Training in Data Science, Machine Learning & Artificial Intelligence
I provide training to executives, business leaders and teams at your site. Please contact me for prices, schedules and to tailor content that works for you.
AI, Machine Learning and Data Science: An Executive Primer (1 day)
What is artificial intelligence (AI)? What does it mean for your business? And how can you take advantage of it? This course will help you answer these questions.
The course focuses on AI, machine learning, and data science to help you understand their implications for business strategy. You will be given the tools to start shaping your own business strategy. Topics can include:
- Jargon busting: what are these various fields and technologies and how can businesses benefit from them?
- Learning from others: how have other businesses benefited from data and AI related technology?
- Maturity and readiness: how to assess your organisation’s data maturity and readiness for leveraging data science, machine learning and artificial intelligence.
- Building a capability: what you need to do to set your organisatsion up for success.
- Prioritisation: how to find the most valuable opportunities that can be realised quickly. How to recognise a problem that can be solved with data, machine learning and artificial intelligence.
- Data literacy: a high level introduction to how machine learning works, correlation and causation, bias, measuring performance, ROI and designing trials.
- Vendors: how to evaluate vendors and negotiate the right machine learning or AI solution for your business.
Building High Performing Data Science & Analytics Teams (1 to 3 days)
This course is for teams, managers and technical leads who work in analytics and data science. Data Science and Analytics are complex activities that are sometimes difficult to communicate. In this course you will learn how to leverage the Guerrilla Analytics Principles to manage this complexity and build and run a high performing team. Topics covered include:
- Motivation: Why working with data is difficult and how to mitigate disruptions with the Guerrilla Analytics Principles.
- Operating models: how to build end-to-end ways of working in your team so confusion is reduced and teams can focus on creating value.
- Technology capabilities: the core capabilities you need to run a productive team smoothly.
- Engagement management: how to manage stakeholders through projects that are highly iterative and exploratory by their nature. Project artefacts and communications that help data science projects succeed.