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Filters are used to limit the data displayed in a visualization. There are different types of filters in Tableau.
Types of filters include: dimension filters, measure filters, context filters, table calculation filters, and extract filters.
Dimension filters limit the data based on specific dimensions, such as date or region.
Measure filters limit the data based on specific measures, such as sales or profit.
Context filter...
Tableau supports various data types including string, integer, float, date, boolean, and geographic data.
String: text data
Integer: whole numbers
Float: decimal numbers
Date: date and time data
Boolean: true/false data
Geographic: latitude and longitude data
Examples: 'John Smith' (string), 25 (integer), 3.14 (float), 01/01/2021 (date), true (boolean), (latitude, longitude) (geographic)
In Tableau, discrete and continuous are two types of data that can be used to categorize and analyze information.
Discrete data consists of distinct, separate values that cannot be measured or divided further.
Examples of discrete data in Tableau include categories like product names, customer segments, or regions.
Continuous data represents measurements or values that can be divided infinitely and can take on any value w...
Tableau is more user-friendly and has better visualization capabilities than QlikView.
Tableau has a more intuitive drag-and-drop interface
Tableau has a wider range of visualization options
Tableau has better integration with other data sources
QlikView has better data modeling capabilities
QlikView has better performance with large datasets
The latest version of Tableau is 2021.3.
2021.3 was released on September 14, 2021.
New features include improved data modeling capabilities and enhanced analytics.
Previous versions include 2021.2, 2021.1, and 2020.4.
Data visualization is the graphical representation of data to help understand patterns, trends, and insights.
Data visualization uses charts, graphs, and maps to present data visually.
It helps in identifying patterns, correlations, and outliers in data.
Examples include bar charts, line graphs, scatter plots, and heat maps.
Aggregation is the process of combining data into a summary form, while disaggregation is the opposite, breaking down summary data into its individual components.
Aggregation involves grouping data and performing calculations on the grouped data.
Disaggregation involves breaking down summary data to reveal the individual data points.
Aggregation is useful for analyzing trends and patterns at a higher level.
Disaggregation ...
Context filters can slow down performance and limit interactivity.
Context filters create a temporary table that affects the performance of the workbook.
Using multiple context filters can further degrade performance.
Context filters limit the interactivity of other filters and actions.
Context filters may not work well with certain data sources or complex calculations.
Removing or modifying context filters requires re-comp
I applied via Naukri.com and was interviewed in Mar 2024. There was 1 interview round.
Pivot and Recursive CTE are advanced SQL techniques used for data transformation and hierarchical querying.
Pivot is used to convert rows into columns based on a specific column value.
Recursive CTE is used to query hierarchical data, such as organizational structures or bill of materials.
Example of Pivot: SELECT * FROM table_name PIVOT (SUM(value) FOR column_name IN (value1, value2, value3))
Example of Recursive CTE: WIT...
I applied via Naukri.com and was interviewed in Mar 2024. There was 1 interview round.
Pivot and Recursive CTE are advanced SQL techniques used for data transformation and hierarchical querying.
Pivot is used to convert rows into columns based on a specific column value.
Recursive CTE is used to query hierarchical data, such as organizational structures or bill of materials.
Example of Pivot: SELECT * FROM table_name PIVOT (SUM(value) FOR column_name IN (value1, value2, value3))
Example of Recursive CTE: WIT...
posted on 22 Jul 2023
I applied via Approached by Company and was interviewed in Jan 2023. There were 4 interview rounds.
Implementing features on Tableau Desktop for data visualization and analysis
Create interactive dashboards for easy data exploration
Use calculated fields to perform complex calculations
Utilize parameters for dynamic filtering of data
Incorporate custom shapes and images for enhanced visualizations
posted on 22 Jul 2023
I applied via Company Website and was interviewed in Jan 2023. There were 3 interview rounds.
Practical implementation of features on Tableau Desktop
Creating interactive dashboards
Using calculated fields for data manipulation
Utilizing parameters for dynamic filtering
Incorporating advanced analytics like forecasting
Implementing data blending for combining multiple data sources
I applied via Referral and was interviewed before Aug 2023. There were 2 interview rounds.
Layers in an application refer to the different components or levels of software architecture that work together to provide functionality.
Layers help to organize code and separate concerns, making it easier to maintain and scale the application.
Common layers in an application include presentation layer, business logic layer, and data access layer.
Each layer has a specific responsibility and interacts with other layers ...
Transition from technical to analytical role for broader career opportunities and interest in data-driven decision making.
Interest in leveraging technical skills for data analysis and problem-solving
Desire for broader career opportunities beyond traditional engineering roles
Passion for data-driven decision making and business strategy
Recognizing the growing demand for analytics professionals in various industries
posted on 15 May 2024
I applied via Naukri.com and was interviewed before May 2023. There were 3 interview rounds.
I was interviewed in Mar 2022.
Deep Learning Architecture has three layers: Input Layer, Hidden Layer, and Output Layer.
Input Layer receives input data and passes it to the Hidden Layer.
Hidden Layer processes the input data and passes it to the Output Layer.
Output Layer produces the final output based on the input data.
Each layer can have multiple nodes or neurons.
Examples of Deep Learning Architectures include Convolutional Neural Networks (CNNs), ...
SVM is a supervised machine learning algorithm used for classification and regression analysis. It finds the best hyperplane to separate data points.
SVM is based on the idea of finding a hyperplane that best divides a dataset into two classes.
It can be used for both classification and regression analysis.
Some of the popular kernels used in SVM are linear, polynomial, radial basis function (RBF), and sigmoid.
Linear kern...
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