Data science for Urban Design and Mobility


Many organizations focus on the need for data scientists. But another equally vital role is that of the business translator who can serve as the link analytical talent and practical applications to business questions.

The amount of data is growing every day. They generate opportunities for medium-sized and large businesses and risks for small ones. Thus, both need to innovate their business models. The course is structured as a showcase of the main technologies applied in different business scenarios to introduce the world of applied Data Science.

Above all, there will be a focus on learning how to manage data-centric projects to support business strategies and innovation projects using an innovative tool, the Goal Poster. Then, moving to the entire data value chain management, from collection to machine learning techniques and data visualization, to implement the right actions and guide decisions. Finally, translate business needs into technical questions and effectively transfer the results to the business, reducing the gap between technical and business expertise.


To survive the data everywhere.

Nowadays, we live in a world of big data. However, it often happens that companies have a lot of data at their disposal but sometimes ignore it, becoming marginal.

You will need to know the impact of data for your company, which data your company is already collecting, what you can do with it and what your competition is doing, defining strategies to deal with the changes.

To improve your business.

With a clear data science strategy, you can respond quickly to changes in society, on the market, or to customer’s needs and experience, which gives you a competitive edge and a potential business optimization.

Any business goal you define and apply data science techniques brings business innovation.

To get started.

This course gets the foundations on interpreting data, discovering ways to benefit from the data you are collecting and starting your data science strategy for your company.

To lead a data-driven business.

You need to learn about the concepts of data-driven organizations, how to get there and how to map business goals onto technological implications.


The base of the course is the interaction with both the teachers and fellow course attendees on real cases using data science methods, building a process in a company based on business needs towards a data-driven company.


  • Data-centric projects organization

  • Master the entire data value chain

  • Implement the right actions to support decisions

  • Translate business needs into technical questions

  • Learning the Goal Poster methodology


October 12: Data-driven Innovation

(afternoon, 2pm to 5pm CET)

  • Self-definition and learning objectives

  • Data-driven decisions and projects

  • Self-Positioning on a data science process

  • Principles and motivations of data engineering and data science

  • Introduction to data analysis methods

  • Formulation of a data-centric problem

  • Principles of data science project management

  • The Goal Poster methodology

  • Group work (part 1): Setting the goal, questions, metrics, and actions

October 15: Data Visualization & Data Storytelling

(morning, 9am to 12noon CET)

  • Group work (part 2): Questions, metrics, and actions

  • Visual and cognitive perception

  • Gestalt principles

  • Data science metrics

  • Group work (part 3): designing a wireframe for the Goal Poster

October 20: Define and realize a data-centric project

(afternoon, 2pm to 5pm CET)

  • Challenges and risks in data science projects

  • Group work (part4): Data risks

  • Examples of data-driven projects


Marco Brambilla

Full Professor @ Politecnico di Milano

Emanuele Della Valle

Associate Professor @ Politecnico di Milano

Marco Brambilla is a full professor of Web Science and Digital Innovation at the Department of Electronics, Information and Bioengineering of the Politecnico di Milano (Italy), where he leads the Data Science Lab.

His research interests are on: Web Science, Big Data Analysis, Social Media Analytics, and Model-driven Development. He currently teaches Enterprise ICT Architecture, Systems and Methods for Big and Unstructured Data, Web Science, Digital Innovation Lab, Model-driven Engineering.

He is the inventor of the Interaction Flow Modeling Language (IFML), a standard by the OMG, and of 2 patents on crowdsourcing and multi-domain search. He also founded two start-ups: Fluxedo and WebRatio.

Emanuele Della Valle holds a PhD in Computer Science from the Vrije Universiteit Amsterdam and a Master degree in Computer Science and Engineering from Politecnico di Milano. He is an associate professor at the Department of Electronics, Information and Bioengineering of the Politecnico di Milano.

In 20 years of research, his research interests covered Big Data, Stream Processing, Semantic technologies, Data Science, Web Information Retrieval, and Service-Oriented Architectures. He started the stream reasoning research field, positioning it at the intersection between Stream Processing and Artificial Intelligence.

He was Principal Investigator of two EIT Digital activities on Digital Cities between 2013 and 2015. From 2001 to 2008, he worked in CEFRIEL.

📅 Dates

October 12 - 15 - 20, 2021


9 hours in 3 days


Synchronous online

€ Course fee

1.200€ FREE thanks to EIT Urban Mobility financing

🇬🇧 Language


📜 Certificate

A digital certificate of participation will be delivered upon completion of the programme


👩‍💼 Contact

Marco Brambilla (

"Cities today are facing a variety of challenges. The city of Milano, Italy has identified the following challenges for the urban plan for sustainable mobility: increasing accessibility of public spaces and mobility services, reducing dependency on private vehicles, redistributing public space in favour of active mobility, reducing road accidents, reducing the exposure to noise and air pollutants, enhancing freedom of choice to more sustainable modes of transport.

Today, the health emergency connected to COVID-19 has set us new challenges, such as the change in mobility behaviour of citizens and commuters due to the reduction in public transport capacity and the propensity to smart working.

Data has become extremely valuable for addressing all these challenges, especially from a business perspective. Improving data within an organization has the potential to deliver value in many areas. It can help lower costs and increase profits but also reduce risks. The data-driven approach allows companies to anticipate changes and challenges more effectively and accurately."

Interview with Andrea Canevazzi (AMAT Milan)