Advanced Analytics for Data Science

Blended Intensive Programme (BIP), September 5-15, October 2 and 20, 2023 – Naples

Information

In this course, we present a comprehensive view of ‘data science’, from the management of datasets (dataset merging, pre-processing and data fusion) to the advanced methods that can be applied for the synthesis and extraction of information, through to the presentation and visualisation of results by means of interactive Dashbords, allowing for what-if analyses.

We will discuss and summarise potential real-world application domains from data corporate, health, and IT security data, inviting stakeholders and professionals from the corporate world to recount their direct experiences and formulate questions to be addressed from an analytical point of view.

The training course aims to provide participants with a detailed overview of methodologies and technologies of modern parametric and non-parametric data analysis procedures. At the end of the course, participants will be able to construct data-centred applications and to critically evaluate the results in order to guide decision-making and to have insights into the relationships that link the different factors of a complex system.

The participation of IBM Client Innovation Center in the consortium aims at having an influential player to assist in this aspect. From these experiences we highlight challenges and potential research directions. Overall, the course aims to serve as a reference point on data science and advanced statistical analysis for researchers decision-makers and application developers.

Participants

Participants in the course in Advanced Analytics for Data Science will be students from undergraduate Master’s degree courses, Phd courses, post docs and technicians interested in learning more about modern methodologies and technologies for the analysis of complex and multidimensional data. Participants may come from both the partner institutes of the partnership and from other partner institutes of the partnership.

Disciplinary Area

For participation in the course, it is preferable to have a previous study background in quantitative studies (mathematics, statistics, economics, data science, computer science). However, it is not compulsory.

A mentor will also be available for each participant during the training period (weekly online mentorship from Data Science experts) and a dedicated programme manager provided by BIP Faculty available virtually. These figures will act as support in the learning phase and in skills development.

Learning and Teaching Methods

The learning method is divided into virtual and face-to-face sessions, focused on case studies of real data management. The learning methodology will combine lectures of the methodologies to practical workshops aimed at implementing the techniques presented on the case studies.

  • Number of ECTS credits awarded: 6 CFU
  • Main language of teaching/training: English level B2, as described in the ‘European Language Portfolio’ section of the Council of Europe

Registration

Participants are waived of the fee course.

​Coffee, snacks, and light lunch will be provided by the host institution while for dinner a couple of partner restaurants will be indicated.

Program

The course will consist of 9 days in total.

September 5 (TBC) – virtual

  • Statistical learning for data science (with the participation of representatives of the various institutions involved – kick-off)

September 8 (TBC) – virtual

  • Presentation of the data integration, pre-processing and data fusion system (with the support of a technical expert

September 11 – in presence

  • 14.30-17.30 Welcome IBM Client Innovation Center

September 12 – in presence

  • 9.30-11.30 Linear regression methods: recalls simple and multiple regression, quantile regression, and gamlss regression
  • 11.30-12.30 Generalized Linear Models
  • 14.30-17.30 Laboratory lectures – supervised tutorials as well as individual and team work

September 13 – in presence

  • 9.30-12.30 Non supervised methods
    14.30-17.30 Methods for dimension reduction and clustering
  • 17.30-18.30 Laboratory lectures – supervised tutorials as well as individual and team work

September 14 – in presence

  • 9.30-12.30 Latent variable models
  • 14.30-17.30 Laboratory lectures – supervised tutorials as well as individual and team work

September 15 – in presence

  • 9.30-12.30 Hybrid approach for the analysis of complex data structures
  • 14.30-16.30. Laboratory lectures 2 hours – supervised tutorials as well as individual and team work
  • 16.30-17.30 Summary, feedback, alternative methods, wrap-up

October 2 (TBC) – virtual

  • Presentation of results using Dashboards (with the participation of a BIP Faculty expert in Data Science & Machine Learning

October 20 – virtual & in presence

  • Presentation of the results of an analysis project by the participants (20 October 2023 – on World Statistics Day). Department of Political Sciences – University of Naples Federico II (Aula G1)
  • There will be a half of day dedicated to the BIP project and Statistics.
    Title: ‘Advanced Statistical Modelling for data science in Digital Age’
  • Keynote speaker: Nial Friel, Full Professor, University College Dublin
    https://maths.ucd.ie/~nial/

This will be followed by a panel discussion attended by members of the scientific committee and the concluding papers of the participants in the Blended Intensive Programme Advanced analytics for data science.

Partnership

  • University of Naples Federico II, Italy
  • IBM Client Innovation Center, Italy
  • Université Côte d’Azur / I3S / CNRS | Polytech Nice Sophia, France
  • Athens University of Economics and Busines, Greece
  • Democritus University of Thrace, Greece
  • International School for Social and Business Studies, Celje, Slovenia   

Program Committee

  • MARIA IANNARIO, University of Naples Federico II (proponent and coordinator)
  • GIANCARLO RAGOZINI, University of Naples Federico II
  • LUCA NACCARATO, IBM Client Innovation Center, Italy
  • GALENA PISONI, Université Côte d’Azur / I3S / CNRS | Polytech Nice Sophia
  • IOANNIS NTZOUFRAS, Athens University of Economics and Busines, Greece (sending institution)
  • ANGELOS MARKOS, Democritus University of Thrace, Greece
  • KRISTIJAN BREZNIK, International School for Social and Business Studies (ISSBS)

Local Scientific Committee

  • MARIA IANNARIO, University of Naples Federico II
  • ALFONSO IODICE D’ENZA, University of Naples Federico II
  • LUCIO PALAZZO, University of Naples Federico II
  • FRANCESCO PALUMBO, University of Naples Federico II
  • GIANCARLO RAGOZINI, University of Naples Federico II
  • DOMENICO VISTOCCO, University of Naples Federico II

Conference Secretariat

  • MARIA GIOVANNA PORZIO, University of Naples Federico II
  • ANTONIETTA BISCEGLIA, University of Naples Federico II

Mail: maria.iannario@unina.it

Social Events

To be announced

Venue

Department of Political Science, University of Naples Federico II

Via Leopoldo Rodinò, 22 – Naples, Italy
http://www.scienzepolitiche.unina.it

  • Statistics Laboratory (Aula G1)

Accomodation

Participants can enjoy low-cost accommodation at the hostel. Some suggested hostels: