Projects

Our Knowledge Center helps organizations in the public, social, and private sectors in solving complex challenges with data science. Existing collaborations include healthcare, education, tourism, unemployment, among others, both nationally and abroad.  

If you feel your enterprise might have a data-driven challenge that we could help tackling, please feel free to reach out to us. 

The Nova SBE Westmont Institute of Tourism and Hospitality (WiTH) partnered with Nova SBE’s Data Science Knowledge Center (DSKC) to develop a platform with comprehensive data about tourism in Africa: The Nova SBE WiTH Africa. 

WiTH Africa aims to improve the quality of tourism-related data in Africa, generating knowledge and insights, fostering data-driven decision-making within the African tourism sector, and advocating about the power of data to a community of African tourism practitioners.  

Check out the WiTH Africa website: https://with-africa.com/ .

And subscribe to our newsletter: https://with-africa.com/join-community

The project is funded by the Westmont Hospitality Group. 

Partners: Nova SBE Westmont Institute of Tourism and Hospitality. 

The transnational project EPSILON with partners from Germany, Portugal, Cyprus and Lithuania addresses the needs of European Data for Good initiatives and higher education institutions with degrees in Data Science. As a first step, the project team will design tailored workflows and tools for European Data for Good initiatives. Based on this, EPSILON will set up a European Knowledge Platform and establish a new Data for Good initiative in Lithuania. The gained experience and knowledge will be transformed into target group specific learning material for students, university teachers and Data Science enthusiasts. 

The EPSILON project is funded by the Erasmus+, Cooperation Partnership (Project number: 2021-1-DE01-KA220-HED-000029711).

Partners: Hochschule Harz (Germany), Vilnius University (Lithuania), University of Cyprus (Cyprus). 

Project website: ▲ Hochschule Harz: European Platform for Data Science: Incubation, Learning, Operations and Network (hs-harz.de).

The project "Study for the knowledge of fraud in the structural funds in Portugal" was proposed in the context of the Think Tank on Fraud Risk, Financial Resources of the European Union, created by the Prosecutor General's Office, with the aim of knowing the reality, assess the risk and define strategies for fraud prevention. The Study aims to understand the reality of irregularities in the structural funds in Portugal.   

The study is being developed in partnership between ISCTE - IUL and NOVA SBE, by a team with experience in the areas of Data Analysis and Integration, Machine Learning and Data Visualization. The project aims to collect information in order to treat, systematize and analyze the data regarding structural Portuguese funds, in order to draw conclusions that allow a characterization of this reality at different levels.   

The study is funded by the Technical Assistance Operational Program - POAT (Project number POAT-01-6177-FEDER- 000126). 

 Partners: ISCTE – IUL, ADC, IFAP, IGF, PGR.

Project MD2TRUST (Trustworthy data science for improving healthcare efficiency: the case of the medical referral process) aims to develop a recommender system for referral of specialist care doctors to be used by primary care doctors, that is compatible with current referral practice, and that can transparently encourage organizational change, towards a more effective patient-centric healthcare management. 

The project is funded by FCT – Fundação para a Ciência e a Tecnologia. 

Partners: Nova FCT - NOVA School of Science and Technology (PT), Carnegie Mellon University (USA). 

The DSKC Data Squads are research projects that connect students interested in having practical experiences in Data Science to Nova SBE Faculty and Staff members who need an extra set of hands to develop data projects and research within the field of Data Science. These projects are implemented by Nova SBE students with the supervision of Data Squad leaders (Nova SBE Faculty and Staff) and the support of DSKC. 

The Data Squads started in 2020 and are currently on their third edition.  

The Social Sector Database (Base de Dados Social) was launched at the end of 2020, under the Social Equity Initiative – a partnership between “la Caixa” Foundation, BPI and Nova School of Business & Economics (Nova SBE). 

In Portugal, the social sector represents an important dimension of the Portuguese economy. However, the few available data sources provide insufficient and disconnected information, a low level of interaction and poor visualizations. In addition, having access to quality information is of extreme importance for the decision-making process of all the social sector stakeholders. Motivated by these challenges, the Nova SBE’s team, in this project represented by its Data Science Knowledge Center, has started mapping the social ecosystem, by creating a public and easily accessible platform. This platform aims to make available, in a simple, intuitive, and fair way, complete and quality information about all social and environmental impact organizations in Portugal. 

