Tableau Training & Certification

[vc_row full_width=”stretch_row” css=”.vc_custom_1559286923229{background-color: #f6f6f7 !important;}”][vc_column width=”1/2″][vc_tta_accordion color=”peacoc” active_section=”1″][vc_tta_section title=”Introduction to Data Visualization” tab_id=”1559286383409-ab730398-6c03″][vc_column_text]

Goal : Give a brief idea of data visualization and introduce Tableau 10

 

 
Objectives:
  • Identify the prerequisites, goal, objectives, methodology, material, and agenda for the course
  • Discuss the basic of Data Visualization
  • Get a brief idea about Tableau, establish connection with the dataset, perform Joins operation on the data set

Topics:

  • Data Visualization
  • Introducing Tableau 10.0
  • Establishing Connection
  • Joins and Union
  • Data Blending

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Visual Analytics” tab_id=”1559286522681-3bf94e12-e7b7″][vc_column_text]

Goal :  Learn to manage your dataset and analyze things visually with the help of Marks Card and “highlighting” feature.
Objectives:
  • Manage extracts and metadata (by creating hierarchy and folders)
  • Describe what is Visual Analytics, why to use it, and it’s various scopes
  • Explain aggregating and disaggregating data and how to implement data granularity using marks card on aggregated data
  • Describe what is highlighting, with the help of a use-case
  • Illustrate basic graphs including bar graph, line graph, pie chart, dual axis graph, and area graph with dual axis

Topics:

  • Managing Extracts
  • Managing Metadata
  • Visual Analytics
  • Data Granularity using Marks Card
  • Highlighting
  • Introduction to basic graphs

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Visual Analytics in depth I” tab_id=”1561382593569-b1979b66-b066″][vc_column_text]

Goal :  This module presents to you the granular content of Visual analytics, covering various techniques to perform sorting, filtering and grouping on the dataset.
Objectives:
  • Perform sorting techniques including quicksort, using measures, using header and legend, and sorting using pill with the help of a use case.
  • Master yourself into various filtering techniques such as Parametrized filtering, Quick Filter, Context Filter. Learn about various filtering option available with the help of use case and different scenarios.
  • Illustrate grouping using data-window, visual grouping, and Calculated Grouping (Static and Dynamic).
  • Illustrate some more graphical visualization including Heat Map, Circle Plot, Scatter Plot, and Tree Maps.

Topics:

  • Sorting.
  • Filtering.
  • Grouping
  • Graphical Visualization

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Visual Analytics in depth II” tab_id=”1561382595833-dd54d407-26c0″][vc_column_text]

Goal : This module presents to you Visual analytics in a more granular manner thereby letting you to dive deep into the content. It covers various advanced techniques of analyzing data including, forecasting, trend lines, reference lines, clustering, parametrized concepts, and creating sets.

Objectives:
  • Explain the basic concepts of sets followed by Creating sets using Marks Card, computation sets and combined sets
  • Describe the concepts of forecasting with the help of Forecasting problem as a use-case
  • Discuss the basic concept of clustering in Tableau
  • Add Trend lines and reference line to your visualization
  • Discuss about Parameter in depth using Sets and Filter

Topics:

  • Sets
  • Forecasting
  • Clustering
  • Trend Lines.
  • Reference Lines.
  • Parameters

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Dashboard and Stories” tab_id=”1561382597303-5168678c-55b9″][vc_column_text]

Goal : Learn all about Dashboards and Stories in Tableau.

Objectives:
  • Describe the basic concepts of Dashboard and its UI.
  • Build a dashboard by adding sheets and object into it
  • Modify the view and layout.
  • Edit your dashboard, how it should appear on phones or tablets.
  • Create an interactive dashboard using actions (filter, highlighting, URL).
  • Create stories for your Visualization and Dashboards.

