Data Science and Business Analytics

Big Data is a big challenge in today’s world. Companies and public entities urgently need to manage the acquisition, presentation, sharing, analysis and visualization of data.

The first module starts with a general overview of the Data Revolution and Industry 4.0 and then jumps into the world of technologies and architectures provided by the main cloud and technology vendors. In the second part, the course explores how to use structured and unstructured data at their best, so to jump in the “AI world” to get insights to provide support to business decisions, with some case studies.

The laboratory work plan addresses the complete process of data analysis, starting with loading data using different approaches and developing models for analyzing and visualizing the results. It will study some programming languages (as R, Python and TensorFlow) with reference to their specific use in High Performance Computing (HPC). In this context it will use libraries for parallel computing (H2O) and libraries for Deep Learning (Keras).

It will cover also software for the creation of graphic Workflow for Data Analytics, as Knime and Orange. The work plan provides for the developing of a project during a Kaggle competition at the end of the course.

The course is coordinated by Giorgio Pedrazzi and will be hold by CINECA staff, the largest Italian computing centre (Dipartimento Super Calcolo, Applicazioni e Innovazione and Laboratorio Big Data e Analytics).

The course discusses some relevant themes related to blockchain technologies, cryptocurrencies, ICOs, smart contracts and novel applications that can be built over the blockchain. Bitcoin and novel cryptocurrencies gathered momentum in the last months. More and more investors look with interest at these technologies, while others label them as a dangerous speculative bubble. The truth is that the blockchain, and the alternative implementations of a distributed ledger, represent very innovative technologies, that can be exploited to build novel distributed applications. Moreover, the possibility of creating smart contracts, running on top of the blockchain, permits trusted interactions and agreements among different (and possibly anonymous) parties, without the need for a central authority. This course will illustrate the main principles and conceptual foundations of the blockchain and smart contracts.

The course focuses on data protection law. After a brief overview of the Italian and European framework, the course will focus on the new General Data Protection Regulation, applicable all around Europe since May 2018. The course aims at exploring the relevant obligations of controllers and processors. Specific attention will also be paid to conditions of lawfulness of the processing, data subjects’ rights and controller’s accountability.

The goal of the course is to introduce business intelligence (BI) platforms, with specific reference to data warehouses seen as an enabling technology for BI. Teaching will be mainly focused on OLAP, multidimensional modelling (at both the conceptual and logical level), and self-service BI. Some notions about data integration and cleaning will be provided.

Today it is crucial to be able to use data for making better marketing decisions. This is possible by better understanding, predicting and managing customers’ behavior in a landscape where customers and brands (firms) are decision makers. This course focuses on the applied use of various techniques/methods, theories and approaches from marketing literature in practical business cases. In more particular words this means exploring, investigating and predicting behavioral and attitudinal (customer) data to provide data-driven answers to relevant marketing questions

This course is a natural continuation of the Machine Learning course. It provides guidelines for running a Data Mining process and then discusses, with practical examples, the complete pipeline from data to machine learning concepts.
In particular, the following topics are covered:

• The functions of Data Mining.
• Supervised and unsupervised learning.
• Algorithms and methods for constructing classification models.
• Clustering algorithms.
• Algorithms for discovering association rules.
• Methods for evaluating the quality of data mining results.

Principles are introduced in class with presentation of slides and stimulating discussions with students. Methods are then applied with laboratory exercises.

The course is focused on understanding of the opportunities arisen from digital transformation, in terms of innovative business models for companies and new value for users. Specifically, the course will deepen technology forecasting and technology roadmapping methodologies, applied to emerging digital trends, focusing on Fintech, IoT and Artificial Intelligence.

Meoli Azzurra

The digitization of the economy is one of the most relevant issues of our time. The objective of this course is to analyze how digital economy has fundamentally challenged traditional business models and created new business opportunities. We will start with a brief introduction of the digital economy and then analyze the specific business strategies adopted by the different players in this ecosystem, specifically platforms. We will also discuss the implications for public policy and regulation. In particular, we will consider whether new business practices and contracts in the digital economy are beneficial or detrimental to society. Real case studies related to Amazon, Airbnb, Booking.com, Facebook, Google, Uber, and others, will be analyzed.

