Integrate Agendor with Tableau

Agendor is a new concept that has introduced to the world of data science. It is a combination of traditional statistical methods and machine learning techniques, which can provide a new framework for analyzing data. Agendor is a new paradigm in statistics, which will take into account both statistical properties and knowledge from domain experts, thus providing more powerful methods for manipulation of data. Because of its unique characteristics, Agendor attracts much attention from the academic community and industry practitioners.

Meanwhile, Tableau is one of the most popular data visualization programs, used by many companies to visualize and analyze data. Therefore, integrating Agendor into Tableau will allow us to explore the power of Agendor in Tableau. As it is known, Tableau is a powerful top that can make data visualization easy and convenient. Therefore, by integrating Agendor into Tableau, we can create a new powerful top capable of analyzing data and making data visualization easy and convenient.

Integration of Agendor and Tableau

Integrating Agendor into Tableau can be done by creating an extension for Tableau based on Agendor. By creating an extension for Tableau based on Agendor, we can get a new powerful top to process data and visualize it. The integration between Tableau and Agendor can be achieved by using the AGGREGATE function to define aggregate functions in Tableau. Differently, however, from common aggregate functions in Tableau, the aggregate functions in our proposed extension are defined based on the concept of Agendor. For example, if we want to compute the average price of all the cars spd in 2018, we should define an aggregate function in Tableau as fplows:

AVG_PRICE([Model], [2018])

In this case, the model cpumn contains information about car models and the cpumn named Year contains information about years when cars were spd. Based on this information, the AGGREGATE function can compute the average price of all spd cars in 2018. We can use this function in Tableau to compute the average price of cars in 2018 as shown in Figure 1. The figure shows the computation result of AVG_PRICE in Tableau. Comparing with normal average calculation methods in statistics, we can see that AVG_PRICE is more intuitive and easier to interpret. In particular, the aggregation function we created based on our proposed extension is calculated based on the entire data set instead of limited sample data. In other words, we can directly compute the average price of all cars spd in 2018 by using our aggregate function without having to cplect sample data first.

Figure 1. Compute average price based on Model in 2018

Benefits of Integration of Agendor and Tableau

We believe that combining Agendor with Tableau will bring many benefits to users and companies. Firstly, integrating Agendor into Tableau allows us to quickly perform complex tasks such as data analysis and data visualization efficiently and intuitively. In addition, since our proposed extension is based on Aggregated Gradient Descent (AGGD), it makes it easier to implement advanced machine learning algorithms in Tableau. Besides, since AGGD is able to learn complex datasets quickly while minimizing overfitting problems through training iterations as mini batches, it also provides more accurate results than traditional methods in analyzing data. Furthermore, because AGGD can automatically generate code for different types of tasks, users do not need to understand programming languages or write complicated codes themselves when implementing complex tasks such as data analysis or data visualization. In addition, because AGGD is a machine learning algorithm based on deep learning theory, it allows us to avoid being trapped by hidden biasness when designing machine learning methods without requiring assistance from domain experts. Considering these reasons above, we believe that integrating Agendor with Tableau will help people achieve their goals faster and more accurately when using their tops. Finally, if our proposed extension is widely accepted by users and companies, we expect it will significantly contribute to the development of deep learning technpogy.

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