The Social Sector Database has the goal of creating value for all social organizations, for the beneficiaries of these organizations and their families, for researchers, for volunteers and employees, for social investors, for government bodies, for companies, among other relevant players of the Portuguese impact ecosystem. Each of these stakeholders will be able to browse and analyze the information that best supports their decision-making, answering to different use cases: 

  • which organizations in my region work in my favorite area of ​​action?  
  • which organizations have active job offers?  
  • which organizations can serve my child with her current disability?  
  • how are social organizations in Portugal distributed by Sustainable Development Goals? 
  • what are neglected social problems in my region? 
  • where can I offer my time voluntarily? 

among many others! 

Available in Portuguese and English, at the Social Sector Database you can find georeferenced social organizations, as well as their identification data (e.g., name, address, fiscal ID, contacts), their governance structure (e.g., legal format, governing bodies), their activities (e.g., area of ​​activity, projects, target audience), their impact (e.g., number of beneficiaries, awards received), and their finance and resources information (e.g., sources of income, revenue, costs, number of employees and volunteers). 

To know more, we invite you to visit our website, follow us on Facebook and subscribe our newsletter!  

Partners: Fundação ”la Caixa” and BPI (Social Equity Initiative). 

This project is the winner of the fourth edition of the Data for Change competition, and it is being developed in partnership with Associação Portuguesa Promotora de Saúde e Higiene Oral - APPSHO. 
 
The project aims to identify students with worse oral health, based on easy-to-collect, self-reported variables such as students oral hygiene habits and beliefs and nutrition and sport behaviors. By using such variables, we aim to develop a low-cost, scalable tool that can be easily applied to prioritize high-need students in accessing oral health resources, minimizing barriers to access such care and suggesting behavioral changes to students. 

The project is being advised by an experts’ board, which includes members from municipalities in the Lisbon metropolitan area, researchers in health and health economics, oral health professionals and a school director. 

Partners: Fundação ”la Caixa” and BPI (Social Equity Initiative). 

The Data Science Project-Based Learning (DS PBL) is an exclusive and unique opportunity that matches Organizations with data challenges with the Master’s in Business Analytics students. 

Students will be part of an interdisciplinary team and assigned to a specific real-world data challenge, in which the team will work throughout the course of their master’s program. Each team is supported by a Data Science Mentor and a Business & Management Mentor. 

The Data Science PBL allows students to: 

  • Immerse in the data science sector  
  • Develop a real-world data science project  
  • Learn from their peers, partners, and mentors  
  • Connect with data science professionals  

and Organizations to:  

  • Develop their team’s knowledge through “learnig by doing” 
  • Get access to an excellent pool of talent to solve the business challenges of their company 
  • Promote their company and improve brand perception  
  • Retain their talent and boost their enthusiasm at work 

Some examples of projects already developed under the DS PBL include: 

  • Predicting the new mobility patterns in the post-Covid19 period (Brisa) 
  • Develop a dynamic recommendation pricing system for the after- market car industry (tips4y) 
  • Defining a Sustainability Customer ID for Commercial Banking (CGD) 
  • Optimizing food production for a social take-away (Cozinha com Alma) 
  • Creating a Product to Segment Donors and Predict Donor Churn for the Non-Profit Sector (AMI Foundation) 
  • Predicting demand of luxury fashion businesses (Stockedge) 

Video: Summary of PBL experience for Students and Clients 2021 

Video: Summary of PBL experience for Students and Clients 2022

Within the scope of the COVID-19 pandemic, the Portuguese Government enacted a set of measures to minimize the impact of the pandemic on the population and economy. In this sense, Nova SBE supported the mission of the company NOS to develop advanced mobility indicators, that may contribute to inform the decision-making of the Portuguese municipalities.

Since different measures to contain the pandemic have been defined based on the goals of people’s movements, we started by segmenting the statistical sections that define the municipalities, in order to infer the purpose of population movements. This segmentation was made mainly on the basis of information on land uses, gathered from different sources and organized into an ontology.

Then, the evolution of population movements throughout the pandemic was analyzed, for the different segments of the statistical sections and according to the main phases of the measures to contain the pandemic.

