Big data and AI are transforming what organisations do and how they do it. Analytics Translators play the pivotal role of recognising opportunities where data and analytics could create value, and translating them into a data-analytic assignment for an analytics team. They are the bridge between analytics and other business functions. This 6-day programme prepares you for playing this key role in your organisation. Or take the 2-day first module only for getting a good orientation into this rapidly developing field.

Why join the Analytics Translator Course?
What will you learn?

• will be literate in new technologies in data engineering and analytics. You will understand what these technologies are, how they transform what organisations do and how they do it, and why they have so much impact;
• will be able to translate a business opportunity into a data analytic question, and to structure the work into a data science project following the CRISP-DM model;
• will be able to recognise validity issues in predictive algorithms and models;
• will have a good overview of applications of data and analytics in business, government and healthcare;
• will be well-prepared for playing a leading role in helping your organisation transform into a data-driven business.

For whom?

Practical information

Teaching staff

*UvA alumni get a 10% discount. Fees are VAT-exempt

Stap Budget

You can request a STAP Budget for this programme. To be eligible for the subsidy of €1,000 you need a STAP registration certificate. To get this, tick the STAP Budget box in the application form above. Take into account that there are deadlines for registration and receiving the necessary STAP registration certificate. More about the STAP Budget

2 modules

The Analytics Translator course is divided into 2 modules. You can choose to follow the first module (2 days) or both modules (6 days). Module 1 dives deeper into understanding AI, data science and big data. While module 2 prepares you for the role of Analytics Translator.

Module 1 (2 days): Preparing for the role of Analytics Translator

• • Orientation module: Aiming to give you a realistic overview of data science and emerging technologies and their potential value for your organisation. 
  • New forms of data, new analytics, new business opportunities: Big Data, machine learning, AI: what are they, what can you do with them, and why do they have so much impact? 
  • Doing data science: Managing data-science projects as CRISP-DM projects, where a business opportunity is translated into a data-mining question, where data sources are identified and pre-processed, where a model is developed and evaluated, and then deployed. 
  • How machines learn and machine learning in business: Machine learning is the quintessential technique in AI. We will explore many of the popular algorithms, such as decision trees, random forests and neural networks. Our focus is on understanding what it takes to deploy machine learning and AI in a sound, reliable and secure way to create value in a business environment. 
  • Digital safari: Explore the landscape of emerging digital technologies We review the foundational technologies that enable the digital transformation: cloud computing, Big Data, AI, cybersecurity and IoT. We also discuss far-future disruptive technologies, such as AI & robotics, energy, extended reality (AR/VR), quantum computing, 3D printing, and biotech. And finally, we learn about service & technology ecosystems and SaaS. 

Module 2 (4 days): Preparing for the role of Analytics Translator

• • Data and analytics use cases: We can learn a lot from other companies that have successfully integrated new technologies and analytics. We review a number of use cases from a variety of industries, including healthcare and government.
  • AI strategy and value chain, and determining the right business question: How do you connect your business strategy to AI and how do you get maximum value out of data science projects, given your high-level company goals?
  • EU Privacy and AI laws: A primer in EU legislation that needs to be considered when using data and algorithms.
  • Leading successful data science teams: Data analytics teams are multidisciplinary and cover many different skill sets. We offer structures that are helpful in getting flow in data analytics projects, and we also discuss what organisational infrastructure such projects need in order to be effective.
  • Data visualisation: You learn the design principles and the applicability of various visualisation techniques.
  • Data governance and data management: We explore the capabilities needed to ensure that high-quality data are available throughout the organisation in a secure and reliable way.
  • Capstone assignment: Participants work on a practical assignment, which they develop and discuss with fellow participants and instructors. In the assignment, participants identify a business opportunity in their own organisation where data and analytics could be of value, and translate it into a data-analytic problem. Part of the assignment is to identify the required data sources and where they could be obtained, and participants will also design a suitable organisational structure for deploying such a project in one’s own organisation.