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Nov 09, 2023 209 likes •367 views

logoslides Master degree in Data Science 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

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logoslides Master degree in Data Science

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logoslides

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...

Master degree in Data Science

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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...

Master degree in Data Science

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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.

Master degree in Data Science

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The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

The program is organized in three semesters

Master degree in Data Science

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The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

The “core” courses

Master degree in Data Science

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The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

Computer science and engineering

Master degree in Data Science

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logoslides

The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

Mathematics

Master degree in Data Science

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The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

Statistics

Master degree in Data Science

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The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

Applications of Data Science

Master degree in Data Science

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The courses

First year I semester First year II semester Second year I semester Fundamentals of Information Systems (12 CFU) Algorithmic Methods and Machine Learning (12 CFU) Business, Economic and Financial Data (6 CFU) Stochastic Methods (6 CFU) Large scale optimization methods (6 CFU) Biological data (6 CFU) Statistical learning (part I) (6 CFU) Statistical learning (part II) (6 CFU) Elective course (6 CFU) Cognitive, Behavioral and Social Data (6 CFU) Elective course (6 CFU) Elective course (6 CFU) Elective course (6 CFU)

Elective courses

Master degree in Data Science

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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,

Master degree in Data Science

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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...)

Master degree in Data Science

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Thanks to the contribution of the Fondazione Bruno Kessler, a new laboratory will be dedicated to Data Science

DIPARTIMENTO MATEMATICA

DATA SCIENCE lab

Master degree in Data Science

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Contacts http://datascience.math.unipd.it/ daipra@math.unipd.it

(Paolo Dai Pra)

Master degree in Data Science