Summer School

2021 Edition
Aim and Organization

As part of its activities, the MSc in Data Science organizes a yearly summer school to acquire valuable credits to qualify for the enrollment into the programme.

Key information

When: 12/07/2021 – 4/08/2021

Schedule:  Download the Summer School 2021 Schedule

Format: The Summer School is organized in 6 modules taking place over a four-weeks period

Teaching Language: All courses will be in ITALIAN

How to apply: Fill in the ONLINE FORM

Registration deadline:
– Module 1 – Mathematics and Statistics: 15/07/2021 h. 23.59 CEST
Module 1 – Economic, Psychological and Sociological Sciences
15/07/2021 h. 23.59 CEST
– *ALL Modules 2*: 22/07/2021 h. 23.59 CEST

Maximum number of courses: Students can attend up to 4 modules (max. 2 modules ‘1’ and  max. 2 modules ‘2’)

Course Prices:

Modules ‘1’have an individual fee of 170€

Modules ’2’ havean individual fee of  80€

Course Payment: *exclusively* by credit card through the online form

Attendance requirements: Attendance is mandatory. In order to qualify for final exams and/or obtain an attendance certificate, students must be present at least 15 out of the 18 hours that are scheduled for each course.

CFU Recognition: Credits obtained upon completion of Summer School courses are recognized as follows:
– For undergraduate students from the Departments of Economics, Industrial Engineering, Information Engineering and Computer Science, and Matematics: CFUs are recognized as extra-numerary (i.e., crediti sovranumerari);
– For undergraduate students from the Departments of Psychology and Cognitive Science, Sociology and Social Research: only for undergraduate students, CFUs are recognized as free choice credits (i.e., crediti a scelta libera)
– For students of the MSc in Finance (DEM): only CFU obtained with the Computer Science Module of the Summer School are recognized as free choice credits (i.e., crediti a scelta libera)
– All other attendees will receive a certificate attesting the successful completion of courses. Requests for the recognition of Summer School CFUs should be addressed to the Department they belong to.

Admissions requirements: There are no prerequisites for participating. Summer School courses are open to students who own only some of the credits that are necessary to enroll in the Data Science MSc or just want to refresh their knowledge in the courses areas. Please note that, due to the online format, some modules may request participants to perform a few preliminary preparation steps. Check out single modules’ description to find out whether this applies to courses you have selected.

Summer School 2021 Modules

MODULES 1
(Monday 12 July 2021 – Saturday 24 July 2021)

Module 1 – Computer Science
(weeks #1 AND #2: 12/07/2021 – 23/07/2021)
18 Hrs, 3 CFU INF/01

Title: Foundations of Python Programming

Contents

– Tools, basics (variables, expressions,commands)
– Strings
– Lists
– Tuples, Sets
– Dictionaries
– Conditionals, for loops, while

Suggested Readings: 

– SoftPython, Fondamenti: 
https://softpython.readthedocs.io/it/latest/index.html#A—Fondamenti
– Allen Downey, Pensare in Python Italiano–2nda edizione: 
https://github.com/AllenDowney/ThinkPythonItalian/raw/master/thinkpython_italian.pdf
– Nicola Cassetta, Tutorial Python: 
http://ncassetta.altervista.org/Tutorial_Python/index.html

Teaching mode: Classes will be streamed live. Access information to live streams will be made available on the Moodle page for this Module.

Final test: The final test will consist of a written exam. More detailed information on the time and the modes/platform to perform the test will be provided by the instructor upon course beginning.

Module requirements:

Computer: It is necessary to have a desktop or laptop computer (not a tablet!) with:
– at least 4GB RAM
– at least 5GB free of hard drive
– Operating System: Windows 8 or more recent, 64-bit macOS 10.13or more recent,Linux (any distribution)

Software: Before partecipating, you are strongly advised to installthe following software:
– Anaconda forPython 3.7:  download page (if you haveless than 5GB free, install Miniconda)
– LibreOffice 6.3.6:  download page
– Browser:  anyamong ChromeFirefox o Safari (InternetExplorer / Edge is not for Data Scientists…)

Software Videocall Check (extra): a software videocall will take place to ensure everybody owns the necessary software and it is working
properly. Dates and Times for appointments will be communicated to enrolled students.

