tableau course content

tableau course content

Tableau Introduction

1. Introduction

2. What is Business Intelligence?

3. Why Business Intelligence?

4. Name different BI tools

5. Difference between Traditional and Visualization BI tools

6. Importance of Data Visualization over enterprise reporting

7. Why Tableau?

8. Tableau Architecture

9. Tableau Products

10. Q & A

Tableau Desktop Course Contents

1. Getting Started

1.1. The Tableau Environment

1.2. Learning to Use Tableau

2. Connect to Data

2.1. Basic Connection

2.2. Clipboard Data Sources

2.3. Working with Multiple Connections

2.4. Joining Tables

2.5. Extracting Data

2.6. Understanding Data Fields

3. Building Data Views

3.1. Parts of the View

3.2. Building Views manually

3.3. Building Views Automatically

3.4. Build-It- Yourself Exercises

4. Do More with Views

4.1. Filtering

4.2. Formatting

4.3. Sorting

4.4. Groups

4.5. Sets

4.6. Aggregations

4.7. Dates and Times

4.8. Annotations and Mark Labels

4.9. Using Multiple Measures

4.10. Missing Values

4.11. Creating Bins

5. Dashboards and Stories

5.1. Creating Dashboards

5.2. Organizing Dashboards

5.3. Understanding Dashboards and

Worksheets

5.4. Creating Story

5.5. Format, Update and Present a Story

6. Advanced Analysis

6.1. Actions

6.2. Calculations

6.3. Forecasting

6.4. Totals

6.5. Parameters

6.6. Background Images

6.7. Background Maps

6.8. Trend Lines and Statistics

7. Publish and Share

7.1. Publishing to Tableau Server

7.2. Saving and Exporting

7.3. Printing

Tableau Server Course Contents

1. Tableau Server Overview

2. Creating Users

3. Creating Groups and assign users to groups

4. Publishing a Workbook to server (Workbooks connected to different data sources like Extract, Live etc.)

5. Publishing Data Source to server

6. Creating User Filters

7. Scheduling Workbooks with Extract as Data source

8. Set Permissions for Workbooks and Views

9. Set Permissions for a Data Source

10. Set Permissions for a Project

11. Check User Permissions

12. Change Owner for Workbooks

13. Change Owner for a Project

14. Others

15. Q&A


Tableau Real-Time Scenarios

1. Real – Time Scenarios Class – I

2. Real – Time Scenarios Class –II

3. Presenting Real Time Environment

4. Explain SDLC (Software Development Lifecycle)

5. Q & A

Sets

Sets in Tableau

Sets:-

Sets are custom fields you create that are based on existing dimensions, and that filter data using one or more criteria. You can create a set from any existing dimension. When you create a set for continuous dates associated with a relational data source , the set will be based on discrete values rather than a continuous range of values

The three main uses of a set are:

  1)Create a subset of the data: –
        Select one or more dimension members that are of interest to you
  2)Create unique encodings: –
        Combine dimension members to create unique encodings
  3)Save filters for later use: – 
        Once you have created a filter, you can save the filter as a set and use it in all of the worksheets in a workbook. This saves you from having to recreate the filter every time you want to use it.

Tableau displays sets in the Sets area of the Data window and labels them with the 
   icon. 

Sorting

Sorting in Tableau

Sorting:-

In Tableau, sorting a data view means arranging dimension members in a specified order. Tableau supports computed sorting and manual sorting.
• Computed Sorting
• Manual Sorting

Computed Sorting:-

Sorting dimensions in a computed manner follows these rules:
• You can sort any discrete field after it has been placed on a shelf (except the Filters shelf).
• Each dimension that appears on a worksheet can be sorted independently of any other dimension.
• The shelf location of the dimension determines the component of the data view that’s sorted. For example, if the dimension resides on the Columns shelf, the columns of the data view are sorted for that field. If the dimension resides on the Color shelf, the color encodings are sorted.
• Sorts are computed based on the values of the filters and sets in the view. Refer to Groups for more information.
• Sorted fields are identified with bold names.

Continuous fields are automatically sorted from lowest number to highest number (as indicated by the axes) and you cannot manually change the sort. However, you can reverse the order of an axis using field specific formatting.

Manual Sorting:-

• Manual sorting allows you to rearrange the order of dimension members in the table by dragging them in an ad-hoc fashion, giving precise control over how items appear next to one another in tables and in legends. It also gives you control over the order in which data is drawn on the screen. This control is useful when comparing specific pieces of data or interpreting overlapping data. Manual sorts can only be applied to discrete fields including a discrete measure.
• There are two ways to manually sort the data in a view. You can either select items in the view and use the Sort toolbar buttons or you can drag and drop headers in the view.

