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
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)
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
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?
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
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
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
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
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
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
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
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
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?
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
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
+
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
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
All above steps are useful for bring together data from different sources
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.
Highlights about Tableau Public, free of cost
Not free, allows connecting R and Python tools
Public to left, and toward the right side of the table
Place a cursor or arrow icon
Title have to improved
cursor
Highlight dashboards
Can me
Find examples of bad graphs and include them
Better titles
Rename modules as exercise sessions. Move it to the first deck.
Topic name + Retail sales comparison
Curriculum has to be replicated in one slide
All above steps are useful for bring together data from different sources
Most commonly used join type is inner join. Allow extraction of only common data observations linked by the primary key
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/
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/
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/
Example would make it more clear. A tabular representation of January sales for India and US
Example would make it more clear. A tabular representation of January sales for India and US
Example would make it more clear. A tabular representation of January sales for India and US
Example would make it more clear. A tabular representation of January sales for India and US
Avoid comparision
Rename modules as exercise sessions. Move it to the first deck.
Topic name + Retail sales comparision