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Watch Why You Should Try Minitab’s Predictive Analytics

Today Minitab's new Predictive Analytics capabilities make it simple for you to get instant insights from data, tackle complex problems and create smart, yet easily explainable visualisations.

Watch this video to discover why our predictive methods are easy to understand, visualize and execute - whether you are just starting your analytics journey or looking to up your game.

Minitab is a world leader in easy-to-use solutions analytics and best-in-class statistical analysis and process improvement tools.

Learn more about our Predictive Analytics suite of TreeNet® (Gradient Boosting), Random Forests® and CART®.

 

Receive FREE Access To The Event Presentation:
Tree-Based Predictive Analytics: More Powerful Than You Might Think

Watch Minitab's Predictive Analytics World presentation. Request your copy of the recording here! 

Find out why tree-based algorithms are the popular choice for leaders who want to empower every worker with predictive analytics. Gillian Groom, Regional Customer Success Manager, will show you the impact 'no code' tree-based predictive analytics will have in your organisation, to reach its goals faster.

Request your copy of the recording today >

 

Speak to us for a free Predictive Analytics demo

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Explore More About Minitab Predictive Analytics
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Feature Engineering

Download our our latest whitepaper for tips on processing and preparing your data for use in predictive analytics and machine learning models. 
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The Minitab Blog

Access best practices, success stories, real-life examples, and how-to advice as you enter the world of machine learning and predictive analytics. 
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On-Demand Webinar

This webinar showcases the Minitab Predictive Analytics Module and highlights how it can enhance your analytic expertise with the power of predictive.
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Minitab Case Study

Read how one bank cut costs and increased customer engagement using the power of predictive analytics to generate insights for cost-saving decisions.

Looking to upskill your team in Predictive Analytics?

Minitab teaches live instructor-led training courses worldwide.

Fundamentals of Analytics Course

Description
In this foundational course, you will learn to minimize the time required for data analysis by using Minitab to import data, develop sound statistical approaches to exploring data, create and interpret compelling visualizations, and export results. Automate your Minitab analysis with minimal user input to save time. Analyze a variety of real-world data sets to learn how to align your applications with the right analytics tool and interpret the statistical output. Learn the fundamentals of important statistical concepts such as hypothesis testing and confidence intervals.

This course places a strong emphasis on making sound decisions based upon the practical application of statistical techniques commonly used in business, manufacturing, and transactional processes.

Learn More >

Regression Modeling and Forecasting Course

Description
Continue to build on the fundamental statistical analysis concepts taught in the Fundamentals of Analytics course by learning to explore and describe relationships between variables with statistical modeling tools. Discover and describe features in data related to the effect and impact of time, and how to forecast future behavior.

Learn how to find and quantify the effect that input variables have on the probability of a critical event occurring. Hands-on examples illuminate how modeling tools help reveal key inputs and sources of variation in your data.

Machine Learning

Description

Expand your analytics by analyzing data from real world problems experienced in many industries to explore and describe relationships between variables. Learn to use supervised machine learning techniques such as CART® to analyze patterns found in historical data to gain better insights, identify potential risks, seek out improvement opportunities, and make predictions about the future.

Use unsupervised machine learning tools such as Clustering to detect natural partitions in the data and group observations or variables into homogenous sets. Reduce the dimensionality of data by transforming the original data into a set of uncorrelated variables.

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