The analysis and modeling carried out were focused on the municipalities of Cascais and Lisbon.

The final report can be found here.

Specifications:

Datasheet

1. Project's name
ANA - Anti-Pandemics Analytics

2. Project's code
LISBOA-01-02B7-FEDER-050865

3. Main goal
To boost research, technological development and innovation 

4. Region
NUTS II - Lisboa

5. Beneficiary Entities
Nova School of Business & Economics, NOS Comunicações and NOS Technology

6. Date of approval
16/07/2020

7. Starting date
15/08/2020

8. Completion date
31/07/2021

9. Total eligible cost
447 470,20 €

10. Financial Support European Union
FEDER - 357 976,16 €

11. Cofinanced by:

 

This project is the winner of the third edition of the Data for Change competition, and it is being developed in partnership with Associação Tempos Brilhantes – ATB.  

Implemented since September 2018, SAPIE is today in use by approximately 85 School Centres (Agrupamentos Escolares). However, the platform has some limitations. The project aims to understand patterns in students’ trajectory with the objective to predict and explain future individual risk of school failure at the end of the 3rd cycle of education (9th grade).

The project will use historical data from Portuguese School Centres regarding students’ performance in order to test different models to ensure the best possible utility on predicting individual risk failure, providing the impact of each feature considered on the final risk score.

This project finished in 2022.

Project's public report available here and short video available here.

Partners: Associação Tempos Brilhantes (ATB), Fundação ”la Caixa” and BPI (Social Equity Initiative).

We developed a system that assesses the risk of individuals becoming unemployed for more than 12 months, at different points of their unemployment journey. This could allow employment counselors to develop early interventions to prevent long-term unemployment. This work was co-created with experts in labor and social work policy, applying a user-centric approach that includes counselors themselves in the design process. As part of the research, we delivered a model that: i) was temporally cross validated; ii) can be dynamically applied at any point of the candidate’s journey within IEFP; iii) is explainable for each candidate; iv) was analyzed for bias against social groups; v) has better performance than the current model in production at the IEFP. In addition, we: i) investigated how the current model at IEFP was used by the counselors, and their attitudes and beliefs towards this AI model, by conducting a survey and focus groups; ii) developed tools to integrate our model in IEFP’s information system, in collaboration with the IEFP (e.g., a database infrastructure, a web service, and a dashboard); iii) designed and implemented a field intervention to investigate how different interfaces would impact the way counselors interact with the model. 

The project is in deployment at national level at IEFP, and it is planned to be completed in the beginning of 2021. 

Partner: Instituto de Emprego e Formação Profissional (IEFP), Faculdade de Engenharia da Universidade do Porto (FEUP) and Fundação para a Ciência e a Tecnologia (FCT).

This is an ongoing project that started in 2018.

This project is the winner of the second edition of the Data for Change competition, and was developed with Agência Natureza Portugal (ANP/WWF), in partnership with Docapesca – Portos e Lotas, S.A.  

The project aimed to use data analytics to shed light on the trends of the reported and sold fish caught before and during the COVID-19 pandemic, in Portugal, in all national ports. The goal was to understand how the fishing activity evolved during and after the pandemic. This exploratory project was also an experimentation on whether a predicting tool is feasible and useful for a better and environmentally friendly management of the fishing industry.   

Project's public report available here and short video available here.

The project finished in 2021.

Partners: Agência Natureza Portugal (ANP/ WWF), Docapesca – Portos e Lotas S.A., Fundação ”la Caixa” and BPI (Social Equity Initiative).

This project was awarded an Honorable Mention on the prize-giving ceremony of Prémios Verdes, by Revista Visão and Águas de Portugal.

The Electronical National Official Journal (DRE - Diário da República Eletrónico) is managed by the Portuguese Mint and Official Printing Office (INCM – Casa da Moeda) and allows the access to all Portuguese Legislation. However, and in order to clarify a specific question, citizens need often to look at different diplomas, that demand a technical and thorough interpretation.  
The partnership between DSKC and INCM rises as an opportunity to enhance the experimentation in the Natural Language Processing (NLP) field, in Portuguese and in the law context, by developing a conversational tool to support citizens clarifying questions about their Retirement. Together with a team of students from the Master’s in Business Analytics, a proof of concept of the “Decoding Legislation” project will be created, intended to make the law more accessible and interpretable for citizens.   

