Data Science and Business Analytics

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 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.

This course explores the following topics: Business intelligence (BI), motivations, objectives, definitions and solutions. Data warehousing as an enabling technology for BI: architectures, techniques and methodologies of On-Line Analytical Processing (OLAP) for data analysis. Multidimensional modeling with the Dimensional Fact Model (DFM). Self-service BI platforms. Relational OLAP. What-if analysis. Laboratory exercises on OLAP, DFM and self-service BI.

The new priorities of marketing management: the marketing metrics and dashboard indicators of marketing.  From the value for the customer to the value of the customer: customer value, satisfaction, loyalty and customer equity. Analysis, measurement and management. Customer Relationship Management: acting on acquisition, retention and development. Big Data and Customer Insight Management. Customer insights and Big Data for improving marketing decisions and management. Marketing Modeling and Marketing Analytics.

Models and methods for knowledge extraction from databases. Study of different types of data and methods of pre-treatment. 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. Data mining laboratory in order to apply the studied methods.

This course provides students with a deep understanding of the following topics: the reference model. The analytics for understanding the pattern and the business dynamics of an enterprise. Business process analytics, market, custodian and supply chain analytics; analytics of competitors. Financial cost and revenues analytics. The use of business analytics in the design of systems for measuring the company’s performance.

This course explores the following topics: Univariate analysis: Distributions of quantitative and qualitative traits. Summary indicators: average values and variability indices. Data transformations. Graphical representations. Association between variables: Connection, covariance, correlation. The linear regression model. Elements of probability calculation: Probability (Binomial, Poisson, Normal). Signals of statistical inference: confidence intervals and hypothesis testing. Analysis of multidimensional data: Matrices of data, variance and covariance, correlation and dissimilarity. Laboratory exercises with dedicated software.

The course will explore and analyze typical problems associated with operations management and address them through quantitative modeling in support of decision making. The issues will be handled by combining a business-oriented approach with quantitative methods on a statistical and econometric basis. The main contents are related to: management of production capacity, inventory management and control, service operations, selection and monitoring of suppliers, process optimization and simulation. The main objectives of the course relate to the ability to model complex business situations and to analyze them through technical and quantitative tools designed to monitor and improve business performance, teams and individuals.

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 the capability acquisition of cooping with problems of text classification and sentiment analysis & opinion mining

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.

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 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).

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.

Since the first mechanical automations of the 17th century, one of the great dreams of the humanity has been the construction of machines exhibiting human behaviors and intelligence. Artificial intelligence (AI) is a discipline whose goal is to realize this dream by using the most different techniques, from symbolic computation based on logic to sub-symbolic models inspired by the structure of the brain, such as neural networks.

The last few years have witnessed a sensational exploit of AI applications in many different fields – with a relevant growth of investments – inducing many experts to believe that the dream will come true in a short time.

This is inducing many companies to reconsider their business strategies and, more generally, requires a deep rethinking of several crucial aspects of our society.

The course will provide an introductory overview of the various existing AI techniques, focusing on their industrial applications, discussing future opportunities and challenges, touching also on some social, economic and ethic implications.

It’s undeniable that mankind strive to build unbreakable systems, even if human history has demonstrated so far that this is nothing more than a myth. The real world is mainly made of fragile systems, so that reality is much different from the expectations.

The goal of this course is to provide a 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 (i.e. operation security) aspects will be considered.

After the course, the students should be able to critically evaluate the security of a (computer) system considering both the system as a whole and its main separated components (e.g. authentication and authorization mechanisms and procedures). They will also be able to find the main vulnerabilities of such a system and to identify the more appropriate countermeasures to allow the mitigation of vulnerabilities and reduce the risk to an acceptable level.

The goals described above will be obtained not only by teaching some of the main theoretical aspects of computer security but also by describing and discussing many real world examples in detail.

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 course provides an overview on the Machine learning field, with a particular emphasis on
symbolic learning techniques (sub symbolic techniques will be covered by the course on Neural Networks).

The main learning problems and paradigms will be introduced along with examples explaining how to
use the learned models into decision support systems.

Application areas that will be covered have industrial impact in fields as automotive, energy management,
predictive maintenance and policy making.
The course will cover both lectures and hands-on sessions.

The digital revolution has led to an explosion in the amount of available data and information (Big Data), the ability to analyze and correlate this information is becoming a fundamental element to an enterprise’s competitiveness and a new source of economic value and innovation. Big Data, however, is unable to be managed properly by using traditional instruments of Information Technologies because of their size, their heterogeneity and their many peculiarities. For this reason, new architectures, methodologies and tools are being created for data scientists to transform this data into value for the company and to support the decision-making process. This course will analyze the various types of data available, technical access to information, methods to handle them and the architectures and tools to store and process them.

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.