Grow Smart with
Data and Analytics

An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity.

After the KNIME Fall Summit, the dinosaurs went back home… well, switched off their laptops. Dean Abbott and John Elder, longstanding data science experts, were invited to the Fall Summit by Michael to join him in a discussion of The Future of Data Science: A Fireside Chat with Industry Dinosaurs.

Predictive modelers love building the models and then comparing them to determine the best model to deliver to the stakeholder. For Regression, the common metrics are R^2, mean squared error, root mean squared error, or mean absolute error. For classification, we usually see the confusion matrix as the basis for accuracy: Precision/Recall, Specificity/Sensitivity, and percent correct classification. These all have their place in our toolbox.

Speakers: Michael Berthold (KNIME), Dean Abbott (Wunderkind Corporation), John Elder (Elder Research)

Data Science fails when outdated models no longer represent what you’ll be seeing in the future. Dean and John pick up on the extra requirements needed to ensure continuous deployment of data science and dive deeper into key topics to protect data science models from extinction!

Predictive analytics has moved from a niche technology used in a few industries, to one of the most important technologies any data-driven business needs. Because of the demand, there has been rapid growth in university programs in machine learning and data science. These teach the science well, but do not describe the “art” of predictive analytics, which includes practical tradeoffs when data is imperfect.

Predictive modelers love building the models and then comparing them to determine the best model to deliver to the stakeholder. For Regression, the common metrics are R^2, mean squared error, root mean squared error, or mean absolute error. For classification, we usually see the confusion matrix as the basis for accuracy: Precision/Recall, Specificity/Sensitivity, and percent correct classification. These all have their place in our toolbox.

Predictive modelers love building the models and then comparing them to determine the best model to deliver to the stakeholder. For Regression, the common metrics are R^2, mean squared error, root mean squared error, or mean absolute error. For classification, we usually see the confusion matrix as the basis for accuracy: Precision/Recall, Specificity/Sensitivity, and percent correct classification. These all have their place in our toolbox.

This talk will describe the use of random permutation to uncover and describe model input and model prediction sensitivities. Techniques such as Breiman’s “permutation importance” and the use of bootstrap sampling to uncover sensitivities will be discussed and will be applied to models built from data drawn from customer analytics.

Five Things They Didn’t Teach You in Data Science School

Hundreds of Bachelors and Masters Degree programs, Certificate programs, and bootcamps have sprung up to teach the science behind Data Science, including machine learning algorithms, statistics, and coding in python, R, and SQL. Yet data scientists quickly discover that it takes more to be a data scientist than knowing the science and data doesn’t always cooperate with the analyst! Dean describes five things that are critical to success in building predictive models and creating solutions with machine learning in a business context that are rarely taught in school. Real-world examples show how these principles matter operationally. Hint: as cool as Gradient Boosted Trees, Random Forests, and Deep Learning networks are, none of these principles are related to algorithms.

Listen to our team of developers and data scientists highlight the newest KNIME features, followed by the keynote presentation by Dean Abbott. Join the free webinar session and speak to the KNIME Workflow Doctor, chat with other developers, and more.

Dean Abbott ist Präsident von Abbott Analytics und derzeit der Bodily Bicentennial Professor in Analytics an der UVA Darden School of Business. Er ist ein international anerkannter Vordenker und Innovator auf dem Gebiet der Datenwissenschaft und der prädiktiven Analytik und verfügt über mehr als drei Jahrzehnte Erfahrung bei der Lösung einer Vielzahl von Problemen im privaten und öffentlichen Sektor. Herr Abbott ist der Autor von Applied Predictive Analytics (Wiley, 2014) und Mitautor von The IBM SPSS Modeler Cookbook (Packt Publishing, 2013).

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Customer Lifetime Value (CLV) is considered one of the most useful measures for business to consumer (B2C) companies, and is usually considered more valuable than other measures like conversion rate, average order value, and purchase frequency. If an accurate measure of CLV can be obtained, companies can determine which customers to prioritize with marketing messages and discount offers.

There are now in the world, machines that think, that learn, and that create. Moreover, their abilityto do these things is going to increase rapidly until in a visible future the range of problems they canhandle will be coextensive with the range to which the human mind has been applied.