Corso di laurea: Data Science Programmazione per l'A.A. 2022/2023

Introduction to programming



The general structure of a digital computer.


Problems and Algorithms.


Flow chart, Structured linear notation, Böhm-Jacopini theorem.


Representation of information: integers and floating-point numbers, characters, strings.




Computational styles



"Turing-able" programming languages vs spreadsheets;


Basic cycle of computation in a spreadsheet: variables (value, format, dependencies)


Compilation and interpretation for traditional programming languages.


Installation of the development environment for the Python language. First program: Editing, Running, Debugging.




Constructs of Python language



Basic syntax, data types, predefined operators, I/O management.


Numbers and mathematical functions.


Flow control: consructs of selection and iterative.


Functions and recursion.




Built-in data structures in Python



Strings.


Lists, Tuples, Dictionaries.




Object oriented programming in Python



General ideas


Basic notation


Examples of simple objects and their usage;


Ereditariety



Advanced topics


Notable algorithms: Searching, Sorting, Merging. Basics of computational complexity.


Modules. Basic Python libraries for scientific computing and data analysis. Basic data visualization.

1) A.Downey, Think Python, 2nd Ed., Grean Tea Press (online available).
2) M.Lutz, Learning Python, 4th Ed., O'Reilly (online available).
3) D.Pine, Introduction to Python for Science and Engineering, SMTEBooks - CRC Press (online available).

4) Jessen Havill - Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming Chapman and Hall/CRC; 1 edizione (14 settembre 2015)

Statistical Models for Univariate Random Variables.Discrete and continuous random variables. Basic distribution functions. Expectation and variance. Statistical models for random variables. Parametric Inference: classical properties of estimators; the maximum likelihood approach and its properties. Goodness-of-fit tests.R functions and packages. Illustration in R.(Slides)Basics of Matrices.Matrices. Special matrices. Basic matrix identities. Trace. Inverse and determinant. Eigen-decomposition. Quadratic forms and definite matrices. (Bishop 2007, Appendix C)Basics of Multivariate Modelling.Random vectors and their distributions. Mean vector, covariance, and correlation matrices. Multivariate normal distribution: properties and effect of the covariance matrix on the shape of the contours. Data Matrix, centered data matrix, and standardized data matrix. (McNeil, Frey, and Embrechts 2005, Chapter 3)Principal Component Analysis (PCA).The goalof PCA. PCA as a tool for data visualization. Definition of principal components (PCs). PCA and Eigen-decomposition. Computing PCs. PCA: Geometrical interpretation. Choosing the number of PCs. Biplot. Illustration of PCA in R.(James G., Witten D., Hastie T., Tibshirani R. 2017, Chapter 10)Cluster Analysis (CA).Clustering distance/dissimilarity measures. Data types in CA. Data standardization. Distance matrix computation. R functions and packages. (Kassambara 2017, Chapter 3)Hierarchical clustering methods.Peculiarities. Agglomerative hierarchical clustering. Algorithm. Dendrogram. Linkage methods. Simplified example. Agglomerative hierarchical clustering methods using the data matrix. Illustration in R. (Kassambara 2017, Chapter 7)Partitioning (or partitional) clustering methods.Peculiarities.K-means clustering. Algorithm. R functions and packages. Illustration in R.K-medoids clustering. PAM Algorithm. R functions and packages. Illustration in R. (Kassambara 2017, Chapters 4–5)Cluster Validation.Overview. Assessing Clustering Tendency. R functions and packages. Illustration in R. Determining the Optimal Number of Clusters. R functions and packages. Illustration in R. Cluster Validation Statistics: Internal and external measures. R functions and packages. Illustration in R. Choosing the Best Clustering Algorithm(s). Measures for comparing clustering algorithms. Cluster stability measures. R functions and packages. Illustration in R. (Kassambara 2017, Chapters 11–14)Model-Based Clustering.Preliminaries. Mixture models. Clustering with mixture models. Maximum a posteriori probability criterion. Gaussian mixtures. Parsimonious modeling via eigendecomposition. Choosing the number of mixture components and the best parsimonious configuration: the Bayesian information criterion. R functions and packages. Illustration in R. (Kassambara 2017, Chapter 18)

·
Bishop C.
M. (2007). Pattern Recognition and
Machine Learning, Springer, Cambridge.

·
Hastie T., Tibshirani R., Friedman
J. (2008). The Elements of Statistical Learning, Springer, New York.

·
James G.,
Witten D., Hastie T., Tibshirani R. (2017). An
Introduction to Statistical Learning with Applications in R, Springer, New
York.

·
Kassambara
A. (2017). Practical Guide to Cluster
Analysis in R.

·
McNeil A. J.,
Frey R., Embrechts P. (2005). Quantitative
Risk Management Concepts, Techniques and Tools. Princeton University Press,
Princeton, New Jersey.

Fundamentals of Database Management Systems (DBMS)


Relational Model: basic concepts, integrity constraints and keys.


SQL language: data definition, data modification, queries, views, transactions.


NO-SQL database: MongoDB

R. Elmasri and S. Navathe, "Fundamentals of Database Systems", 7th Edition, Pearson, 2016.


Instructor’s notes

Use of the statistical software in R regarding:

Descriptive Statistics. Simple Statistical Distributions. Data tables. Frequency distributions. Main summary statistics: arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variance, standard deviation, relative variation. Graphical representations. Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Covariance and correlation.

