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Computational Intelligence for Optimization
This course unit should introduce students to the basic concept of optimization and to a set of heuristic methods for solving, or approximating, optimization problems. At the same time, this course unit should help students acquiring some basis of programming.

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 Analytics
Big data is nothing but a collection of a large number of data that is impossible to be processed using traditional data computing techniques. Hadoop on the other hand can be defined as a complete subject that involves various tools, techniques and frameworks. The course unit provide in-depth knowledge on Big data and Hadoop technologies.
At the end of the course unit, the students should be able to process and analyze vast amount of heterogeneous data for getting useful insights from them.

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).

Deep Learning
This course unit will focus on a part of a broader family of machine learning methods, based on architectures such as deep neural networks, deep belief networks and recurrent neural networks.
The objective of this course unit is to introduce the students to these complex architectures and to present some of the most relevant results that have been obtained in the recent years, like for instance the ones in computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, etc. This course unit is very practical, and many of the most popular deep learning systems and tools will be explained and used.

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.

Text Mining
This course unit will approach the process of deriving high-quality information from text. High-quality information will be derived through the devising of patterns and trends through means such as statistical pattern learning.
This course unit will give solid basis to the process of structuring the input text (for instance parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output.
Practical applications developed during this course unit will involve, among several others, the scan of vast sets of documents written in a natural language and either model the document set for predictive classification purposes or populate large databases or indexing the extracted information.