Topics:

  • Introduction to Dashboard.
  • Creating a Dashboard Layout.
  • Designing Dashboard for Devices.
  • Dashboard Interaction – Using Action.
  • Introduction to Story Point.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is the difference between Traditional BI Tools and Tableau?” tab_id=”1584463430270-ba4f8924-bafd”][vc_column_text]

Traditional BI Tools Tableau
1. Architecture has hardware limitations. 1. Do not have dependencies.
2. Based on a complex set of technologies. 2. Based on Associative Search which makes it dynamic and fast
3. Do not support in-memory, multi-thread, multi-core computing. 3. Supports in memory when used with advanced technologies.
4. Has a predefined view of data. 4. Uses predictive analysis for various business operations.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is Tableau?” tab_id=”1584463431314-00da24bd-19ae”][vc_column_text]

  • Tableau is a business intelligence software.
  • It allows anyone to connect to the respective data.
  • Visualizes and creates interactive, shareable dashboards.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are the different Tableau Products and what is the latest version of Tableau?” tab_id=”1584463431909-b1d81110-da72″][vc_column_text]Here is the Tableau Product family.

(i)Tableau Desktop:

It is a self service business analytics and data visualization that anyone can use. It translates pictures of data into optimized queries. With tableau desktop, you can directly connect to data from your data warehouse for live upto date data analysis. You can also perform queries without writing a single line of code. Import all your data into Tableau’s data engine from multiple sources & integrate altogether by combining multiple views in a interactive dashboard.

(ii)Tableau Server:

It is more of an enterprise level Tableau software. You can publish dashboards with Tableau Desktop and share them throughout the organization with web-based Tableau server. It leverages fast databases through live connections.

(iii)Tableau Online:

This is a hosted version of Tableau server which helps makes business intelligence faster and easier than before. You can publish Tableau dashboards with Tableau Desktop and share them with colleagues.

(iv)Tableau Reader:

Tableau Training and Certification

  • Instructor-led Sessions
  • Real-life Case Studies
  • Assessments
  • Lifetime Access

It’s a free desktop application that enables you to open and view visualizations that are built in Tableau Desktop. You can filter, drill down data but you cannot edit or perform any kind of interactions.

(v)Tableau Public:

This is a free Tableau software which you can use to make visualizations with but you need to save your workbook or worksheets in the Tableau Server which can be viewed by anyone.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are the different datatypes in Tableau?” tab_id=”1584463447075-b14ce506-9e4b”][vc_column_text]Tableau supports the following data-types:

Tableau Data Types - Tableau Interview Questions - Edureka[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are Measures and Dimensions?” tab_id=”1584463448016-ce839991-896a”][vc_column_text]Measures are the numeric metrics or measurable quantities of the data, which can be analyzed by dimension table. Measures are stored in a table that contain foreign keys referring uniquely to the associated dimension tables. The table supports data storage at atomic level and thus, allows more number of records to be inserted at one time. For instance, a Sales table can have product key, customer key, promotion key, items sold, referring to a specific event.

Dimensions are the descriptive attribute values for multiple dimensions of each attribute, defining multiple characteristics. A dimension table ,having reference of a product key form the table, can consist of product name, product type, size, color, description, etc.[/vc_column_text][/vc_tta_section][vc_tta_section title=” What is the difference between .twb and .twbx extension?” tab_id=”1584463449042-244796d9-687e”][vc_column_text]

  • A .twb is an xml document which contains all the selections and layout made you have made in your Tableau workbook. It does not contain any data.
  • A .twbx is a ‘zipped’ archive containing a .twb and any external files such as extracts and background images.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are the different types of joins in Tableau?” tab_id=”1584463452890-4802be98-1332″][vc_column_text]The joins in Tableau are same as SQL joins. Take a look at the diagram below to understand it.Joins - Tableau Interview Questions - Edureka[/vc_column_text][/vc_tta_section][vc_tta_section title=” How many maximum tables can you join in Tableau?” tab_id=”1584463453739-d76eae66-5cad”][vc_column_text]You can join a maximum of 32 tables in Tableau.[/vc_column_text][/vc_tta_section][vc_tta_section title=” What are the different connections you can make with your dataset?” tab_id=”1584463454546-35ea4c05-9fcb”][vc_column_text]We can either connect live to our data set or extract data onto Tableau.

  • Live: Connecting live to a data set leverages its computational processing and storage. New queries will go to the database and will be reflected as new or updated within the data.
  • Extract: An extract will make a static snapshot of the data to be used by Tableau’s data engine. The snapshot of the data can be refreshed on a recurring schedule as a whole or incrementally append data. One way to set up these schedules is via the Tableau server.