The course focuses on the finance aspects following the life cycle of young and innovative ventures from their start-up. These young ventures usually require substantial outside financing in early stages to eventually create employment, growth, social contributions and tax revenues in their future. Bank financing is hardly available for these ventures and therefore, all funds need to be raised from other sources, e.g. friends and family, business angels, or from professional financial intermediaries, so-called venture capital and private equity funds. Besides those traditional channels, ventures can raise funds from “the crowd” using digital platforms (crowdfunding) or blockchain-based technologies (ICOs).

Artificial intelligence (AI) is a discipline whose goal is to realize this dream through the use of a wide variety of techniques, from logic-based symbolic computation to sub-symbolic models inspired by the structure of the brain, such as neural networks. This course will provide an introductory overview of the various existing artificial intelligence techniques, focusing on industrial applications, discussing future challenges and opportunities, and also addressing some of the social, economic, and ethical implications.

The aim is to provide basic knowledge concerning the main concepts and principles of computer security (e.g., risk, tools for risk assessment and evaluation, attacks and their typical structure, resources, functional systems requirements, human component). In this way, the course will provide the basic tools for the design and implementation of reasonably secure systems. During this process, either methodological, technological and behavioral aspects (i.e. operation security) need to be considered.

The course analyzes the legal framework of the rights of the person when dealing with technology, the protection of personal data, the intersection between person and market in the face of technological platforms and big data, the normative references of GDPR, Digital Service Act and other legal formats with a global vocation. Attention will be paid to the ethical challenge underlying the balance between human intervention and artificial intelligence, to the comparison between legal language and algorithms from the applicability of models to the role of law and regulation in the face of technological change and to the governance and risk management of data.
Some insights concern automated decisions, profiling in the credit and in the insurance sector and the coexistence between incumbents and new entrants in the financial sector.

This course provides an introduction to the basic principles and methods of Data Mining and Machine Learning, with emphasis on Classification, Clustering, Association Rules, Outlier discovery. Analysis of the main problems related to data quality and data transformation. Python will be used as part of the course to solve machine learning problems.

Neural networks are a class of machine learning algorithms, originally inspired by the brain, structured in layers of interconnected artificial neurons.

The network can be trained on data to optimize its connections to a specific task.

Deep neural networks, that is networks with multiple internal (so called hidden) layers, have recently seen a lot of success at practical applications. They’re at the heart of production systems at companies like Google and Facebook for image classification, speech recognition, natural language processing, language understanding or robotics.

The course gives an overview of the foundational ideas and the recent advances in neural nets, explaining the potentialities of the topic for practical purposes. We shall cover supervised and unsupervised techniques, methods for visualizing and understanding the behavior on neural nets, as well as adversarial techniques to fool them. We shall also hints to recent applications in the field of reinforcement learning, and some amazing results in game simulation.

The course will detail typical operations management issues by taking a quantitative approach for supporting decision-making processes. The main topics covered in the course will focus on the modelling and simulation of the organizational processes aimed at supporting companies’ strategy implementation. In particular, the course grounds on the principles of system thinking to help participants to model and to understand the dynamic interplay among people, decisions and resources involved in companies’ operations. The aim of the course is to hone decision-makers’ capabilities to capture the deep causal structure that generates events and phenomena thereby foreseeing long-term consequences of decisions and actions. In the course, participants will learn how to build a computer model of complex organizational processes in order to conduct “what-if” and scenario analysis.

The course aims to explore the principles of financial statement analysis. It provides the participants with a framework to understand how businesses’ value and risks are captured in financial statements and price them correctly.

This course emphasizes statistical methods useful for tackling modern-day data analysis problems. A special attention is dedicated to techniques that help managers to make intelligent use of databases by recognizing patterns and making predictions.

The students will develope skills to:
• plan a statistical data analysis process
• manage a data source
• choose the best method to analyze the data
• implement the analysis and interpret the results

The goal of the course unit is to present theoretical and practical aspects of text mining regarding text classification and sentiment analysis & opinion mining.

The learning outcomes are technical capabilities of coping with text processing, text classification, sentiment analysis & opinion mining, transfer learning, using also deep neural networks, language models and explanation text mining.