Summer School 2019

Where: Department of Sociology and Social Research, via Verdi 26, Trento; COMPUTER LAB #3, Ground Floor

When: 15/07/2019 – 27/07/2019

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 (2 during week #1; 2 during week #2)

Course Prices: Courses held in week 1 will cost 170€; Courses held in week 2 will cost 80€

Course Payment: The overall cost of selected courses will 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.

Week 1
15 July – 20 July 2019

MODULE 1: MATHEMATICS & 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 textbook

“Matematica per le scienze” di A. Guerraggio.

MODULE 2: COMPUTER SCIENCE

(Hours 18, 3 CFU, INF/01):

Title: Foundations of Programming in Python
Lecturer: M. Luca 
Syllabus:
  • What is a program, variables, expressions and statements;
  • Functions, conditionals and recursion;
  • Functions with return value, iteration;
  • Strings;
  • 5. Dictionaries, tuples;
  • 6. Case study: data structure selection, files.

MODULE 3: ECONOMIC, PSYCHOLOGICAL, AND SOCIOLOGICAL SCIENCES

(Hours 18, 3 CFU, SPS/, SECS-P/, M-PSI/*):

Title: An Introduction to SPSs
Lecturers: Giuseppe Veltri (University of Trento), Luigi Lombardi (University of Trento), Roberto Gabriele (University of Trento), Enrico Perinelly (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, models; Presentation of examples on the topics covered.
  • Introduction to cognitive representations (hours 6): A selection of some elementary concepts about cognitive psychology and cognitive architectures are presented in a simple and integrated fashion. Basic definitions for psychological dimensions such as, for example, perception, motion, and action as well as learning, memory, judgement and decision making are gently illustrated within a cognitive approach.
  • Introduction to sociology (hours 6): This module will discuss some of the main research tradition of sociological research with an emphasis on analytical and structural sociology that aims at understanding establishing and explaining probabilistic regularities in human populations. We will discuss some of the main object of sociological research and how they have been operationalized into empirical research including the challenges involved.
Suggested textbooks
  • 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 G. (2014) 7 ed., South-Western PubPsychology
  • M. W. Eysenck & M. T. Keane (2015). Cognitive Psychology A Student’s Handbook, 7th Edition. Psychology Press.Sociology
  • Ackland, R. (2013). Web social science: Concepts, data and tools for social scientists in the digital age. London: SAGE Publications.
  • 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.
  • Steuer, M. D. (2011). The scientific study of society. New York: Springer.
Week 2
22 July – 27 July 2019

MODULE 4: MATHEMATICS & STATISTICS

(Hours 18, 3 CFU, (MAT/, SECS-S/)
Title: Introduction to Statistics
Lecturer: M. Tomaselli
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
  • Anderson D., Sweeney D., Williams T. (2010) Statistica per le analisi economiCo-aziendali, Apogeo.
  • Agresti A., Franklin C. (2016) Statistica. L’arte e la scienza di imparare dai dati, Pearson.

MODULE 5: COMPUTER SCIENCE

(Hours 18, 3 CFU, INF/01)
Title: Foundations of Algorithms in Python
Lecturer: M. Luca
Syllabus:
  • Search on lists, orderings;
  • Dictionaries, lists of lists, introduction to matrices;
  • Matrices with numpy;
  • Tree data structures, json and xml formats;
  • Operations on sequences, list comprehensions;
  • Introduction to databases

MODULE 6: ECONOMIC, PSYCHOLOGICAL, AND SOCIOLOGICAL SCIENCE

(Hours 18, 3 CFU, SPS/, SECS-P/, M-PSI/*)
Title: Modelling and methodology
Lecturers: Elena Pavan (University of Trento), Roberto Gabriele (University of Trento), Enrico Perinelli (Universty 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 cognitive methodology (hours 6): The three major methodological frameworks to the study of cognition are illustrated: a) the experimental paradigm b) the correlational approach c) the modelling perspective. For each of these three methodological viewpoints, a simple case study will be used as a practical guiding example, which will also highlight some connections between these approaches and data science problem representations.
  • 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 about the nature of data available, the construction and use of valid research instruments and the different methods of analysis applied to social data.
Suggested textbooks
  • 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 G. (2014) 7 ed., South-Western PubPsychology
  • M. W. Eysenck & M. T. Keane (2015). Cognitive Psychology A Student’s Handbook, 7th Edition. Psychology Press.Sociology
  • Ackland, R. (2013). Web social science: Concepts, data and tools for social scientists in the digital age. London: SAGE Publications.
  • 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.
  • Steuer, M. D. (2011). The scientific study of society. New York: Springer.