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This course is for managers and workers without a strong quantitative background and presents a range of skills and applications.With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. Using data to provide insights and guidance that computers alone cannot requires “data literacy”, which is doubly important for anyone working alongside, supporting, leading, or hiring a data-science and analytics team. Data literacy involves a broad range of skills, including: an appreciation of the language of data; understanding uncertainty (expressed as probabilities) and complexity; interpreting relationships in data (multivariate correlation) and visual representations of them; and reading visual representations of data. It also includes the ability to think rigorously and abstractly about evidence-based decision-making and manipulate data accordingly. This course introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases.

This two day course provides an informed, realistic foundation for establishing best practice data governance. As data becomes more and more critical both to the effective operation of organisations, and to their performance in an increasingly competitive landscape, the efficient and effective management of that data becomes crucial. Data Governance refers to the processes and skills required to effectively manage data, whether big or small, traditional or digital. This two day course provides an informed, realistic and comprehensive foundation for establishing best practice Data Governance in your organisation. Suitable for every level from CDO to executive to data steward, this highly practical course will equip you with the tools and strategies needed to successfully create and implement a Data Governance strategy and roadmap.

This course is for managers and workers without a strong quantitative background and presents a range of skills and applications with data. With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. This course for managers and workers without a strong quantitative background introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.

Our leading course has transformed the machine-learning (ML), artificial intelligence (AI) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it. This course is an intuitive, hands-on introduction to AI, data science and machine learning. This is your artificial intelligence 101, data science 101 and machine learning 101 as there is significant overlap. The training focuses on central concepts and key skills, leaving you with a deep understanding of the foundations of AI and data science and even some of the more advanced tools used in the field. The skills taught are transferable to all software platforms, and the course does not involve coding, or require any coding knowledge or experience. A tool with a graphical user interface is used so you can focus on learning the central skills and ideas. Key skills taught include building, assessing, selecting and deploying predictive models, as well as employing some of the most commonly used methods in the field, including general linear models (GLMs), and advanced methods such as random forests. This also makes it your predictive analytics 101.

Our leading course has transformed the machine-learning (ML), artificial intelligence (AI) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it. This course is an intuitive, hands-on introduction to AI, data science and machine learning. This is your artificial intelligence 101, data science 101 and machine learning 101 as there is significant overlap. The training focuses on central concepts and key skills, leaving you with a deep understanding of the foundations of AI and data science and even some of the more advanced tools used in the field. The skills taught are transferable to all software platforms, and the course does not involve coding, or require any coding knowledge or experience. A tool with a graphical user interface is used so you can focus on learning the central skills and ideas. Key skills taught include building, assessing, selecting and deploying predictive models, as well as employing some of the most commonly used methods in the field, including general linear models (GLMs), and advanced methods such as random forests. This also makes it your predictive analytics 101. The course also covers key issues of data science practice in a work environment, and directs you to a range of further learning directions.

This course is aimed primarily at executives and managers, and anyone who wants to leverage analytics to support their most vital decisions. The most valuable use of analytics is in support of the most important decisions in the organisation. The effective use of data and analysis promises better decision making at all levels of the organisation, yet the highest levels tend to go unserved. This course serves to empower senior executives with skills to make more effective use of data analytics. The course covers strategic decision making in a rigorous context, and shows attendees ways to use data to make better decisions. Attendees will learn how to receive, understand and make decisions from a range of analytics methods, including visualisation and dashboards.

R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces. This two-day course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work. This two-day course is an introduction to the R programming language, beginning with the most basic operations of downloading and installing the environment. Participants will learn how to input and manipulate data and be instructed in all the aspects of procedural programming in R, allowing them to create their own R functions and customise code. The course will also introduce R data structures, statistical operations, the creation of R visualisations, and options for generating output from all of these to external files. It will also provide an overview of the use of packages in R, and an introduction to some of the most common data mining, interactive visualisation and integrated graphical user interface packages.

This course is for managers and workers without a strong quantitative background and presents a range of skills and applications. With the advent of automation, humans’ role has become to do what computers cannot. Many more white-collar workers—perhaps all of them—will end up “working with data” to some extent. Using data to provide insights and guidance that computers alone cannot requires “data literacy”, which is doubly important for anyone working alongside, supporting, leading, or hiring a data-science and analytics team. Data literacy involves a broad range of skills, including: an appreciation of the language of data; understanding uncertainty (expressed as probabilities) and complexity; interpreting relationships in data (multivariate correlation) and visual representations of them; and reading visual representations of data. It also includes the ability to think rigorously and abstractly about evidence-based decision-making and manipulate data accordingly. This course introduces a range of skills and applications related to critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases.

This course is for executives and managers who want to leverage analytics to support their most vital decisions. This course is for executives and managers who want to leverage analytics to support their most vital decisions and enable better decision-making at the highest levels. It empowers senior executives with skills to make more effective use of data analytics. It covers contexts including strategic decision-making and shows attendees ways to use data to make better decisions. Attendees will learn how to receive, understand and make decisions from a range of analytics methods, including visualisation and dashboards.