Download Data Science for Management - Università Cattolica del Sacro Cuore

Data Science
for Management
Ma
ste
r
Cattolica
FACULTY OF ECONOMICS
a.y. 2016/2017 - I edition
Milano, January - December 2017
First Level Specializing Master
T
The Master in Data Science for Management
is a first-level international Master program entirely taught in English. The program offers
students a comprehensive training in computational and statistical methods for management
from a problem solving perspective. The broad
goal is to empower students to become Digital
Data Managers for Business, a rapidly expanding
and highly rewarding job, which takes advantage
of the digital revolution. To address this challenge the Master program has been designed
around eight core courses (structured in lectures,
labs and seminars) that cover a variety of fields
including: database systems and programming,
statistics, text and web mining, and digital
enterprise analytics.
Prof. Guido Consonni
Director of the Master in
Data Science for Management
Learning outcomes
S
tudents will acquire solid
computational and
statistical skills to tackle real
business problems.
Participants Profile
Specifically:
The Master is addressed
to graduates with
a Bachelor or Master
of Science degree,
or who expect to
graduate by the last
session of academic
year 2015/2016 , in any
of the following fields:
Computer Science,
Economics, Engineering,
Management,
Mathematics, Statistics,
Physical Sciences.
Alternative degrees may
be considered under
specific circumstances.
❚ sound statistical and data
mining expertise
Career opportunities
G
raduates of this Master
program are ideally suited
to fill jobs as Big Data Managers
❚ data analysis capabilities in
across a variety of industries,
a business oriented perspective
ranging from ICT to consulting,
❚ proficiency in computer
from banking and finance to
science (data management and
insurance. Within companies,
programming)
Data managers will exploit the
ongoing digital revolution
delivering analytically-informed
solutions to various corporate
functions.
Curriculum overview
Active attendance is mandatory. A minimum of 80%
attendance is required.
Classes run from Monday to Friday; 20 hours of classes
and 20 hours of individual work weekly under the
supervision of a tutor
COURSE ACTIVITIES
HOURS
Classes
360
Individual study
1040
Internship andfinal report
100
Total
1500
Preparatory Courses
Management - 2 ECTS
Statistics - 2 ECTS
empirical research will be
analyzed. The open source
software environment for statistical
computing and graphics R will be
introduced.
❚ Management for Digital
Courses
❚ Data Management and
Warehousing - 4 ECTS
The course illustrates how to
implement and technically
maintain a data warehouse. The
focus is on database data design,
extraction, profiling and
standardization along with data
transformation. A detailed analysis
of big data quality management is
provided.
❚ Software Development and
Technologies for Business
Intelligence - 5 ECTS
The course focuses on software
development and Object Oriented
Programming within the Excel
framework. Students will gain
broad software development skills
to be able to independently write
procedures and functions to
expand and automate data
analysis studies and results.
❚ Statistics and Probability
(basics) - 6 ECTS
The aim of this course is to
deepen the knowledge of
inferential methods for empirical
research with applications
focusing on economics,
management and marketing, both
at a univariate and multivariate
level. Together with the theoretical
concepts, data sets derived from
Enterprise - 7 ECTS
The course illustrates the business
characteristics of a Digital
Enterprise along with the impact of
a Digital Enterprise on the
Customer Experience. At the end
of the course students will be able
to understand the importance of
ensuring that Digital Enterprise
initiatives have clear business
objectives, and identify the
relationships of Digital Enterprise
with specific enablers (Digital
Marketing, Analytics and Customer
Relationship Management).
❚ Geospatial Information
Management - 5 ECTS
This course will enable students to
develop their Data Science
capabilities and learn statistical
techniques for managing big
spatial data sets. Apart from a
theoretical section where the
procedures are introduced, a
substantial part of the course will
be devoted to practical
laboratories using the software
environment R in which the
students will apply the procedures
to different real datasets.
clustering techniques on hypertext
documents. Students are
introduced to information retrieval
and filtering methods. Practical
applications on web information
extraction and text categorization
are presented.
❚ Data Mining and Pattern
Recognition - 6 ECTS
The purpose of this course is to
provide step-by-step instructions
for the entire data modeling
process, with special emphasis on
the business knowledge necessary
to successfully use statistical
models. Moreover, students will be
able to select suitable approaches
for pattern recognition, and to
compare alternative methods in
order to implement the best
decision process for the problem
under study.
❚ Business Intelligence and Data
Analytics - 5 ECTS
This course illustrates the usage of
data and analytics in modern
business activities. The main focus
is on Data Warehousing
methodology and Database
Marketing set-up in a
multidimensional framework.
Demand Segmentation and
Scoring Models will be the
practical applications.
❚ Team work - 2 ECTS
❚ Text and Web Mining - 5
ECTS
This course focuses on extracting
knowledge from the web by
applying classification and
❚ Internship - 8 ECTS
❚ Final project and examination
- 3 ECTS
Partners
The Master in Data Science
for Management relies on
an extensive network of
companies and institutions
Scientific Director
that participate in different
Guido Consonni
ways to the project providing teaching,
case studies, mentoring,
Executive Coordinator
and internship opportunities.
Riccardo Bramante
❚ BPER Services
❚ eBay
Executive Board
❚ Energia Crescente
Riccardo Bramante
❚ Expert Systems
Adjunct Professor of Business Statistics, Università Cattolica
❚ IBM
del Sacro Cuore
❚ Microsoft
Guido Consonni, Professor of Statistics, Università Cattolica
❚ Nunatac
del Sacro Cuore
❚ SAS
Federico Rajola, Professor of Corporate Organization
❚ Sky Italia
and Head of CeTIF and ILAB, Università Cattolica del Sacro Cuore
Alberto Saccardi, Founding partner, Nunatac
Teaching staff
Giuseppe Arbia, Università Cattolica del Sacro Cuore
Michelangelo Barbera, Independent consultant
Matteo Borrotti, Energia Crescente
Riccardo Bramante, Università Cattolica del Sacro Cuore
Marco Cerri, Sky Italia
Mauro Minella, Microsoft
Stefano Peluso, Università Cattolica del Sacro Cuore
Alberto Saccardi, Nunatac
At a glance
Main features
❚ International perspective of
the learning program
❚ Entirely taught in English
Application deadline:
❚ Development of a personal and
December 31st, 2016
professional network
The selection committee will
❚ Effective
blending of Data
Science and Management skills
invite selected candidates to
attend an interview (possibly
a Skype call) for the final
Participants profile
evaluation.
The Master is addressed to
graduates with a Bachelor or
Maximum number of partici-
Master of Science degree, or
pants: 20
who expect to graduate by the
last session of academic year
Duration:
2015/2016 in any of the follo-
Master begins in January 2017
wing fields: Computer Science,
and runs until December 2017.
Economics, Engineering,
The closing ceremony is expec-
Management, Mathematics,
ted in February 2018.
Statistics, Physical Sciences.
Alternative degrees may be con-
Program type: full time.
sidered under specific circum-
Lectures will take place from
stances.
Monday to Friday.
Attendance is mandatory.
Tuition fee: 9.000 €
INFORMATION
[email protected] - [email protected]
master.unicatt.it/datascience
www.unicatt.it