Management - 2 ECTS
Statistics - 2 ECTS
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. 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 - 6 ECTS
The aim of this course is to deepen the knowledge of inferential methods which are useful for empirical research in all areas of business economics, as well as banking, insurance and finance. Together with the theoretical concepts, data sets derived from empirical studies will be analysed. The open source software environment for statistical computing and graphics R will be introduced and used throughout.
Data Visualization - 5 ECTS
This course covers the basics of data visualization and exploratory data analysis. We will be using several data visualization libraries in Python / R starting with simple datasets and then moving to economic and financial data. We’ll also be looking at how to treat errors and missing data to avoid the most common representation mistakes.
Management for Digital 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).
Text and Web Mining - 5 ECTS
This course focuses on extracting knowledge from the web by applying classification and 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 modelling 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 preparation to create suitable multidimensional Database Marketing frameworks. Demand Segmentation and Scoring Models will be the practical applications.
Active attendance is mandatory. A minimum of 80% attendance is required.
Classes run from Monday to Friday, 5 hours per day between 9 a.m. and 2 p.m.
|Internship and final report||100|
*20 hours of classes and 20 hours of individual work weekly under the supervision of a tutor.
Assessment and diploma
To obtain the Master diploma students are required to: successfully pass an exam at the end of each course; effectively participate in the project works; serve a four-month internship and defend a Master thesis.