Probability.Random number generation and data modeling according to different probability distributions: uniform, binomial, Poisson, Gaussian.

Statistical inference.Sample distributions: Student-t, chi-square. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions. Hypothesis testing. Null hypotheses and alternative hypotheses. P-values. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions.

Statistical models.The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model.

Documents available on the web page of The R Project for Statistical Computing: https://www.r-project.org and other resources available on the web.

Context and background of European data protection law; data protection terminology, principles and rules; data subjects’ rights and their enforcement; international data transfer and flows of personal data; specific types of data and their relevant protection rules; modern challenges in personal data protection (big data, algorithms and artificial intelligence); data and contracts on line.

C. Giakoumopoulos - G. Buttarelli – M. O’Flaherty, Handbook on European data protection law, 2018 edition, EU publications; (and)-Handouts provided by the teacher.

DETAILED COURSE CONTENT

The course focuses on economic, strategic and technological dimensions of designing and developing digital solutions and business models to shape and lead technological and social impacts taking place in distributed innovation networks and ecosystems.

The managerial perspective emphasis the different organizational role at every level, e.g. project manager, team manager, functional manager, general manager, CEO, Founder, Startupper, that are involved in digital innovation and transformation enhancing the company competitiveness.

Currently, organizations with successful transformations deploy more technologies than others developing digital innovation, building managerial capabilities and planning talent development practices in the following critical technological areas of interest of the course: traditional web technologies, cloud-based services, mobile internet technologies, big data and big data architecture (eg, data lakes), internet of things, design thinking, artificial-intelligence tools, robotics (eg, robotic process automation), advanced neural machine-learning techniques (eg, deep learning), augmented-reality technologies, additive manufacturing (eg, 3-D printing).

In details the course will help you learn:


the business, economic and technological dimensions to manage strategically the digital revolution;
the entrepreneurial trade-off between existing business models and new digitally enhanced emphasizing platforms and ecosystems;
the competitive collaborations among companies with different digital business models;
how to shape and lead innovative solutions (product and service) in the digital contest.


An important part of the course are the several happenings and seminars, which will bring together leading practitioners at the cutting edge of digital technologies to discuss emerging issues.

TEXTBOOK INFORMATION

Textbook:BRESCIANI S., FERRARIS A., ROMANO M., SANTORO G. (2021). Digital Transformation Management for Agile Organizations. p. 1-216, Howard House, Wagon Lane, Bingley BD16 1WA, UK:Emerald Publishing Limited, ISBN: 9781800431720.

Reading Materials will be announced in class and electronically.

1. Introduction to Business Intelligence and Big Data AnalyticsGoal and rationale of BI systemsThe value of knowledge - data driven decision makingThe structure and evolution of BI and Big Data analytics systemsOLAP vs OLTPData warehouse and Business intelligenceAdvanced tools and platforms for BI and analytics2. Data models for data warehouseConceptual modelingDimensions and factsMulti-dimensional data modelConceptual, logical and physical design3. BI ArchitectureETL (extract, transform and load) functionalitiesOLAP analysisOLAP queryReporting and Interactive DashboardOverview on commercial and open-source BI platforms4. Data VisualizationIntroduction to VisualizationData Visualization fundamentals: Visual Perception and Preattentive AttributesCharts and standard views: relevance, appropriateness and best practicesUse of colors in data visualizationDashboard DesignAdvanced and innovative tools for data visualization: the Tableau platform

[GoRi] Golfarelli, Rizzi.Data Warehouse Design: Modern Principles and Methodologies, McGraw Hill[Dash] Steve Wexler, Jeffrey Shaffer, Andy Cotgreave.The Big Book Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley (2017)[Few1] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten, 2nd edition, Analytics Press (2012)[Few2] Stephen Few. InformationDashboard Design: Displaying Data for At-a-Glance Monitoring, 2nd edition, O’Reilly Media (2013)[Notes] Instructor’s notes (published onStudiumand/or the Microsoft Teams platform)

Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.Classification. Logistic regression; parameter estimation. Linear and quadratic discriminant analysis.Resampling methods. Cross-validation, Bootstrap.Tree-based Methods. Regression Trees and Classification Trees. Bagging, Random Forest, BoostingDeep learning. Single layer networks and multilayer networks. Fitting a neural network.

1. James G., Witten D., Hastie T., Tibshirani R. (2021). An Introduction to Statistical Learning with Applications in R, 2nd Edition, Springer, New York.2. Hastie T., Tibshirani R., Friedman (2008). The Elements of Statistical Learning, Springer, New York3. Course notes

Linear Programming (LP)(about 18h)
LP models; Graphical method; Simplex method; Duality; Sensitivity analysis

Integer Linear Programming (ILP)(about 9 h)
Branch & Bound method; 0-1 programming; Knapsack problem

Software(about 5 hours)Excel,Matlab, Mathematica,

Network problems(about 8 h)Graphs (Kruskal, Dijkstra)

J. Stacho, Introduction to Operations Research, Columbia University, NY,http://www.cs.toronto.edu/~stacho/public/IEOR4004-notes1.pdfM.S. Bazaraa, J.J. Jarvis, H.D. Sherali, Linear Programming and Network Flows, John Wiley & Sons, 2009.F. Hillier, G.J. Liebermann, “Introduction to Operations Research”, McGraw-Hill, 2006Matoušek-Gärtner: Understanding and using linear programming, Springer 2007