- Why use a workbench
- Why choose KNIME?
- Adding KNIME nodes with extensions
- Accessing data
- Exploring data statistically and visually
- Merging and aggregating data in KNIME
- Modeling in KNIME
- Scoring new data
- Combining KNIME with R and Python
Skill Level Beginner
- Almost every day, I'm doing some kind of work in predictive analytics, whether it's consulting with my clients, leading seminars at analytics conferences, or software training. In my work, I encounter dozens of different software options. So many, that it's hard to keep track. So why consider KNIME? KNIME is a popular open-source option that is very easy to learn. It offers just about all the functions you could ever need natively, but for that rare function that isn't available, you can always use R and Python right in KNIME. I'm Keith McCormick, and I've been doing predictive analytics for more than 20 years now. I hear more and more buzz about KNIME every year. The conferences are growing in size, the developer community is very active, and users are enthusiastic. It's become my go-to choice when leading seminars or when a client needs something easy to try in order to get started with predictive analytics. But you're never limited with KNIME. If you're into deep learning or text mining, big data, or even the internet of things, you'll find great case studies. And if you are more focused on the basics or just getting started, you'll find it to be very easy to learn. In fact, in 15 minutes, you should be up and running with a basic example. In this course, we're going to stick to the basics, but we'll cover all the highlights, so whether you want to click along or just want to watch the demonstrations, I think you'll enjoy the ride. KNIME might very well be the predictive analytics tool you've been waiting for.