The essentials of machine learning and artificial intelligence
Good and Bad in Data Science
David J. Hand reminds us: “The world is bigger and more complex than you can imagine.” This makes it both difficult and critical to be clear about the questions we ask and the criteria we use to answer them.
Whether you’re using data science to extract understanding (which is often the objective of scientific discovery) or to build operational systems (for instance, process optimization or fraud detection), Hand’s talk provides a relevant overview of what successful data science requires.
He gives many examples of the strengths and potential pitfalls of data science, covering such topics as:
- The nature of data science.
- What data science requires.
- The two sides of data science – big picture versus small picture.
Corning, Solvay and Novozymes experts make the most of advanced analytics
This panel shares many considerations for companies on the move toward maturity with data analytics. They agree: There’s tremendous opportunity for those who invest in digital systems, analytics software and education that builds the workforce’s knowledge of advanced analytics.
They explain how Industry 4.0 is driving the need to extract meaning from more and more data, and why you should seek to understand the state of digital transformation at your organization. They make a case for a companywide investment in analytics skills and why subject matter experts will always be essential.
You’ll hear from:
- David J. Hand, Imperial College London
- Aziza Yormirzaeva, Corning
- Francisco Navarro, Solvay
- Lene Bjørg Cesar, Novozymes A/S