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Python TutorialBI and Visualization
4.5 Rating 50 Questions 30 mins read7 Readers

Data visualization is merely the method by which data is described via visual representation. For millions of years, people have used visuals to describe the world around them. Data visualization gives a universal and instant understanding through the use of the strong visual treatment scheme of our minds.
Technological developments have increased the significance of business intelligence by making data visualization more common and effective than ever before. Tableau leads the world in providing company people with all backgrounds and sectors with the data visualization process. Global businesses understand that the capability to efficiently visualize information leads to improvement. Data visualization facilitates the understanding of large and small data by the human mind and also facilitates the detection of patterns, trends, and outliers in groups. Good visualization of information should position meaning in complex datasets so your message is clear and concise. Some benefits are as follows:
A staple in Tableau interview questions, be prepared to answer this one.
Tableau is an innovative Tableau Software data visualization software. Tableau can readily connect to almost any information source, such as corporate information storage, Microsoft Excel or Web-based information. Tableau enables instantaneous understanding by transforming information into interactive dashboards called visually attractive visualizations. It takes only seconds or minutes rather than months or years, and it can be achieved with a drag and drop interface that is simple to use. Tableau is a business intelligence software that enables someone in just a couple of clicks to connect to data and then visualize and create shareable interactive dashboards with some other information. Every Excel user can learn it easily, but powerful enough to solve even the most complicated analytical problems. It only requires seconds to securely share your results with others. The result is BI software, which you can trust to give the people who need it answers. The figure below shows the Family offerings tableau:

Software for quick analysis and quick-fire business intelligence is provided by Tableau Software. Tableau Desktop is an application for data visualization that allows you to analyze practically any structured information and generate lovely interactive charts, dashboards, and accounts in minutes. You can connect nearly any data source from Tableau to data stores after a fast setup and show information in several graphic views. You will work more quickly than ever before, designed to be simple to use. Tableau Server provides browser-based visual analysis that can be used at a fraction of the cost of traditional software. With only a few clicks, you are automatically able to post or incorporate live, interactive charts, dashboards and reports with present information to the requirements of all of your company. It deploys in a few minutes and customers can generate thousands of accounts in your IT facilities, without IT services. Tableau Reader is a free viewing application that allows anyone to read and communicate with Tableau Desktop's packaged workbooks.
Although Tableau can analyze databases, you don't need to know anything about databases to use Tableau. You don't need Tableau. Indeed, Tableau is intended to enable entrepreneurs with no technical training to effectively analyze their information.
Tableau has three easy concepts:
This is a regular feature in Tableau developer interview questions, be ready to tackle it.
Tableau product families include the following: Tableau Desktop, Tableau Prep, Tableau Online, Tableau Server, Tableau Mobile, and Tableau Public.
There are different offerings from product family point of view for individuals, for organizations etc.
We can save and share data using a variety of different Tableau File Types. The difference between each file type relate to the amount and type of information stored in the file. Following are different file types:
Out of the above, tde and twbx files generally handle potentially large data.
Tableau supports below data types:
Data type can be changed for a data type either on the “Data source” page or in the “Data” pane in Tableau. For example, if current data type is defined as “text value” and this needs to be changed, then data type in “Data source” page will provide dropdown list options of other data types such as “Number (decimal)”, “Number (whole)”, “Data & Time”, “Date”, “String”, “Boolean”. By default, the data type would be defined as a “Text value”.
It is important to change the data type so that it reflects correctly while we generate an extract.
When Tableau reads from data sources such as files in Microsoft excel or csv (comma separated value) formats, then it assigns data type automatically for respective values such as text/strings, date field, date and time field, Boolean field and so on. Additionally, if we source data from a csv with some mixed-value column is mapped to a single data type field in Tableau, then Tableau may assign one of the data type. For example a mixed value could be a mixture of numbers and dates, and they can be mapped to a number data type or date data type.
