logoslides INTRODUCTION INTRODUCTION Paolo Dai Pra Master degree in Data Science INTRODUCTION Paolo Dai Pra

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1 INTRODUCTION Paolo Dai Pra INTRODUCTION Paolo Dai Pra INTRODUCTION

2 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, cognitive sciences...

3 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...

4 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.

5 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data The program is organized in three semesters

6 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data The core courses

7 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data Computer science and engineering

8 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data Mathematics

9 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data Statistics

10 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data Applications of Data Science

11 The courses I Second year Fundamentals of Information Algorithmic Methods and Systems Machine Learning Business, Economic and Financial Data Stochastic Methods Large scale optimization methods Biological data Statistical learning (part I) Statistical learning (part II) Cognitive, Behavioral and Social Data s

12 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, Cognitive services, Bioinformatics & Computational Biology,

13 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...)

14 Thanks to the contribution of the Fondazione Bruno Kessler, a new laboratory will be dedicated to Data Science DATA SCIENCE lab DIPARTIMENTO MATEMATICA PROPOSTA 3B

15 Contacts (Paolo Dai Pra)