Module 1 – Mathematics and Statistics
(week #2: 19/07/2021 – 24/07/2021)
18 Hrs, 3 CFU for any of MAT/, SEC-S/

Title: Linear Algebra

Contents: 

– The set R^n. Vectors in R^n. R^n as a vector space.
Definition of a matrix. The determinant of a matrix. Inverse matrix. Rank of a matrix.
Systems of linear equations. Solutions of a system of equtions: Rouche’-Capelli theorem and Cramer theorem.
– Functions: What is a function. General notions on functions. The main elementary functions. The equation of the line. The parabola. The equilateral hyperbola. The power function. The exponential function. The logarithm function.
Intro to statistics. Graphical representation of data. Arithmetic mean, median, mode. Measures of dispersion.

Suggested Readings: A. Guerraggio (2018). Matematica per lescienze. Person EducationLimited

Teaching Mode: Classes will be streamed live.Access information to live streams will be made available on the Moodle page for this Module

Final Test: The final test will consist of an oral exam. More detailed information on the time and the mode/platform to perform the test will be provided by the instructors upon course beginning.

Module 1 – Economic, Psychological and Sociological Sciences
(week #2: 19/07/2021 – 24/07/2021)
18 Hrs, 3 CFU for any of SPS/, SECS-P/, M-PSI/

Title: An introduction to SPSs

Syllabus

– Introduction to economics (hours 6): Individual decisions and system behaviour: concepts and models; Production costs: definitions and interpretative models; The market: supply and demand; Big data and economics: introduction and case studies. Presentation of case studies and studies  on selected topics.

– Introduction to psychology (hours 6): A selection of some elementary concepts about psychology are presented in a simple and integrated fashion. Basic descriptions of psychological sectors (e.g., personality psychology, organizational psychology, psychometrics, clinical psychology) and basic definitions of psychological constructs/variables (e.g., motivation, individual differences, workengagement, personality traits, burnout) are illustrated within an integrated theoretical-methodological approach.

– Introduction tosociology (hours 6): This module will discuss some of the main research traditions of sociological research with particular emphasis on analytical and structural sociology, and digital sociology. We will discuss some of the main objects of sociological research, how they have been operationalized and the challenges involved.

Suggested Readings:

– C.W.L. Hill,(2011) International Business: Competing in the global marketplace, (8th ed.),NY
– Baldwin, R. E.,& Evenett, S. J. (2015). Value creation and Trade in 21st Century Manufacturing, Journal of Regional Science, 55(1), 31-50.
– Chesbrough E.(2003) Open Innovation, Harvard Business School Press, Boston, Massachusetts.
– Mankiw, N. G. (2020). Principles of economics. Cengage Learning.
– Goldthorpe, J. H. (2016). Sociology as a population science. Cambridge: Cambridge University Press.
– Marres, N. (2017). Digital Sociology: The reinvention of social research. Cambridge: Polity Press.
– Veltri, G.A. (2020). Digital Social Research. Cambridge: Polity.

MODULES 2
(Monday 26 July 2021 – Saturday 7 August 2021)

Module 2 – Computer Science
(weeks #3 AND #4: 26/07/2021 – 6/08/2021)
18 Hrs, 3 CFU INF/01

Title: Foundations of Algorithms in Python

Contents: 

– Functions
– Error handling and testing
– Composite data structures, matrices as list of lists
– Matrices – numpy
– Sequences, reading from file
– Application: databases

Suggested Readings:

– SoftPython, Fondamenti: 
https://softpython.readthedocs.io/it/latest/index.html#A—Fondamenti
– SoftPython, Formati dati: 
https://softpython.readthedocs.io/it/latest/formats/formats-sol.html
– SoftPython, Database:
https://softpython.readthedocs.io/it/latest/database/database-sol.html
– Allen Downey, Pensare in Python Italiano– 2nda edizione:  https://github.com/AllenDowney/ThinkPythonItalian/raw/master/thinkpython_italian.pdf
– Nicola Cassetta, Tutorial Python: http://ncassetta.altervista.org/Tutorial_Python/index.html

Teaching mode: Classes will be streamed live. Access information to live streams will be made available on the Moodle page for this Module.

