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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