Tableau Data Extracts

Tableau Data Extracts

Extracting Data:-

• Extracts are saved subsets of a data source that you can use to improve performance, upgrade your data to allow for more advanced capabilities, and analyze offline. 
• You can create an extract by defining filters and limits that include the data you want in the extract.
• After you create an extract you can refresh it with data from the original data source. You can either fully refresh the data, replacing all of the extract contents; or you can increment the extract; which only adds rows that are new since the last refresh

Extracts can:

•  Improve performance. For file based data sources such as Excel or Access, a full extract      takes advantage of the Tableau data engine. For large data sources, a filtered extract can    limit the load on the server when you only need a subset of data.
•  Add functionality to file based data sources, such as the ability to compute Count Distinct.
•  Provide offline access to your data. If you are traveling and need to access your data  offline, you can extract the relevant data to a local data source.

DUAL AXES

DUAL AXES IN TABLEAU

Dual Axes:-

• You can compare multiple measures using dual axes, which are two independent axes that are layered on top of each other. 
• Dual axes are useful when you have two measures that have different scales. 
• To add the measure as dual axis drag the field to the right side of the view and drop it when you see a black dashed line. 
• You can also select Dual Axis on the field menu for the measure.

The result is a dual axis view where the Profit axis corresponds to the purple line and the

Shipping Cost axis corresponds to the brown line.

NOTE:-

• You can add up to four layered axes: two on the Columns shelf and two on the Rows shelf.
• When you are using dual axes you can align the two axes up by right-clicking the dual axis and selecting Synchronize Axis.

Blended Axes

Blended Axes in Tableau

Blended Axes:-

All the Measures are combinedly analyzed in Single Axes.

• Measures can share a single axis so that all the marks are shown in a single pane. 
• Instead of adding rows and columns to the view, when you blend measures there is a single row or column and all of the values for each measure is shown along one continuous axis. 
• For example, the view below shows quarterly sales and profit on a shared axis.

• To blend multiple measures, simply drag one measure or axis and drop it onto an existing axis.

• Blending measures uses the Measure Names and Measure Values fields, which are generated fields that contain all of the measure names in your data source and all of the  measure values
• The shared axis is created using the Measure Values field. The Measure Names field is added to the Color shelf so that a line is drawn for each measure. Finally, theMeasure Names field is filtered to only include the measures you want to blend.

Note:-

• Blending axes is most appropriate when comparing measures that have a similar scale and units.
•  If the scales of the two measures are drastically different, the trends may be distorted.

Individual Axes

Individual Axes in Tableau

Individual Axes:-

In Individual Axes we always  analyze the data by placing 1 measure in each Axes

• Add individual axes for each measure by dragging measures to the Rows and Columns shelves.
• Each measure on the Rows shelf adds an additional axis to the rows of the table.
• Each measure on the Columns shelf adds an additional axis to the columns of the table. 
• For example, the view below shows quarterly sales and profit. The Sales and Profit axes are

           individual rows in the table and have independent scales.

USING MULTIPLE MEASURES IN TABLEAU

Working with Multiple Measures in Tableau

Using Multiple Measures:-

• There are lots of different ways to compare multiple measures in a single view. 
• For example,you can create individual axes for each measure or you can blend the two measures to share an axis and finally, you can add dual axes where there are two independent axes layered in the same pane.
• In any of these cases you can customize the marks for each axis to use multiple mark types and add different levels of detail. Views that have customized marks are called combination charts.

          We can use Multiple measures in 3 ways.

           1.Individual Axes

           2.Blended Axes

           3.Dual Axes

Changing the Repository Location

Changing the Repository Location

::Changing the Repository Location:-::

You can specify a new location for the Tableau repository if you are not using the default

location in your Documents folder. For instance, if you are required to have your data on a

network server instead of on your local machine, you can point Tableau at the remote

repository.

1. Select File > Repository Location.

2. Select a new folder that will act as the new repository location in the Select a Repository

dialog box.

3. Restart Tableau so that it uses the new repository.

Note:-

Changing the repository location does not move the files contained in the original repository.

Instead, Tableau creates a new repository where you can store your files.

Organizing Sheets

Organizing Sheets

::Organizing Sheets:-::
There are three ways to navigate and view the sheets in a workbook: 
1)Tabs at the bottom of the workbook
2)Filmstrip
3)Sheet sorter. 

1)Sheet Tabs:-

• Each sheet, whether worksheet or dashboard, is represented as a tab along the bottom of the workbook. 
• Dashboards are indicated with a Dashboard icon next to the sheet name. Simply select the tab for the sheet you want to open.

