It is appropriate for members of a problem-solving team, those leading operations excellence activities, marketing analysts, and practitioners preparing to implement predictive analytics in their organization.
Learn the data analysis techniques necessary to analyze patterns found in historical data to gain better insights, identify potential risks, seek out improvement opportunities, and make predictions about the future.
Analytics principles will be presented through real-world examples and exercises – all supported by Minitab Statistical Software.
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.
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.
Fundamentals of Analytics
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.
Fundamentals of Analytics and Regression Modeling and Forecasting