Learn how to use Tableau to see and understand your business’s data better. Tableau is a key player in the business intelligence field.

TABLEAU Training @ 0091-9704077815
Power BI Guru E solutions provides best Power BI Training in Hyderabad with 100% Real time Live projects , Job Placements , Certification classroom,online,corporate training’s in Hyderabad.Self-service analytics in the cloud 
with Tableau Online Training.

Key Features

30 hours of Instructor-led training classes
Document API
Transform data for analysis and create effective data views
Mobile device management
JavaScript API improvements
ETL refresh
REST API improvements


TABLEAU Training

Tableau is a Business Intelligence tool for visually analyzing the data. Users can create and distribute an interactive and shareable dashboard, which depict the trends, variations, and density of the data in the form of graphs and charts. Tableau can connect to files, relational and Big Data sources to acquire and process data. The software allows data blending and real-time collaboration, which makes it very unique. It is used by businesses, academic researchers, and many government organizations for visual data analysis. It is also positioned as a leader Business Intelligence and Analytics Platform in Gartner Magic Quadrant.

What you will learn from our workshop:

  • 1. Tableau helps to see and understand the data With mission “We help people see and understand their data”, Tableau products are transforming the way people use data to solve the problems. Tableau makes analyzing data fast and easy, beautiful and useful. No wonder it has gained a growing interest among the business users, but also groups traditionally not using BI tools are taking it into use. You might have noticed that for example more and more journalists are using Tableau to publish data stories to the web. One of the biggest Finnish media houses Helsingin Sanomat is using Tableau to visualize the news related data.Tableau is revolutionizing the way data is being used. To access and further analyze the data doesn’t anymore require IT department participating. The data is accessible to all levels of organizations and individuals. Democratizing data allows people to think and act quickly, bringing transparency and agility to fact-based decision making.

Importance of Tableau in Data Analytics:

Tableau enables businesses to make decisions using the data visualization features available to business users of any background and Industry. It empowers businesses to keep up with the continuously evolving technology and outperform its competition through an innovative means of visualizing their data. There is not a single data source that Tableau fails to connect with. Let it be Data Warehouse, MS Excel or any web data, it establishes a connection with all of them. Basically, in any type of data analytics, Tableau provides an end to end insight by transforming data into visually engaging, interactive views in dashboards. With easy to use drag-and-drop interface one can come up with insights in few moments rather than months or years.


Even a person from a non-technical background can easily learn tableau. But it will be preferable if you have basic understanding of SQL. A basic knowledge of SQL would help in writing custom queries that won’t be possible through to drag and drop functionality to achieve complex data set building.

Course Content :

  • 1. Introduction to Tableau 2. Tableau Terminology, Field Types & Visual Cues
    2.1 Tableau Terminologies 2.2 Opening & Closing Tableau 2.3 Start Page 2.4 Data Source Page 2.5 Connecting to Database 2.6 Creating Table Joins & Different types of Tableau Joins 2.7 Different Types of File Types, File Extensions 2.8 Difference Between Extract & Live Connection 2.9 Data Extract Vs Data Source Filter 2.10 Tableau Union Vs Tableau Pivot 2.11 Tableau Workspace (Development Area) 2.12 View Sections 2.13 Data Types & Symbols 2.14 Data Roles (Dimensions Vs Measures) 2.15 Data Roles (Continuous Vs Discrete) 2.16 Changing Data Types & Data Roles 2.17 Using fields in Rows & Columns 2.18 Measure Names & Measure Values 2.19 Using Number of Records 3. Understanding Mark Type & different type of Mark Shelves
    3.1 Behaviour of Dimensions in different Mark Shelves 3.2 Behaviour of Measures in different Mark Shelves 3.3 Different Mark Types 3.4 Different Types of Colour Palette’s

    4. Building Views
    4.1 Understanding Rows & Columns 4.2 Transposing Text Tables 4.3 Adding Total, Sub-Total, Grand-Total to a Text Table 4.4 Normal Filter/ Quick Filter 4.5 Difference between Dimension Filters & Measure Filters 4.6 Understanding Date Fields & Filter options 4.7 Sorting of Data 4.8 Sets for Dimensions