• In the bottom right corner of the application window, there are several controls that you can use to advance through each sheet or quickly jump to the first or last sheet in the workbook. 
• These controls are only available when there are too many sheet tabs to show across the bottom of the application window. You can also navigate between sheets using the Window menu or move through the multiple worksheets by pressing Ctrl + F6 on your keyboard

• To make it easier to identify and group sheets, you can assign each sheet tab a color. Rightclick the tab and select Color. You can select from seven different colors. Selecting None resets the color back to the default.

2)FILMSTRIP:-

• Similar to the sheet tabs, the filmstrip displays along the bottom of the workbook. However,instead of sheet names, the filmstrip shows a thumbnail image of each sheet. 
• The filmstrip is useful when you are using Tableau to present your analysis and works well when you are working in Presentation mode

3.Sheet Sorter:-

• The sheet sorter shows all sheets in a workbook as thumbnail images on a single page and is similar to the slide sorter in Microsoft Power Point. 
• The sheet sorter is useful when you have a large number of sheets in a workbook. 
• Open the sheet sorter by clicking the sheet sorter tab in the upper right corner of the workbook.

WORKSPACE COMPONENTS

WORKSPACE COMPONENTS

::Data Window:-::

• Data fields appear on the left side of the workspace in the Data window. 
• You can hide and show the Data window by clicking the minimize button in the upper right corner of the Data window. 
• The Data window collapses to the bottom of the workbook. Click the minimize button again to show the Data window.

Tableau Data Extract API

::Tableau Data Extract API::

Use the Tableau Data Extract API to connect to data that is not a supported data source. With
the Tableau Data Extract API, you create a program that accesses and processes your data.
You then use that program to create a Tableau Data Extract (TDE) file.
The Data Extract API is available for developers on Windows platforms. Go to http://www-
.tableausoftware.com/data-extract-api, and choose the appropriate version for your platform
and programming language:

•    Data Extract API – Python – 32-bit
•    Data Extract API – Python – 64-bit
•    Data Extract API - C/C++/Java – 32-bit
•    Data Extract API - C/C++/Java - 64bit

::Notes for Developers::

The Data Extract API includes a sample program, makeorder, coded in each supported language
to demonstrate a typical usage scenario: creating an extract containing product orders.
The application creates the extract order.tde with several columns of different types. The general
flow of the sample programs is:
1. Open an Extract object to create a new file.
2. Define the extract’s schema using a TableDefinition.
3. Add the Extract table.
4. Insert rows.
5. Close all objects.

It is important to free memory by closing all objects, and it is particularly critical to ensure
Extract objects are cleaned up properly, particularly in non-native execution environments. See
the note sections below for language-specific details.

String columns in a Data Extract can be 8- or 16-bit and can be sorted according to many available
collations. By default, strings are sorted according to their binary representation, though
this can be changed on a per-table or per-column basis.

::Python Notes::

Objects in the Data Extract API are automatically closed by _del_ when necessary. While garbage collection handles the vast majority of concerns related to releasing resources, it is important to note that the virtual machine provides no guarantee that any particular object will ever be freed. While most objects are merely memory, Extract objects represent physical files
created when close is invoked. Therefore, it is not safe to rely on garbage collection to close
Extract objects. We recommend using with statements to ensure Extract instances are
cleaned up. Alternatively, you can explicitly call close

::Java Notes::

Data Extract API objects are automatically closed by finalize() as necessary. The Java
Virtual Machine does not guarantee that any particular object is ever garbage collected. While
most objects are merely memory that can be safely reclaimed by the operating system at JVM
shutdown, Extract objects represent physical files that are created when close() is invoked.
Therefore, it is important to invoke Java Notes Data Extract API objects are automatically closed by finalize()as necessary. The Java Virtual Machine does not guarantee that any particular object is ever garbage collected. While most objects are merely memory that can be safely reclaimed by the operating system at JVM shutdown, Extract objects represent physical files that are created when close() is invoked.Therefore, it is important to invoke Extract.close() for all Extract instances. We recommend using the try-with-resources construct introduced in Java 7. For earlier versions of Java, you must call Java Notes

::C++ Notes::

Data Extract API objects should be managed according to standard memory management
best practices, such as using stack variables or smart pointers. As in other languages, all
objects have a Close() method to free internal resources. Close() is invoked by the
destructor when necessary. However, it is important to note that Extract::Close() may
throw an exception, so it is safer to call it explicitly, rather than allowing an exception to potentially escape the destructor.

::C Notes::

Objects in the Data Extract C API are managed through opaque TAB_HANDLEs. Every
created object must be closed. It is advisable to free objects in the reverse order of creation. explicitly.
.

EXTRACTING DATA

::Extracting Data::

Extracts are saved subsets of a data source that you can use to improve performance, upgradeyour data to allow for more advanced capabilities, and analyze offline. You can create an extract by defining filters and limits that include the data you want in the extract. After you create an extract you can refresh it with data from the original data source. You can either fully refresh the data, replacing all of the extract contents; or you can increment the extract; which only adds rows that are new since the last refresh.

