Summer School 2020

Where: All learning modules will be held ONLINE. More detailed information on each module’s online organization is provided below.

When: 20/07/2020 – 1/08/2020

Schedule: Download the 2020 Summer School schedule

Format: The Summer School is organized over a two-weeks period. Every week hosts 3 modules

Teaching Language: All courses will be held in ITALIAN

How to enroll: Fill in the ONLINE FORM available on the UniTrento website.

Maximum number of courses: Students can attend up to 4 courses ( max. 2 during week #1; max. 2 during week #2)

Course Prices:

  • Modules held in week 1 have an individual fee of 170€;
  • Modules held in week 2have an individual fee of  80€

Course Payment: The overall cost of selected module can be paid *exclusively* by credit card through the online form

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 or just want to refresh their knowledge in the areas of informatics and/or mathematics/statistics and/or socio-psych-economics.  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 case applies to courses you have selected.

MODULE 1. MATHEMATICS AND STATISTICS

(Hours 18, 3 CFU, MAT/, SECS-S/)

Title: Linear algebra
Lecturer: Mario Lauria (University of Trento)
Syllabus:
  • 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 le scienze. Person 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: COMPUTER SCIENCE

(Hours 18, 3 CFU, INF/01)

Title: Foundations of Programming in Python
Instructor: David Leoni (University of Trento)
Syllabus:
  • Tools, basics (variables, expressions, commands)
  • Strings
  • Lists
  • Tuples, Sets
  • Dictionaries
  • Conditionals, for loops, while
Suggested readings:
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 install the following software:

  • Anaconda for Python 3.7:  download page    (if you have less than 5GB free, install Miniconda)
  • LibreOffice 6.3.6:  download page
  • Browser:  any among ChromeFirefox o Safari ( Internet Explorer / Edge is not for Data Scientists…)
Software Videocall Check (extra):

On Thursday 16 July 2020, from 9:00 to 12:00 a videocall will take place to ensure everybody has the necessary software properly working (we just test software, you don’t need to participate for entire 3 hours).  We strongly advise you to arrive to the call with the software already installed. In case somebody cannot join, send an email to david.leoni@unitn.it and we will schedule some other moment.

MODULE 3: ECONOMIC, PSYCHOLOGICAL AND SOCIOLOGICAL SCIENCES

Title: An Introduction to SPSs
Lecturers: Roberto Gabriele (University of Trento), Enrico Perinelli (University of Trento), Elena Pavan (University of Trento)
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, work engagement, personality traits, burnout) are gently illustrated within an integrated theoretical-methodological approach.

Introduction to sociology (hours 6): This module will discuss some of the main research traditions of sociological research with particular emphasis on analytical and structural sociology, which aims at understanding establishing and explaining probabilistic regularities in human populations, 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.
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.

Week 2
27 July – 1 August 2020

MODULE 4. MATHEMATICS AND STATISTICS

(Hours 18, 3 CFU, MAT/, SECS-S/)

Title: Introduction to Statistics
Lecturer: Filippo Trentini (Fondazione Bruno Kessler)
Syllabus:
  • 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 textbook

Newbold, P. (2019),  Statistics for Business and Economics Eighth Edition (Global Edition), 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 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 5: COMPUTER SCIENCE

(Hours 18, 3 CFU, INF/01)

Title: Foundations of Algorithms in Python
Instructor: David Leoni (University of Trento)
Syllabus:
  • Functions
  • Error handling and testing
  • Composite data structures, matrices as list of lists
  • Matrices – numpy
  • Sequences, reading from file
  • Application: databases
Suggested readings:
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 install the following software:

  • Anaconda for Python 3.7:  download page    (if you have less than 5GB free, install Miniconda)
  • LibreOffice 6.3.6:  download page
  • Browser:  any among ChromeFirefox o Safari ( Internet Explorer / Edge is not for Data Scientists…)
Software Videocall Check (extra):

On Thursday 16 July 2020, from 9:00 to 12:00 a videocall will take place to ensure everybody has the necessary software properly working (we just test software, you don’t need to participate for entire 3 hours).  We strongly advise you to arrive to the call with the software already installed. In case somebody cannot join, send an email to david.leoni@unitn.it and we will schedule some other moment.

MODULE 6: ECONOMIC, PSYCHOLOGICAL AND SOCIOLOGICAL SCIENCES

Title: Modelling and methodology
Lecturers: Roberto Gabriele (University of Trento), Enrico Perinelli (University of Trento), Giuseppe Veltri (University of Trento)
Syllabus:

Introduction to management (hours 6): Porter’s scheme: Competitive dynamics and strategies; Open and closed innovation: comparison paradigms; the value chain: company boundaries and international dynamics; Discussion of some cases on the concepts treated

Introduction to quantitative methods in psychology (hours 6): We will cover the main quantitative methods used in psychological research, with a particular focus to the study of individual differences. Topics will cover: the concepts of reliability and validity; the measurement of psychological unobservable phenomena through latent variable statistical approaches; the impact of data science advancements in psychological research.

Introduction to sociological methodology (hours 6): We will cover the main epistemological and methodological aspects of research with social data using quantitative, qualitative and mixed methods. We will focus more on quantitative methods, in particular on the nature of data available, the construction and use of valid research instruments and the different methods of analysis applied to social data.

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, Principles of Economics,
  • Mankiw, N. G. (2020). Principles of economics. Cengage Learning.
  • Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605-634.
  • Bleidorn, W., & Hopwood, C. J. (2019). Using machine learning to advance personality assessment and theory. Personality and Social Psychology Review, 23(2), 190-203.
  • 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.