    Ravi Kishore
    4.9 Bins for Measures 4.10 Groups 4.11 Hierarchy 4.12 Difference between Filters, Groups, Sets, Hierarchy 4.13 Aggregations 4.14 Types of Aggregations (Dimension Vs Measures) 4.15 Changing Aggregations Functions 5. KPI Chart
    5.1 Creating KPI Chart for Simple Text Table 5.2 Creating KPI Chart for YOY & MOM 5.3 Custom Shapes 5.4 Quick Table Calculations

    6. Geographical Map
    6.1 Basic Geographical Map 6.2 Advanced Geographical Map 6.3 Map box Integration 6.4 Custom Geocoding 6.5 Page Shelf

    7. Bar Charts
    7.1 Simple Bar Chart 7.2 Bar in Bar Chart 7.3 Using Shapes in Bar Charts

    8. Trend Charts
    8.1 Simple Trend Chart (Discrete Vs Continuous) 8.2 Combined Trend Chart 8.3 Bar Vs Line Chart 8.4 Using Shapes in Trend Chart 8.5 Representing Trend Chart with Lollipops 8.6 Sparkle Line Chart

    9. Pie Charts
    9.1 Simple Pie Chart 9.2 Pie Charts for Different Fields Within a Dimension 9.3 Donut Charts 9.4 Using Shapes Within a Donut Chart

    10. Other Chart Types
    10.1 Word Cloud Chart 10.2 Waffle Chart 10.3 Waterfall Chart 10.4 Funnel Chart 10.5 Heat Maps 10.6 Highlight Tables 10.7 Tree Map 10.8 Circle View

    Ravi Kishore
    10.9 Side—By-Side Circle View 10.10 Area Maps 10.11 Bin Charts/ Histograms 10.12 Box & Whisker Plot 10.13 Scatter Plot 10.14 Gantt Chart 10.15 Bullet Chart 10.16 Bubble Chart 10.17 Building X & Y Co-ordinate Charts

    11. Calculated Fields
    11.1 Simple Calculated Fields 11.2 Advanced Calculated Fields 11.3 Different Types of Functions in Calculated Fields 11.4 LOD’s

    12. Data Blending & Parameters
    12.1 Data Blending 12.2 Creating Simple Parameters 12.3 Advantages & Disadvantages of Parameters 12.4 Use cases of Parameters

    13. Dashboards
    13.1 Components of Dashboard 13.2 Understanding How to Place Worksheets in Containers 13.3 Action Filters & its Types 13.4 How to make a worksheet Global 13.5 Best Practices to Develop a Dashboard

    14. Tableau Server Introduction
    14.1 How to Publish a Dashboard in Tableau Server 14.2 How to Set Permission Levels for Users Accessing the Dashboard in Tableau Server

Upcoming Events

Power BI Srinivas24 May 197 AM25700 USDOnline TrainingEnquire
Power BI Srinivas22 May 197 PM IST25700 USDOnline TrainingEnquire
Power BISrinivas25th May 1910.30 PM IST25700 USDweekend Online TrainingEnquire

What are the differenyt sources that you worked on ?

SQL Server 
Tabular SSAS

What is the differeces b/w direct query/importing/live connection?

Import: we will load the data & craete a model in power Bi 
Direct query:we will load the structure of the tables & create a relation ships in power Bi file by keeping the data at database.we always access the data directly from source
Live connection: we will keep the data & model away from power Bi service,this can be reusable in mul

What is the difference b/w calculated column/measure?

calculated column/computed/derived column which is created based on existing column
with the help of operators & functions .
-->whenever we perform the data refresh automatically this column gets the data
-->It will store the data in the model
--->it will occupy the emory 
--->It is faster 
-->it is static
-->it is unique at table level
-->for text/date related data types we can use
-->numeric columns for intermediate calculations we can use it 

--->It is a formula stored in a model
--->Whenever we used a measure at that time 
it wil get calculated
--->It will not store any data,it will not occupy any memory 
-->It will impact the performance 
--->It is unique at model level
-->It is dynamic

What are the different types of filters?

Page level
Visual level
Report level