We live in a world where decisions are being made by software. From mortgage applications to driverless vehicles, the results can be life-changing. But the benefits of automation are clear. If businesses use data science to automate decisions they will become more productive and more profitable.
So the question becomes: how can we be sure that these algorithms make the best decisions? How can we prove that an autonomous vehicle will make the right decision when life depends on it? How can we prove that data science works?
In this presentation, you will discover how to test the models produced from the application of Data Science. We will discuss the common problems that are encountered and I will show you how to overcome them. You will learn how to evaluate models both quantitatively and visually. And I will explain the differences between technical measures of performance and measures that are better for business use. I will provide context by showing both disastrous and hilarious examples from industry.
Who Will Benefit?
This talk is designed to be both entertaining and informative. It is primarily focused towards people with exposure to data science due to the use of terminology. But both beginners and those interested in technology will enjoy the talk because the content is thoroughly explained and fun!
Dr. Phil Winder is an internationally recognised speaker with expertise that lies between cloud-native software and data science. He is a strong believer that we should be avoiding the same mistakes caused by the disintegration of dev and ops in data science. And that we need a new breed of engineers that span data-dev-ops.
One of Phil’s businesses, Winder Research, provides cloud-native data science consultancy, training and development. He also runs the website https://TrainingDataScience.com which provides free and paid data science training courses and workshops to help the next generation of engineering-focused data scientists.
Phil’s talks focus on being fun and inclusive but also deliver value to businesses through knowledge transfer.