Tableau’s predictive modeling feature allows users to leverage powerful statistical models to build and update predictive models efficiently while giving them the flexibility to select their predictors, collaborate on the model results within other table calculations, and comprehend and examine a large volume of data. Go through this presentation to discover how Tableau’s predictive modeling feature allows users to leverage powerful statistical models to build and update predictive models efficiently.

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3 Ways Tableau Improves
Predictive Analytics
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2. 2
1. Segmentation and Cohort Analysis
• To validate a hypothesis, data experts first ask a series of
questions and create relevant data segments for detailed
analytics.
• Tableau offers rich features that enable users to perform
iterative analysis and comparison of pertinent data
segments.
• For example, in a hospital, data analysts can split patient
volume by departments and then analyze the data in
segments to validate their hypothesis about each
department’s mortality rate.
2. Scenario and What-If Analysis
3. Out-of-the-Box Stats and Predictive Analytic
Technologies
• With Tableau, it is possible to perform predictive analytics
without much effort, so it is easier to explore multiple
scenarios quickly.
• Tableau’s Table Calculations feature allows users to choose
from a standard set of time-series operations, such as
moving averages, year-over-year calculations, and running
totals.
• It also allows users to write custom computations. Tableau
also enables users to leverage several native modeling
capabilities, including trending and forecasting, by simply
using drag-and-drop functionality.
• When performing what-if analysis, Tableau allows users to
easily change the parameters to observe its impact on the
calculation.
• As the feature is effortless to use, even non-technical users
can experiment with multiple inputs and explore the possible
outputs from complex models.

3. Summary
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• Tableau’s flagship product, Tableau Desktop, is a great tool that assists
teams with self-service data visualization and analytics.
• While Tableau is known for its flexible front end and intuitive visual
interface, many data scientists now use Tableau for advanced analytics and
predictive modeling.
• Tableau also integrates directly with external services such as directly with
Python, R, and MATLAB.
• It allows users to leverage other predictive services such as RServe and
TabPy, which facilitates collaboration from all possible channels.

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