and Blending + Retail Sales comparison Exercise Session 5 – Mini project - Customer segmentation & Predictive analytics using R Exercise Session 6 & 7 – Implementing LOD calculations + Retail sales data set Exercise Session 8 – Customer lifetime analysis + Retail Sales data set Exercise Session 9 – Tableau final project on Data Visualization challenge onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
  • 94. Where do we stand? • Understood Data types • Created visualizations • Created calculation fields • Used filters • Did calculations • Developed dashboards • Performed data joining and blending
  • 95. Learning goals in session 4 • To perform data analysis • To perform predictive analytics • Explore statistical functions in Tableau • Write R code using Tableau functions
  • 96. Agenda Visual Analytics in Practice Introduction to Data analytics Introduction to Predictive Analytics Execute R functions in Tableau Connect Tableau and R
  • 97. Descriptive & Predictive analytics process
  • 98. Tableau supports extracting data from various databases
  • 99. Role of Excel in data analysis But when it comes to cleaning data, Excel is a preferred program
  • 100. Steps in Data Cleansing using MS Excel • Backing up data • Labelling columns • Dealing with Duplicates • Filtering data • Using Quick Chart visualizations • Replacing text • Concatenate • Splitting data • Flash fill • Exploring V-lookup functions Source: http://www.datacleansing.net.au/
  • 101. 02 MS Excel functions used in Data Cleaning Data cleaning with excel TASK EXCEL FUNCTION S p e l l c h e c k a n d R e m o v e d u p l i c a t e s Excel Spell Check And Remove Duplicate Data Values Using Functions Located In Design And Review Ribbons R e m o v e s p a c e a n d n o n p r i n t i n g c h a r a c t e r s Clean(), Trim(), Substitute(), Code() F i x i n g n u m b e r a n d n u m b e r s i g n s Dollar(), Text(), Fixed(), Value() C h a n g i n g c a s e s o f t e x t Upper(), Lower(), Proper() M e r g i n g a n d s p l i t t i n g c o l u m n s Transpose() F i x i n g d a t e s a n d t i m e s Date(), Datevalue(), Time (), Timevalue() M a t c h i n g d a t a v a l u e s Vlookup(), Lookup(), Index(), Match() Reference: https://support.office.com/en-us/article/Top-ten-ways-to-clean-your-data-2844b620-677c-47a7-ac3e-c2e157d1db19
  • 102. Agenda Visual Analytics in Practice Introduction to Data analytics Introduction to Predictive Analytics Execute R functions in Tableau Connect Tableau and R
  • 103. The most commonly used Predictive models in Tableau
  • 104. Linear Regression Used in scenarios like Housing price prediction
  • 105. K-means clustering Used in scenarios like Identifying unique customer groups
  • 106. Predictive analytics using R allows us • Allow forecasting: future revenues • Allows deriving YES / NO answers: granting housing loans • Allows clustering similar data: segmenting customers • Understand unique and weird patterns: anomalies detection Some more applications of predictive analytics
  • 107. 13 Why integrate R for predictive analysis? Provides access to 10,000 analytics packages!
  • 108. Predictive analytics using R allows us • Increase Analytics power of Tableau • Visualize Big Data calculations using Tableau • Predict and Forecast business insights using Tableau • Utilize statistical models and visualize output using Tableau Why implement predictive analytics?
  • 109. Agenda Visual Analytics in Practice Introduction to Data analytics Introduction to Predictive Analytics Execute R functions in Tableau Connect Tableau and R
  • 110. 3 functions used for executing R programs in Tableau
  • 111. 13 Execute R Programs in Tableau SCRIPT_BOOL Returns a Boolean result i.e. the output is a YES / NO Accept or Reject a bank loan
  • 112. 13 Executing R functions in Tableau SCRIPT_INT Returns an Integer result i.e. any specific number say 23 Predict number of cars sold
  • 113. 13 Executing R functions in Tableau SCRIPT_REAL Returns a real result i.e. any specific number including a decimal number Predict average Housing Price in a location
  • 114. 13 How does sample R program code in Tableau look like?