Extracts can:

• l Improve performance. For file based data sources such as Excel or Access, a full extract takes advantage of the Tableau data engine. For large data sources, a filtered extract can limit the load on the server when you only need a subset of data.

• l Add functionality to file based data sources, such as the ability to compute Count Distinct.
• l Provide offline access to your data. If you are traveling and need to access your data offline, you can extract the relevant data to a local data source.

DATA BLENDING

DATA BLENDING:-

• Data blending is when you blend data from multiple data sources on a single worksheet. 
• The data is joined on common dimensions. Data Blending does not create row level joins and is not a way to add new dimensions or rows to your data. Instead, data blending should be used when you have related data in multiple data sources that you want to analyze together in a single view. For example, you may have Sales data collected in an Oracle database and Sales Goal data in an Excel spreadsheet.
• To compare actual sales to target sales, you can blend the data based on common dimensions to get access to the Sales Goal measure.
• To integrate data, you must first define common dimensions between the primary and secondary data sources. For example, when blending Actual and Target sales data, the two data sources may have a Date field in common. The Date field must be specified as a linking field.
•  If the two dimensions don’t have the same name, you can define a custom relationship that creates the correct mapping between fields.
• For each data source that is used on the sheet, a query is sent to the database and the results are processed. Then all the results are left joined on the common dimensions.
•  The join is done on the member aliases of the common dimensions so if the underlying values aren’t an exact match, you can fix it up in Tableau.
• In general, a good test to see whether data can be integrated smoothly is to drag the dimensions from the primary data source into a text table on one sheet. Then on another sheet, drag the same fields from the secondary data source into a text table. 
• If the two tables match up then the data is most likely going to blend correctly

Working with Multiple Connections

Working with Multiple Connections:-

• A workbook can contain multiple connections to multiple data sources. 
• Each connection is listed at the top of the Data window.
• Each worksheet has a primary connection and can optionally have several secondary connections using data blending. 
• The primary connection and the secondary connections are linked by specified relationships.
• Adding a secondary connection to a sheet can be useful when you have data in multiple data sources that you want to integrate into a single analysis.

Clipboard Connections

Clipboard Data Sources:-

• Sometimes you want to pull in data from an outside source for some quick analysis. Rather than create a whole data source and then connect in Tableau, you can copy and paste the data directly into the application. 
• Tableau automatically creates a data source that you can begin analyzing. When you save the workbook, the data source is saved as a tab delimited text file into your Tableau Repository.
• You can copy and paste data from a variety of office applications including Microsoft Excel and Word. 
• You can also copy and paste html tables from webpages. 
• Tables that are copied as comma separated values or tab delimited can be pasted into Tableau. Please be aware that not all applications use these formats when copying.

Creating Connections

Creating Connections

Creating Connections:-

To build views of your data, you must first connect Tableau to a data source.
You can connect to any supported data source with the Connect to Data dialog box.
1. Select Data > Connect to Data or press Ctrl + D on your keyboard. You can also select the Connect to Data option on the start page.

2. On the Connect to Data page, select the type of data you want to connect to. You can also select a saved data connection (TDS files) open a Tableau Server Data Source.

Tableau WorkFlow

::TABLEAU WORKFLOW::

While Tableau lets you analyze databases and spreadsheets like never before, you don’t need to know anything about databases to use Tableau. In fact, Tableau is designed to allow business people with no technical training to analyze their data efficiently.

Tableau is based on three simple concepts:

1. Connect2. Analyze3. Share

1. Connect :- 

Connect Tableau to any database that you want to analyze. Note that Tableau does not import the data. Instead it queries to the database directly.

2. Analyze :- 

Analyzing data means viewing it, filtering it, sorting it, performing calculations on it, reorganizing it, summarizing it, and so on.
Using Tableau you can do all of these things by simply arranging fields of your data source on a Tableau worksheet. When you drop a field on a worksheet, Tableau queries the data using standard drivers and query languages (like SQL and MDX) and presents a visual analysis of the data.

3. Share: -

You can share results with others either by sharing workbooks with other

Tableau Workspace

Tableau Workspace

• The Tableau workspace consists of menus, a toolbar, the Data window, cards that containshelves and legends, and one or more sheets. Sheets can be worksheets or dashboards.
• Worksheets contain shelves, which are where you drag data fields to build views. You canchange the default layout of the shelves and cards to suit your needs, including resizing, moving,and hiding them.
• Dashboards contain views, legends, and quick filters. When you first create a dashboard, theDashboard is empty and all of the worksheets in the workbook are shown in the Dashboardwindow.

                                                           Workspace in Tableau 8

Workspace in Tableau 9