The benefit of Tableau extract over live connection is that extract can be used anywhere without any connection and you can build your own visualization without connecting to database.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are shelves?” tab_id=”1584463455516-07fe81e7-bf5c”][vc_column_text]They are Named areas to the left and top of the view. You build views by placing fields onto the shelves. Some shelves are available only when you select certain mark types.

Shelves - Tableau Interview Questions - Edureka[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][vc_column width=”1/2″][vc_tta_accordion color=”peacoc” active_section=”1″][vc_tta_section title=”Mapping” tab_id=”1561382561432-7f73ef2a-cc67″][vc_column_text]

Goal: This module helps you in understanding mapping in detail, editing unrecognized and ambiguous location, and creating customized geocoding. Learn about polygon map and Web Mapping Service, and finally, add background images with self-generated coordinates.

Objectives:
  • Map the coordinates on the map, plot geographic data, and use a layered view to get the street view of the area.
  • Edit the ambiguous and unrecognized location plotted on the map.
  • Customize territory on a polygon map.
  • Connect to the WMS Server, use a WMS background map and saving it.
  • Add a background image and generate its coordinate and plot the points.

Topics:

  • Introduction to Maps.
  • Editing Unrecognized Locations.
  • Custom Geocoding.
  • Polygon Maps.
  • Web Mapping Services.
  • Background Images.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Calculation” tab_id=”1561382561455-654071d3-eb53″][vc_column_text]

Goal: This module will help you in creating basic calculations including string manipulation, basic arithmetic calculations, date math, logic statements and quick table calculations. Along with this, you will be also introduced to LOD expressions with the help of use cases.

 
Objectives:
  • Perform Calculations using various types of functions such as Number, String, Date, Logical, and Aggregate.
  • In addition, you will get to know about Quick Table Calculation.
  • Cover the following LOD expressions – Fixed, Included, and Excluded.

Topics:

  • Introduction to Calculation: Number Functions, String Functions, Date Functions, Logical Functions, Aggregate Functions.
  • Introduction to Table Calculation.
  • Introduction to LOD expression: Fixed LOD, Included LOD, Excluded LOD

Hands-On:

  • All Functions (Number, String, Date, Logical, Aggregate)
  • Table Calculation.
  • LOD expressions.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”LOD Problem Sets & Hands on” tab_id=”1561382611424-56181e07-6453″][vc_column_text]

Goal: This module will explain the scenarios where you can implement LOD expressions. This is showcased with the help of a set of problems.

Objectives:
  • Tackle complex scenarios by using LOD expressions.

Hands-On:

  • Use Case I – Count Customer by Order.
  • Use Case II – Profit per Business Day.
  • Use Case III – Comparative Sales.
  • Use Case IV – Profit Vs Target
  • Use Case V – Finding the second order date.
  • Use Case VI – Cohort Analysis

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Charts” tab_id=”1561382613753-7c9c9136-4ca1″][vc_column_text]Goal : Plot various types of Charts using Tableau 10 and have extensive hands-on on industry use cases.

Topics :
  • Box and Whisker’s Plots
  • Gantt Charts
  • Waterfall Charts
  • Pareto Charts
  • Control Charts
  • Funnel Charts

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Integrating Tableau with R and Hadoop” tab_id=”1561382614729-6b63842b-62b1″][vc_column_text]

Goal: This module introduces you to the concept of Big Data, Hadoop, and R. You discuss the integration between Tableau and R and finally publish your workbook on Tableau Server.

Objectives:
  • You will know the basics of Big Data, Hadoop, and R.
  • You will discuss the integration between Hadoop and R and will integrate R with Tableau.
  • In addition, you will get to publish your workbook on Tableau Server.

Topics:

  • Introduction to Big Data
  • Introduction to Hadoop
  • Introduction to R
  • Integration among R and Hadoop
  • Calculating measure using R
  • Integrating Tableau with R
  • Integrated Visualization using Tableau

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are sets?” tab_id=”1584463435930-276efdaf-b502″][vc_column_text]Sets are custom fields that define a subset of data based on some conditions. A set can be based on a computed condition, for example, a set may contain customers with sales over a certain threshold. Computed sets update as your data changes. Alternatively, a set can be based on specific data point in your view.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are groups?” tab_id=”1584463457360-2b274dc6-f322″][vc_column_text]A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.