  • 115. Agenda Visual Analytics in Practice Introduction to Data analytics Introduction to Predictive Analytics Execute R functions in Tableau Connect Tableau and R
  • 116. 13 • Install R programming tool • Install R studio open source edition • Install package: ‘Rserve ’ • Install Tableau Desktop Enterprise trial version • Connect R and Tableau using guidelines in the course documents Integrate R & Tableau in 4 steps +
  • 117. What groundwork is necessary to perform Analytics using Tableau
  • 118. • Learn descriptive statistics • Learn Inferential statistics • Learn basic R programming The groundwork needed
  • 119. The resources for learning R and business statistics
  • 120. Learn basic R programming Provides basic coding courses on R programming
  • 121. Learn business statistics from this book Provides detailed problems on Business statistics
  • 122. • Perform predictive analysis tableau • Learn about important predictive analytics models • Learn integrating Tableau and R • Learn fundamentals of R programming Recap: Session 4
  • 123. An overview of the practical sessions Exercise Session 1 – Creating basic data visualizations and Introduction to Calculations Exercise Session 2 – Descriptive stats on H1B Visa Data + Geo Data analysis Exercise Session 3 – Mini project on Start-up investment analysis Exercise Session 4 – Data Joining and Blending + Retail Sales comparison Exercise Session 5 – Mini project - Customer segmentation & Predictive analytics using R Exercise Session 6 & 7 – Implementing LOD calculations + Retail sales data set Exercise Session 8 – Customer lifetime analysis + Retail Sales data set Exercise Session 9 – Tableau final project on Data Visualization challenge onlinehelp.tableau.com/current/pro/desktop/en-us/functions.html
  • 124. Any queries ? Now or any time in future, please write to arun@upxacademy.com If it’s a technical issue please attach a screenshot of the problem description and inform the exact details

Notas do Editor

  1. Drop an email, we should recap what we have done in class one, before we start the next session. Where do we stand and Learning goals decks need to be included, in a couple of points
  2. All above steps are useful for bring together data from different sources
  3. Highlight Tableau desktop and public. Use a dotted box to highlight the products we use. Create a new logo for Tableau Desktop, Public and server version.
  4. Highlights about Tableau Public, free of cost
  5. Not free, allows connecting R and Python tools
  6. Public to left, and toward the right side of the table
  7. Place a cursor or arrow icon
  8. Title have to improved
  9. cursor
  10. Highlight dashboards
  11. Can me
  12. Find examples of bad graphs and include them
  13. Better titles
  14. Rename modules as exercise sessions. Move it to the first deck. Topic name + Retail sales comparison Curriculum has to be replicated in one slide
  15. All above steps are useful for bring together data from different sources
  16. Most commonly used join type is inner join. Allow extraction of only common data observations linked by the primary key
  17. A simple example with 2 tables and data would be helpful for people to make sense of these joins. https://www.w3schools.com/sql/sql_join.asp https://www.geeksforgeeks.org/sql-join-set-1-inner-left-right-and-full-joins/
  18. A simple example with 2 tables and data would be helpful for people to make sense of these joins. https://www.w3schools.com/sql/sql_join.asp https://www.geeksforgeeks.org/sql-join-set-1-inner-left-right-and-full-joins/
  19. A simple example with 2 tables and data would be helpful for people to make sense of these joins. https://www.w3schools.com/sql/sql_join.asp https://www.geeksforgeeks.org/sql-join-set-1-inner-left-right-and-full-joins/
  20. Example would make it more clear. A tabular representation of January sales for India and US
  21. Example would make it more clear. A tabular representation of January sales for India and US
  22. Example would make it more clear. A tabular representation of January sales for India and US
  23. Example would make it more clear. A tabular representation of January sales for India and US
  24. Avoid comparision
  25. Rename modules as exercise sessions. Move it to the first deck. Topic name + Retail sales comparision