The deck gives you an introduction to Tableau and how you can build powerful data visualizations with it.

1. CERTIFICATE BOOTCAMP
IN
TABLEAU AND DATA VISUALIZATION
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2. ETL, DATA WAREHOUSING & BUSINESS INTELLIGENCE
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3. WHAT IS TABLEAU AND ITS SCOPE IN DATA VISUALIZATION
• Tableau is a simple yet powerful tool that can be used to
understand and study data and derive meaningful insights out of
the data.
• It’s simple and easy to use with the drag and drop features.
• Tableau is very powerful tool that can be used to connect to large
or small datasets (oltp, olap, static files, nosql databases) to name a
few and perform on demand analysis of data.
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4. GARTNER MAGIC QUADRANT
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5. DESKTOP SERVER ONLINE
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TABLEAU OFFERINGS

6. PREP MOBILE PUBLIC
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TABLEAU OFFERINGS

7. CONNECTING TABLEAU TO DATA SOURCES
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8. PREPARING YOUR DATA FOR ANALYSIS
RELATIONSHIP
Relationship are the default intelligent tableau way that can be
used in most instances, including across tables with different levels
of detail. Relationships are flexible and are adaptable to the
structure of the analysis on a sheet by sheet basis.
Relationships cannot be formed between tables from data sources
published to tableau server or tableau online. Relationships also
can’t be formed based on calculated fields.
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9. PREPARING YOUR DATA FOR ANALYSIS
JOINS
Joins combine tables by adding more columns of data across
similar row structures. This can cause data loss or duplication if
tables are at different levels of detail, and joined data sources
must be fixed before analysis can begin.
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10. PREPARING YOUR DATA FOR ANALYSIS
BLENDS
If our data is located across multiple data sources we can use data
blending concept. Blends query each data source independently.
The results are aggregated to the appropriate level, then the results
are presented visually together in the view. Prior to version 2020.2,
data blending was often the best way to handle data sources at
different levels of detail. These can now be combined with
relationships. Blending is only encouraged when it is the best
method for your data or relationships are not available.
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11. DATA PROCESSING SCOPES IN TABLEAU
• EXTRACT FILTERS
• DATA SOURCE FILTERS
• CONTEXT FILTERS
• DIMENSION FILERS
• MEASURE FILTERS
• TABLE FILTERS
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12. EXTRACT FILTER
Extract filters are used to
filter the extracted data
from data source. This
filter is utilized only if the
user extracts the data from
data source.
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13. DATA SOURCE FILTER
A data source filter is used
to filter the data in data
source level. It can restrict
the records present in the
data set.
Data source filter works on
both live and extracts
connection.
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14. CONTEXT FILTER
An independent filter that can create a separate
dataset out of the original data set and compute
based on the selections made in the worksheet.
One or more categorical filter can be used as a
context filter. All other filters used in the
worksheet works based on the selection of
context filter.
Context filters are used to further filter based on
the context filter selected.
Use case: For retaining cities only based on state.
It can be added to the context filter.
Context filter can be used to improve overall
performance. If the data source is huge and the
requirement is to analyze based on a particular
area, context filter can be used to perform the
same.
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15. DIMENSION FILTER
When a dimension or a
categorical value is used to
filter the data in a worksheet, it
is called as Dimension filtering.
It is a non-aggregated filter
where a dimension, group, sets
and bin can be added. A
dimension filter can be applied
through the top or bottom
conditions, wildcard match and
formula.
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16. MEASURE FILTER
A measure filter can filter
the data based on the
values present in a
measure. The aggregated
measure values can be
used in measure filter to
modify the data.
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17. HOW SORTING WORKS IN TABLEAU
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• There are many ways to sort data in Tableau.
• Data can be sorted using single click options from an axis, header, or field label.
• Data can be sorted in Ascending, Descending or custom order.
• Let’s go back to the workbook to understand how sort works.

18. IMPORTANCE OF CHARTS
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• Charts are to display data and invite further exploration of a topic.
• Often a simple table won't adequately demonstrate important relationships or patterns between
data points but charts do.

19. BAR CHART
A bar plot shows
categorical data as
rectangular bars with
heights proportional to
the value they represent.
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20. LINE CHART
Line plot is a type of
chart that displays
information as a series of
data points connected
by straight line
segments. A line plot is
often the first plot of
choice to visualize any
time series data.
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21. AREA CHART
An area chart is really
similar to a line chart,
except that
the area between the x
axis and the line is filled
in with colour or shading.
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22. PIE CHART
A pie chart expresses a
part-to-whole
relationship in your data.
Imagine an actual pie.
Each slice represents one
component and all slices
added together equal
the whole.
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23. SCATTER PLOT
Scatter plot is a graph in
which the values of two
variables are plotted
along two axes. It is a
most basic type of plot
that helps you visualize
the relationship between
two variables.
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24. BUBBLE PLOT
A bubble chart is
a scatter plot in which a
third dimension of the
data is shown through
the size of markers.
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25. BOX PLOT
Boxplot is a chart that is
used to visualize how a
given data (variable) is
spread over quartiles. It
also help to understand
outliers in the dataset.
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