

To maximise impact, you need AI literacy at every level of your teams.

We help leadership teams to understand the impact that AI could have on their business.

We teach managers how to commission data science projects and build AI into their operations.

We help analysts integrate data science into their work and help data scientists keep up with the cutting edge of their field.

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‘AI for Execs’ bootcamp

Get up-to-date with the latest in applied AI, from key AI concepts to current usage trends and masterclasses on how to maximise its impact in your organisation.


Learn standard packages for data visualisation (Matplotlib, Pyplot), data wrangling (Pandas, Numpy) and machine learning (Scikit-Learn, Keras).

Make technical concepts accessible

Give people across your organisation insight into your AI projects. We’ll help you make complicated AI concepts, results and data outputs accessible and understandable for everyone, regardless of technical expertise.


Learn basic statistics, how to determine the significance of results using frequentist statistical tests, and how to apply Bayes theorem to parameter inference.

Data science project management

Become familiar with the typical structure of data science projects, common staffing profiles, standard delivery methodologies (eg Agile) and project risk management.

Introduction to machine learning

Understand the foundations of machine learning, including linear and logistic regression, model evaluation, regularisation, and cross-validation.

Technical translation

Cultural differences between technical and non-technical teams can undermine your AI project’s effectiveness. Learn how to foster communication and change by building cross-functional teams with a broad range of skills.

Natural language processing

Discover how to apply machine learning to human (natural) language, from basic techniques like tokenization and vectorisation to pre-trained language models like word2vec and GloVe embeddings.

AI safety and data science ethics:

Understand how machine learning algorithms can fail or be misused and the challenges the industry faces in relation to the safety and fairness of models.

Deep learning

Get an introduction to the different types of neural networks (including dense, convolution and recurrent networks), how they work (including gradient descent and backpropagation) and the wide range of problems they can solve.

Engineering system architecture

Get experience in designing data pipelines and learn how to query and build a range of database types, including relational databases. Find out how machine learning models are deployed via APIs and web applications.

Problem solving

Discover how to identify the right technical solution for a project and how to develop data science software in a restricted time frame.


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