Summer School 2018

Week 1
30 July – 04 August 2018

MODULE 1: MATHEMATICS & STATISTICS

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

Title: Linear algebra
Mario Lauria (University of Trento)
Syllabus:
  • Functions: What is a function. General notions on functions. The main elementary functions. The equation of the line. Straight lines through a point. Straight lines through two points. Intersection of two lines. Parallel lines. The parametric equation of the line. The parable. The equilateral hyperbola. The power functions. The exponential function. The logarithm functions. The concept of infinity. [§ 3 e 4]
  • Derivatives: Definition of derivative. Calculation of some derivatives. Rules of derivation. Search for the maximum and minimum points of a function. Optimality problems. Convexity and concavity of a function. Flexion points. Study of a function. [§ 7 e 8]
  • Antiderivatives: The immediate and almost immediate antiderivatives. Integration by parts. Integration by substitution. Integral defined for continuous functions. Geometric interpretation of the integral. Calculation of a definite integral. Generalized (or improper) integrals. [§ 9 e 10]
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
Davide Poggiali (University of Padua)
Syllabus:
  1. What is a program, variables, expressions and statements; 2. Functions, conditionals and recursion; 3. Functions with return value, iteration; 4. Strings; 5. Dictionaries, tuples; 6. Case study: data structure selection, files.

MODULE 3: ECONOMIC, PSYCHOLOGICAL, AND SOCIOLOGICAL SCIENCE

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

Title: An Introduction to SPSs
Elena Pavan (University of Trento), Luigi Lombardi (University of Trento) and Roberto Gabriele (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 textbook
Economics
  • 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
30 July – 04 August 2018

MODULE 4: MATHEMATICS & STATISTICS

(Hours 18, 3 CFU, (MAT/, SECS-S/)
Title: Introduction to Statistics
Diego Giuliani (University of Trento) and Matteo Tomaselli (University of Trento)
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
Davide Poggiali (University of Padua) and Maurizio Napolitano (Fondazione Bruno Kessler)
Syllabus:
  1. Search on lists, orderings; 2. Dictionaries, lists of lists, introduction to matrices; 3. Matrices with numpy; 4. Tree data structures, json and xml formats; 5. Operations on sequences, list comprehensions; 6. Introduction to databases

MODULE 6: ECONOMIC, PSYCHOLOGICAL, AND SOCIOLOGICAL SCIENCE

(Hours 18, 3 CFU, SPS/, SECS-P/, M-PSI/*)
Title: Modelling and methodology
Giuseppe Veltri (University of Trento), Luigi Lombardi (University of Trento) and Roberto Gabriele (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 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 textbook
Economics
  • 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.