INTRODUCTION Paolo Dai Pra
logoslides
INTRODUCTION Paolo Dai Pra INTRODUCTION Master degree in Data Science Paolo Dai Pra
INTRODUCTION Paolo Dai Pra Why a new Master in Data Science?
High volumes of data emerging in many different context led to the development of new methodologies to: Explore and organize the structure of available data. Identify sources of noise, distortion and uncertainty. Create and test models. Identify objectives and possible strategies, using data analysis to draw conclusions. Visualize and communicate results to specialists and non-specialists alike. This suggest a multidisciplinary approach, involving computer science and engineering, statistics, mathematics as well as those scientific contexts in which data emerge: economics, life sciences, logoslides cognitive sciences...
Master degree in Data Science
INTRODUCTION Paolo Dai Pra Why in Padova?
Research involving Data Science and applications is particularly rich and diversified in Padova, involving also cooperation with private firms and public institutions.
Computer science and engineering: data and process mining, networks, security... Statistics: analysis of economic data, biostatistics and bioinformatics, environmental statistics... Mathematics: stochastic models, large scale optimization and computational methods, topological data analysis... Other topics: neuroscience, computational biology, human-computer interaction, cognitive sciences... logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra Admission
The number of students admitted to the program is restricted as follows: EU students and non-EU students with residency in Italy: 40 Non-EU students resident abroad: 10 (call for admission closed) CALL FOR ADMISSION OPEN UNTIL SEPTEMBER 1st
The Master in Data Science welcomes students with different background: Statistics, Computer Science, Engineering, Mathematics, Physics, Biology, Economics.....
Selection is based on student’s curriculum. logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) The program is organized in three semesters
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) The “core” courses
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) Computer science and engineering
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) Mathematics
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) Statistics
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) Applications of Data Science
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The courses
First year First year Second year I semester II semester I semester Fundamentals of Information Algorithmic Methods and Business, Economic and Systems Machine Learning Financial Data (12 CFU) (12 CFU) (6 CFU) Stochastic Methods Large scale optimization Biological data (6 CFU) methods (6 CFU) (6 CFU) Statistical learning (part I) Statistical learning (part II) Elective course (6 CFU) (6 CFU) (6 CFU) Cognitive, Behavioral and Elective course Elective course Social Data (6 CFU) (6 CFU) (6 CFU) Elective course (6 CFU) Elective courses
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The elective courses
All courses are credited with 6 CFU • Game Theory. • Introduction to Omic Disciplines. • Mathematical models and numerical methods for big data, • Computational Marketing. •Law and Data. • Computer and Network Security, • Process Mining. • Bioinformatics, • Methods and Models for Combinatorial Optimization, • Biology and Physiology, • Human Computer Interaction, • Network Science, • Knowledge and Data Mining. • Human Data Analytics. • Big Data Computing, • Structural Bioinformatics, logoslides • Cognitive services, • Bioinformatics & Computational Biology,
Master degree in Data Science
INTRODUCTION Paolo Dai Pra The last semester is devoted to a STAGE (required for all students) and the THESIS
Internships will be offered by private firms, public institutions (e.g. ISTAT, Azienda Ospedaliera, Regione Veneto...) or research center (e.g. FBK’s research center, CNR Labs, University Labs...)
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra Thanks to the contribution of the Fondazione Bruno Kessler, a new laboratory will be dedicated to Data Science
DATA SCIENCE DIPARTIMENTO MATEMATICA lab
logoslides PROPOSTA 3B
Master degree in Data Science
INTRODUCTION Paolo Dai Pra Contacts
http://datascience.math.unipd.it/
[email protected] (Paolo Dai Pra)
logoslides
Master degree in Data Science
INTRODUCTION Paolo Dai Pra