Partner: Portuguese Mint and Official Printing Office (INCM).

This is an ongoing project that started in 2020.

This project is the winner of the first edition of the Data for Change program and was developed in partnership with the Associação Protectora dos Diabéticos de Portugal (APDP).

Diabetic nephropathy is a critical condition that describes the gradual loss of kidney function, which can lead to irreversible damage to the kidneys. According to the Portuguese National Diabetes Observatory, one in three people with diabetes develops nephropathy.

Postponing or preventing this condition translates into an improvement in the quality and extension of the life expectancy of diabetic patients, also impacting the public health budget given that, as the disease progresses, the treatments become more complex and expensive, reaching dialysis or kidney transplantation. As it is a condition that evolves silently in the early stages, the first signs of diabetic nephropathy are critical to prevent or slow down the disease progression.

We analyzed historical transactional data, recorded during patient visits to APDP, which allow for a sociodemographic and clinical characterization of the diabetic population. As a result, the DSKC team developed a model to predict the likelihood of developing level three diabetic nephropathy over a three- and five-year time horizons. In addition to the probability, the tool also indicates the impact of each variable analyzed on the final result, allowing medical teams to act proactively, thus preventing the onset or progression of the disease.

The project finished in 2021.

 

Learn more about this project:

In July 2021, the project was recognized with first place in the Data4Good category of the SAS Curiosity Data Science Iberian Awards.

 

Partners: APDP (Associação Protectora dos Diabéticos de Portugal), ”la Caixa” Foundation and BPI (Social Equity Initiative). 

Financial institutions around the globe are looking upon the development of a data driven strategy. This strategy usually implies the restructure of their current ETL processes, teams and also the adoption of new technologies. The project proposed the implementation in Python of an existing algorithm along with several other new techniques to predict the credit default rate. For this purpose, we prepared a seminar for the data analytics department, where we used their data and the existing business problem to present the most common libraries and methods. The seminar was one of several efforts developed by the client to start two transition processes: 1) From licensed software (SAS) to Python, 2) From traditional econometric models to machine learning techniques.

Partner: Cofidis.

Crowdfunding social projects is an innovative and inspiring way to foster cooperation and collaboration within our society. This project aimed to determine the key factors that influence the success of a crowdfunding campaign. Based on the analysis of more than 2.4 thousand campaigns already carried out on the platform, the DSKC team created a model that can be used at different points in time, during the campaign. In addition to informing the predicted probability of success, the tool also points out the main factors responsible for the forecast, in order to better inform the PPL team. In this way, PPL can more assertively provide feedback to fundraisers on how to best set up their campaigns, as well as proactively react to ongoing ones, in order to maximise the chance of raising the capital required for their projects.   

Partner: PPL Crowdfunding.

This project took place between 2019 and 2020.

Developed in partnership with Tips4y – Automotive Intelligence, the project aims to develop and provide an open data platform on the automotive sector in Portugal.

For this objective, we used data about the Portuguese car park and the Aftermarket, which allowed to perform a characterization of the sector. As a result, the DSKC team developed an interactive platform that allows for search, customization and visualization of indicators such as the distribution of vehicles in the Portuguese car fleet by segment, type, fuel, district, among others, and the distribution of aftermarket orders by type of customer, weekday and quantity of items, among others. The project also allows Tips4y to innovate in the development of products and services, through new orders, communications and partnerships generated by the platform.

The project finished in 2021.

Partner: Tips4y – Automotive Intelligence.

Developing a predictive support system to understand which individuals are at risk of becoming long-term unemployed, in order to assist IEFP's Counsellors to determine how to personalize their professional development plans best. The tool allows Counsellors to understand which factors contribute to an individual's risk, giving them insight into the system's decision-making process, and granting the agency to override the tool in favor of an expert human judgment when appropriate. We are currently preparing a pilot study that will provide a critical evaluation of Counsellor’s opinions about the tool, to better understand the extent to which it facilitates their daily interactions with unemployed persons, and to support future research in unemployment prevention. This work was co-created with experts in labor policy and social work, including the Counsellors themselves in the design process.

