pagetop

Business Intelligence

• To know the evolution of information system use in organizations to support decision making resulting from the
  ongoing digital transformation;
• To understand the Business Intelligence concept and development technologies, including the supporting Data
  Warehouses;
• To know to multidimensional model concept and its role in supporting analytical business decision making models;
• To be able to build multidimensional databases;
• To understand the different possibilities offered by the different Business Analytics technics;
• To be able to build data visualizations with dashboards;
• To evaluate the complexity and challenges created by Big Data;
• To identify the challenges of personal privacy in analytical environments.

Data Mining
In terms of acquired knowledge, at the end of this unit the student must be able to:

• Discuss the main DM topics;
• Pre-process data;
• Use different visualization tools to explore data;
• Cluster data;
• Organize and implement a clustering process;
• Describe the main algorithms used in the association analysis.

Data Visualization
The creation and study of the visual representation of data is studied in this course unit. Thanks to the most used visualization techniques, the students will be able to effectively analyze and reason about data and evidence.
Using the methods studied in this course unit, it will be possible to make complex data more accessible, understandable and usable. Students will be faced with specific analytical tasks, such as making comparisons or understanding causality, and they will be introduced to the design principle of the graphics.
Information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics are the core of this course unit that will stand at the border between theory and applications.

Programming for Data Science
The goal of this course unit is to introduce the basics of programming in Python. The course unit is oriented to students that do not have any experience in computer programming, starting from the very basics of computation and imperative programming. However, the course unit will rapidly evolve towards advanced programming techniques and concepts. In this way, at the end of this course unit, the students will be able to effectively approach complex problems, typically characterized by vast amounts of data, programming efficient strategies to extract information and support the decision making process.

Statistics for Data Science 
In this course unit, students will learn all the most popular and useful statistical approaches to Data Science. For instance, the course unit will cover advanced techniques of multivariate statistical analysis, classical and generalized regression models, analysis of time series, and several others. At the end of this course unit, given a vast set of data and a specific problem, students will be able to choose the appropriate methodology and will be able to understand and critically analyze the obtained results. The students will also have a deep vision of the advantages, limitations and conditions of applicability of the most known statistical methodologies to data analysis.

Big Data Modelling and Management

• Understand the main challenges of storing and modelling Big Data.
• Explain the impact of Big Data solutions in different technological applications.
• Identify the main sources of Big Data and enumerate classic examples.
• Understand the different solutions for data modelling (xml, json, graph , relation, etc.), and the database management
  systems associated.
• Execute CRUD operations in different database management systems that support non-structured data models.
• Identify the best solution to model data in different scenarios.
• Explain the differences between streams of data and static data.
• Understand data sketch techniques to deal with data streams (B loom Filter, Count-Min, Count-Sketch and FM
  Sketch).

Business Cases with Data Science
Students should be able to identify and implement the most adequate analytical models to different business problems/functional areas. In addition, students should be able to interpret the results of business analytics and their implications to the business. According to the data analysis results, students should be able to make data driven decisions to optimize the business process.

Business Process Management
This course unit aims at developing knowledge and skills related to business process management, focusing on the application of conceptual methods and software tools to design, analyze, transform, monitor and control business processes and improve their performance using information technology in organizations. In particular, there are four learning outcomes:

• Identify business processes;
• Model complex business processes using a process modeling language (BPMN 2.0);
• Analyse qualitatively and quantitatively business processes;
• Redesign business processes.

Digital Transformation
This course unit (UC) intends to develop methodological and research skills on business and digital transformation. At the end of the course, students should:

• Know how to identify the concepts and enablers that motivate the digital transformation;
• Understand the concepts for the creation of new services or products as a service - "servitization" and PSS - "Product
  Services Systems", with examples of new business models powered by digital solutions;
• Know how to identify the strategic resources needed to implement digital transformation;
• To have knowledge of methodologies for the digital transformation of the business, in particular the Business;
• Transformation Management Methodology (BTM²) and how to apply it according to the concrete cases.

Machine Learning
Course unit objectives:

1. 1 - Make predictions from data.
2. 2 - Know the main problems related to predictions based on data ("data driven").
3. 3 - Know the main techniques:

• Classical methods: regression, interpolation, extrapolation;
• Bayesian Decisions;
• Instances based Systems - Decision trees;
• Neural networks – Ensambles.

Storing and Retrieving Data
The Storing and Retrieving Data provides an overview of the fundamental concepts of Relational Database Management Systems (RDBMS) and Data Warehousing. In this course students will learn the basics of Data Normalisation and Database design, and to perform basic and complex CRUD operations and to create views that facilitate data retrieval; understand the differences between OLTP and OLAP databases, and the role of Data Warehouses as a support for Analytics in an organisation; Understand the Multidimensional Model and different and architectures of Data Warehouses; implement a ETL pipeline to retrieve data from the OLTP layers to OLAP Layers; execute complex OLAP operations to support the analytics process over the Data Cube abstraction of a Data Warehouse using a ROLAP server.