[/vc_column_text][/vc_tta_section][vc_tta_section title=” What is a hierarchical field?” tab_id=”1584463458194-7269be2a-127b”][vc_column_text]A hierarchical field in tableau is used for drilling down data. It means viewing your data in a more granular level.[/vc_column_text][/vc_tta_section][vc_tta_section title=” What is Tableau Data Server?” tab_id=”1584463458762-b20ec9ed-5244″][vc_column_text]Tableau server acts a middle man between Tableau users and the data. Tableau Data Server allows you to upload and share data extracts, preserve database connections, as well as reuse calculations and field metadata. This means any changes you make to the data-set, calculated fields, parameters, aliases, or definitions, can be saved and shared with others, allowing for a secure, centrally managed and standardized dataset. Additionally, you can leverage your server’s resources to run queries on extracts without having to first transfer them to your local machine.
[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is Tableau Data Engine?” tab_id=”1584463459546-0f104aa5-ea6f”][vc_column_text]Tableau Data Engine is a really cool feature in Tableau. Its an analytical database designed to achieve instant query response, predictive performance, integrate seamlessly into existing data infrastructure and is not limited to load entire data sets into memory.

If you work with a large amount of data, it does takes some time to import, create indexes and sort data but after that everything speeds up. Tableau Data Engine is not really in-memory technology. The data is stored in disk after it is imported and the RAM is hardly utilized.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What are the different filters in Tableau and how are they different from each other?” tab_id=”1584463460249-0806f690-f7cf”][vc_column_text]In Tableau, filters are used to restrict the data from database.

The different filters in Tableau are: Quick , Context and Normal/Traditional filter are:

  • Normal Filter is used to restrict the data from database based on selected dimension or measure. A Traditional Filter can be created by simply dragging a field onto the ‘Filters’ shelf.
  • Quick filter is used to view the filtering options and filter each worksheet on a dashboard while changing the values dynamically (within the range defined) during the run time.
  • Context Filter is used to filter the data that is transferred to each individual worksheet. When a worksheet queries the data source, it creates a temporary, flat table that is uses to compute the chart. This temporary table includes all values that are not filtered out by either the Custom SQL or the Context Filter.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”How to create a calculated field in Tableau?” tab_id=”1584463460850-2bc8db4c-8952″][vc_column_text]

  • Click the drop down to the right of Dimensions on the Data pane and select “Create > Calculated Field” to open the calculation editor.
  • Name the new field and create a formula.

Take a look at the example below:

Calculated Field - Tableau Interview Questions - Edureka[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is a dual axis?” tab_id=”1584463461547-6f5904b3-3e25″][vc_column_text]Dual Axis is an excellent phenomenon supported by Tableau that helps users view two scales of two measures in the same graph. Many websites like Indeed.com and other make use of dual axis to show the comparison between two measures and their growth rate in a septic set of years. Dual axes let you compare multiple measures at once, having two independent axes layered on top of one another. This is how it looks like:

Dual Axis Graph - Tableau Interview Questions - Edureka[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is the difference between a tree map and heat map?” tab_id=”1584463463121-f484899d-ef72″][vc_column_text]A heat map can be used for comparing categories with color and size. With heat maps, you can compare two different measures together.

Heat Map - Tableau Interview Questions - EdurekaA tree map also does the same except it is considered a very powerful visualization as it can be used for illustrating hierarchical data and part-to-whole relationships.

Tree Map - Tableau Interview Questions - Edureka[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is disaggregation and aggregation of data?” tab_id=”1584463463906-b8a01e4b-f695″][vc_column_text]The process of viewing numeric values or measures at higher and more summarized levels of the data is called aggregation. When you place a measure on a shelf, Tableau automatically aggregates the data, usually by summing it. You can easily determine the aggregation applied to a field because the function always appears in front of the field’s name when it is placed on a shelf. For example, Sales becomes SUM(Sales).  You can aggregate measures using Tableau only for relational data sources. Multidimensional data sources contain aggregated data only. In Tableau, multidimensional data sources are supported only in Windows.

According to Tableau, Disaggregating your data allows you to view every row of the data source which can be useful when you are analyzing measures that you may want to use both independently and dependently in the view. For example, you may be analyzing the results from a product satisfaction survey with the Age of participants along one axis. You can aggregate the Age field to determine the average age of participants or disaggregate the data to determine what age participants were most satisfied with the product.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][/vc_row]

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