Partner: IEFP, FEUP and FCT.

This is an ongoing projcet that started in 2019.

In the last few years, Portugal has been witnessing rapid growth in tourism which is ill-equipped to face hard to quantify challenges such as tourist congestions, loss of city identity, degradation of patrimony, etc. The project proposes a data-driven approach to the tourism management in the Portuguese territory through a set of tools to inform Portuguese policy-makers about the state of the tourism industry, going beyond the traditional survey data and research and providing complete analyses of this sector using Big Data sources. With the insights provided by the project, tourism decision-makers will have crucial information to design policies that foster sustainable and friendly cities, both for tourists and dwellers.

Partners: Turismo de Portugal and NOS.

As Tuscany is one of the most popular tourist destinations in the world, the region, its resources and inhabitants have been put under extreme pressure. The project aimed to use machine-learning algorithms to group individuals and, thus, help to better understand the region’s tourists and improve tourism management.

Partners: Toscana Promozione Turistica and Vodafone Italy.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2018.

The purpose of the project is to use the data made available by the IEFP to build a modelled system whose objective is to empower the IEFP and its local offices to better identify individuals at high risk of long-term unemployment, and to provide data-driven insights which will allow for a more efficient allocation of IEFP’s resources to respond to the needs of unemployed individuals.

Partners: IEFP and EAPN.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2018.

The ultimate goal of the project was to increase the vaccination rate for measles, mumps, and rubella in Croatia. In order to achieve this, the team predicted the risk level of each child of not receiving the required two doses of the MMR vaccine and develop clusters of the above-mentioned children, according to similar features that would allow for customized vaccination-promotion policies.

Partners: Croatian Institute of Public Health (CIPH), Institute of Public Health of Split-Dalmatia County, Andrija Stampar School of Public Health and Croatian Society for School and University Medicine.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2018.

The project’s goal was to create a machine learning model using data from incidents recorded, road characteristics, speed and flow traffic data and weather data in order to predict, within a given time window, the probability of an accident. This project allows for a better allocation of road inspectors to road sections to patrol and decrease their response time to incidents.

Partner: Rijkswaterstaat.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.

The project aimed to support local authorities in understanding and measuring tourism beyond traditional surveys and statistics so that they can explore and design solutions for sustainable tourism in the city.

Partners: Toscana Promozione Turistica and Vodafone Italy.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.

Illegal fishing and over-fishing are posing great threats to our oceans. It is estimated that 70% of global fisheries are over-exploited, and many are now on the brink of collapse. Populations of tuna, a critical apex predator in many marine ecosystems, have declined by over 90% in the past 40 years. Unless serious action is taken, we may be on the brink of large scale fishery collapses, which could devastate the environment and the livelihoods of millions around the world that depend on such fisheries.

The project aimed to create an open-source Risk Tool combining multiple satellite data sources to help overcome illegal, unreported and unregulated fishing, and creating a risk score.

Partners: Spire, World Economic Forum, Planet and DigitalGlobe.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.

The goal of the project was to assist Cascais’ Municipality in understanding patterns of unemployment in the region and to develop a system to recommend the best type of interventions to bridge skills gaps and increase people’s likelihoods of employment.

Partners: Câmara Municipal de Cascais and IEFP.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.

The project focused on optimizing matchmaking between patients and doctors by developing an automated mediation service. Its main objective was to increase the likelihood of a long-lasting relationships and the quality of follow-ups, as well as the practice of preventive medicine.

Partner: José de Mello Saúde.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.

The project aimed to help Rijkswaterstaat, a division of the Dutch Ministry of Infrastructure and Environment, by developing a policy for optimizing stationary and patrolling locations of inspectors on duty to minimize the time it takes them to reach an incident site. This way, the project contributed to optimize safety and traffic flow for road users.

Partner: Rijkswaterstaat.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.

The project aimed to figure out how improved use of its rooftops could help address challenges with water storage, green spaces, and energy generation. In order to achieve this, the team used aerial and satellite images to identify the usage of rooftops in Rotterdam.

Partner: City of Rotterdam.

This project was part of the Data Science for Social Good Europe – Summer Fellowship 2017.