Presenter: David Meintrup, professor of mathematics and statistics, Ingolstadt University of Applied Sciences
David Meintrup is responsible for the mathematical and statistical education of engineering students. Besides his teaching and research in academia, Meintrup works as a statistical trainer and consultant, in particular for the semiconductor, solar, pharmaceutical and biotech industries. He is coauthor of the book Statistics with JMP: Graphs, Descriptive Statistics and Probability, published by Wiley in March 2015. He studied at the University of Münster and at the University of Colorado, Boulder.
About the content:
Interest is growing in data literacy and the opportunity to bring about efficiency and effectiveness through the better exploitation of data.
We’ve all heard the promises, but gaining an advantage from data-driven methods is somewhat different. Success requires the integration of data management, data analysis and subject matter expertise, plus the ability to communicate findings with brevity and simplicity. Understanding the circumstances under which our data has been collected, how it has been measured, and its potential limitations, are foundations of the process of extracting maximum information from our data.
Perhaps you’re looking to get started with machine learning techniques or are looking for a route to effectively integrate these tools within existing programs. Whatever challenge you’re facing, you will learn:
- How to get started with applying Machine Learning tools to your data to maximise the value you can obtain from it.
- The importance of using domain knowledge, experience, and intuition alongside statistical and machine learning methods.
- Understanding the impact data quality, structure and resolution have upon the questions we can answer from our data.
- Understanding how data-driven processes and analytics can deliver explainable insight into your data