data scientistdare-project.eu/wp-content/uploads/2019/09/poster-da.re_2.pdf · data scientist the...

1
The Data Science Project to Re-image education (Da.Re.) is part of the Erasmus+ program funded by the European Union. Consulenza e Data Scientist The Sexiest Job of the 21st Century The final goal of this project is to desi- gn an innovative study curriculum in Data Science. The consortium carried out a survey to know the state of the art of data science according to three main directives: university training paths of all levels, market needs and new training needs. The results of the survey show the need, horizontally shared by any kind of organization, of BRIDGE PERSON: a figure skilled in data analysis but also aware of the specific domain of data. The Da.Re. curriculum shapes a bridge person giving competencies in three main fields, characterised by diffe- rent specific contents: Apply to the pilot course! dare-project.eu Harvard Business Review In August 2018 an experimental pilot course was launched for helping in de- fining the final contents of the curri- culum. The course is made up by onli- ne and face-to-face education with experts from companies: the idea is that the online education provides students with the technical knowled- ge and skills needed to do the practical training. The residential activity will be placed in the early 2019, based in the Marche Region, in ITALY. The par- ticipants will be challenged in different case studies to be solved, structured in the 3 main fields developed for the online education. Graph obtained using the correlation between the time series of the EEG (insight) Empirical distribution of one time series (insight) Clustering using gaussian mixture models Retrieval / Import Cleaning Integration Tr ansformation Reduction F eature ex traction Dimensionality Reduction Clustering Regression What to show? Dimensionality of data T ypes of data to visualise Principles of graphical excellence Bib. Johnson J., Tesei L., Piangerelli M., Merelli E., Paci R., Stojanovic N., Leitão P., Barbosa J., Amador M. BIG DATA: BUSINESS, TECHNOLOGY EDUCATION, AND SCIENCE, Ubiquity 2018 Riccardo Paci, Cristina Cristalli, Martina Senzacqua Luca Tesei, Emanuela Merelli, Marco Piangerelli

Upload: others

Post on 02-Oct-2020

0 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Data Scientistdare-project.eu/wp-content/uploads/2019/09/Poster-Da.Re_2.pdf · Data Scientist The Sexiest Job of the 21st Century The final goal of this project is to desi-gn an innovative

The Data Science Project to Re-image education (Da.Re.) is part of the Erasmus+ program funded by the European Union.

Consulenzae

Data ScientistThe Sexiest Job of the 21st Century

The final goal of this project is to desi-

gn an innovative study curriculum in

Data Science. The consortium carried

out a survey to know the state of the

art of data science according to three

main directives: university training

paths of all levels, market needs and

new training needs.

The results of the survey show the

need, horizontally shared by any kind

of organization, of BRIDGE PERSON:

a figure skilled in data analysis but

also aware of the specific domain of

data.

The Da.Re. curriculum shapes a bridge

person giving competencies in three

main fields, characterised by diffe-

rent specific contents:

Apply to the pilot course!dare-project.eu

Harvard Business Review

In August 2018 an experimental pilot

course was launched for helping in de-

fining the final contents of the curri-

culum. The course is made up by onli-

ne and face-to-face education with

experts from companies: the idea is

that the online education provides

students with the technical knowled-

ge and skills needed to do the practical

training. The residential activity will

be placed in the early 2019, based in

the Marche Region, in ITALY. The par-

ticipants will be challenged in different

case studies to be solved, structured

in the 3 main fields developed for the

online education.

Graph obtained using the correlation between the time series of the EEG (insight)

Empirical distribution of one time series (insight)

Clustering using gaussian mixture models

Retrieval

/ Import

Cleaning

Integration

Transformation

Reduction

Featureextraction

DimensionalityReduction

Clustering

Regression

What to show?

Dimensionalityof data

Types of data to visualise

Principlesof graphical excellence

ConsulenzaeConsulenzae

Bib. Johnson J., Tesei L., Piangerelli M., Merelli E., Paci R., Stojanovic N., Leitão P., Barbosa J., Amador M.BIG DATA: BUSINESS, TECHNOLOGY EDUCATION, AND SCIENCE, Ubiquity 2018

Riccardo Paci, Cristina Cristalli,

Martina Senzacqua

Luca Tesei, Emanuela Merelli, Marco Piangerelli