Final test: The final test will consist of a written exam. More detailed information on the time and the modes/platform to perform the test will be provided by the instructor upon course beginning.

Module Requirements:

Computer: It is necessary to have a desktop or laptop computer (not a tablet!) with:
– at least 4GB RAM
– at least 5GB free of hard drive
– Operating System: Windows 8 or more recent, 64-bit macOS 10.13or more recent,Linux (any distribution)

Software: Before partecipating, you are strongly advised to installthe following software:
– Anaconda forPython 3.7:  download page (if you haveless than 5GB free, install Miniconda)
– LibreOffice 6.3.6:  download page
– Browser:  any among ChromeFirefox o Safari (InternetExplorer / Edge is not for Data Scientists…)

Software Videocall Check (extra): a software videocall will take place to ensure everybody owns the necessary software and it is working
properly. Dates and Times for appointments will be communicated to enrolled students.

Module 2 – Mathematics and Statistics
(week #3: 26/07/2021 – 31/07/2021)
18 Hrs, 3 CFU for any of MAT/, SEC-S/

Title: Introduction to Statistics

Contents: 

– Data: statistical units, variables, and measurement scales. Graphical methods, tabular tools, and numerical measures for univariate and bivariate descriptive analysis.
– Introduction to probability: basic concepts and definitions, random variables, and probability distributions. Sampling and sampling distributions. Introduction to the inferential statistics: interval estimation and hypothesis test.

Suggested Readings: Newbold, P.(2019),  Statistics for Business and Economics Eighth Edition (GlobalEdition), Pearson Education Limited.

Teaching Mode: Classes will be streamed live. Access information to live streams will be made available on the Moodle page for this Module

Final Test: The final test will consist of an oral exam. More detailed information on the time and the mode/platform to perform the test will be provided by the instructors upon course beginning.

Module 2 – Economic, Psychological and Sociological Sciences
(week #3: 26/07/2021 – 31/07/2021)
18 Hrs, 3 CFU for any of SPS/, SECS-P/, M-PSI/

Title: Modeling and Methodology

Contents: 

– Introduction tomanagement (hours 6): Porter’s scheme: Competitive dynamics and strategies;Open and closed innovation: comparison paradigms; the value chain: companyboundaries and international dynamics; Discussion of some cases on the conceptstreated

– Introduction toquantitative methods in psychology (hours 6): We will cover the mainquantitative methods used in psychological research, with a particular focus tothe study of individual differences. Topics will cover: the concepts ofreliability and validity; the measurement of psychological unobservablephenomena through latent variable statistical approaches; the impact of datascience advancements in psychological research.

– Introduction tosociological methodology (hours 6): We will cover the main epistemological andmethodological aspects of research with social data using quantitative,qualitative and mixed methods. We will focus more on quantitative methods, inparticular on the nature of data available, the construction and use of validresearch instruments and the different methods of analysis applied to socialdata.

Suggested Readings:

– C.W.L. Hill,(2011) International Business: Competing in the global marketplace, (8th ed.),NY
– Baldwin, R. E.,& Evenett, S. J. (2015). Value creation and Trade in 21st Century Manufacturing, Journal of Regional Science, 55(1), 31-50.
– Chesbrough E.(2003) Open Innovation, Harvard Business School Press, Boston, Massachusetts.
– Mankiw, N. G. (2020). Principles of economics. Cengage Learning.
– Goldthorpe, J. H. (2016). Sociology as a population science. Cambridge: Cambridge University Press.
– Marres, N. (2017). Digital Sociology: The reinvention of social research. Cambridge: Polity Press.
– Veltri, G.A. (2020). Digital Social Research. Cambridge: Polity.

Teaching Mode: Classes will be streamed live. Access information to live streams will be made available on the Moodle page for this Module

Final Test: The final test will consist of an oral exam. More detailed information on the time and the mode/platform to perform the test will be provided by the instructors upon course beginning.