Accessible ways to get more value from your data
Tools and techniques that help businesses get more value from even incomplete or patchy data are now freely available and accessible to all types of organisations. Attend this intensive course to find out in just half a day, what you need to know about these analytical techniques, how they are being used and what questions you should ask to start using them in your organisation now.
PLUS: Gain practical experience on the associated course on Basics of Predictive Analytics.
This successful client training programme is newly available to organisations of all sizes and kinds. Learn how you can:
There are two types of course available, which can be booked independently or together.
The Appreciation Course is designed for senior managers and information leaders who want to appreciate how predictive analytics could help them create more value from their data. Participants don’t need to be mathematics or software experts. The course explains why they need to know about predictive analytics and tells them what they need to know to ask the right questions and understand the answers.
The Basics Course is also suitable for those who might attend the Appreciation Course, as well as information professionals who want to revise and/or start using these techniques in earnest.
Each course is run as a workshop, with some presentations, but also games, simulations and practical exercises. Courses have no more than 10 participants who work in groups where discussion and shared learning is encouraged.
The Appreciation Course covers:
The Basics Course will have you building models and making predictions, using real data from actual scenarios. You will leave able to access predictive analytics tools and use them to explore your data.
The course is led by Z/Yen’s experienced Predictive Analytics Leads: Ian Harris. With more than 4 decades of experience between them, Ian and Mark have been in the forefront of developing Predictive Analytics for clients in that time.
Please contact Linda on (020) 7562 9562 or firstname.lastname@example.org for further information.
Further Reading: Machine Learning and Professional Work - A Lookahead to 2040