Suggested approach is to handle mixed value columns in a way one can format empty cells within the data source so that data type of that field can be matched and also to create additional new field that does not contain mixed values so that it is of a particular data type.
Tableau support below aggregation types: sum, average, median, count, count distinct, standard deviation, variation, minimum, maximum, standard deviation of a population, variance of a population, attribute (ATTR), dimension.
Aggregation in Tableau can be performed on measures and dimensions both. It is more common in measures. When we create a measure in the view, aggregation is already applied to its values by Tableau. So “Revenue” will become “Sum(Revenue)”. Typically “Sum” is the default. Else other aggregations such as Average, Median, Count, Count(Distinct), Minimum, Maximum, Percentile, Std. Dev, Std. Dev(Pop.), Variance, Variance(Pop.) can also be selected from the list. These options are primarily for measures. For dimensions, we can have aggregation types as a minimum, maximum, count, count distinct, etc.
This is frequently asked in Tableau interview questions.
When we have multiple data sources and we want to integrate those to visualize in our Tableau dashboard, we use blending.
Each data source contains its set of dimensions and measures. We can not have specific set of dimensions and measures which will limit us to create groups, formulas etc across the data. Data sources can be of multiple RDBMS such as Oracle, SQL Server, DB2 etc. and other data sources such as spreadsheets etc.
Blending also avoids joining of duplicate data as it will be linked via a common dimension.
One can change the linking field (dimension or measure) or add more linking fields to include additional rows of data from the secondary data source in the blend, changing the aggregated values.
It's no surprise that this one pops up often in Tableau questions for interview.
Auto scrolling filters are not supported in Tableau Server, but they can be consumed in Tableau Desktop or Tableau Reader.
Step by step process of adding auto filters can be described below:
Selecting filter here adds filtering action to the set of data used.
Essentially auto filters are useful when some action is triggered in the source sheet, then that reflects in the target sheet where there is a correlation of data shown or linked.
When using “action”, by hovering filters automatically gets applied to target sheet if we have defined as “hover” option, and so on.
A must-know for anyone heading into Tableau interview, this is frequently asked in Tableau interview questions for experienced.
Any field placed on the filter shelf enables a filter for that dimension or field. The style of filter control is dependent on whether the field is continuous or discrete. If we want to expose a filter in the worksheet, we can right-click on any pill used anywhere in the workspace and then select the menu option Show Quick Filter.
Granular details can be displayed with the help of symbol maps. Proportional values can also be represented using these maps which will help show quantitative values for individual ids. So if we want to show sales data for last 5 years across the country, the same can be shown in the geo-view with proportionate size / magnitude related to each and every location accordingly.
Filled up at the other hand is more suitable for showing “ratio” data. Pie charts typically have that information to a relative whole. For example, we want to display percentage of sales for a product by geographic location. In that case, filled maps can be used to show percentage of sales for different categories individually and relative to total sales by country.
From Symbol Maps standpoint:
From Fill Maps standpoint:
A) Heat maps, Highlight Tables and Tree maps are all possible in Tableau
B) Heat maps use colour and size to compare up to two measures
C) Highlight tables can display one measure only using a colour gradient background to differentiate values
D) Tree maps effectively display larger dimension sets using colour and size to display one or more dimensions and up to two measures
All of the options above – a, b, c, d are correct. Tableau uses Heat maps, Highlight Tables and Tree maps to compare granular combinations of dimensions and measures.
Both of them are different and not similar.Reference lines in Tableau are used to show visual comparisons to benchmark certain figure, constant or values. These can be added at a constant or computed value on the axis. For example, we are showing sales of Firm1, Firm2 and Firm3; the reference line can be set as an “average sales figure” which could act as a benchmark.
Trend lines on the other hand can be used to represent pattern of the trends in the data. For the same above example of Sales of a Firm1, we can use different trend lines to represent if it is following a particular trend. These can be linear trend, logarithmic trend, exponential trend, polynomial trend, power trend etc.