Course Topics
- Review of statistical inference: random variables, confidence intervals, and hypothesis testing. - Introduction to statistical learning concepts: basic vocabulary and notions, parametric and nonparametric approaches, predictive and inferential objectives, bias-variance trade off, supervised and unsupervised learning - Linear regression and extensions: simple and multiple linear regression, model estimation and assessment, model assumptions, inferential tools, qualitative predictors, interaction effects, polynomial regression, basic notions on nonparametric regression - Classification: introduction to classification, logistic regression, model estimation, evaluation of classifiers - Other supervised learning techniques: trees, splines, additive models - Model selection/assessment and evaluation of model complexity: resampling methods, cross-validation and information criteria - Unsupervised learning: clustering tools such as k-means and hierarchical clustering, principal component analysis - Applications with the R software

Teaching format
In-person lectures and computer labs. Whenever possible, lectures will be structured to prioritize in-class time for discussions, and practical applications.

Educational objectives
Knowledge and understanding The student acquires advanced knowledge and understanding of the models and instruments of economic-business analysis for the creation of a new company with particular attention to the identification of new market opportunities, the availability and procurement of economic-financial resources and technological and organisational skills for the development of the company The student acquires advanced knowledge and understanding of the models and tools of economic-business analysis for the management of a new enterprise, from the financial and organisational point of view and with respect to the dynamics of growth and development I/we acquire advanced knowledge and understanding of the theories and tools for the economic analysis of business decisions. I/we acquire knowledge and understanding of theories and tools for the economic analysis of the market, at the level of the individual enterprise and the supply system I/we acquire knowledge and understanding of the theories and tools of statistical analysis for making market forecasts I/we acquire knowledge of the legal forms required for setting up a company and for the legal protection of intellectual property rights I/we acquire advanced knowledge and understanding of models for new product development and innovation management within enterprises I/we acquire advanced knowledge and understanding of business analysis tools and solutions for the development of innovations and organisational knowledge I/we acquire advanced knowledge and understanding of innovation economics models and systems for regional innovation development The student acquires knowledge of quantitative models for the formulation of forecasts necessary to guide management decisions and to predict the life cycle of a product and a sector Ability to apply knowledge and understanding ability to acquire and select information that may be relevant from an entrepreneurial point of view, also in economic-productive contexts different from those studied ability to analyse the combination of market opportunities and resources of the enterprise and to identify entrepreneurial formulas, also with the elaboration of original, compatible and sustainable solutions and combinations ability to select business economics models, suitable for the appropriate analysis of a specific economic-social and productive context ability to assess the potential and sustainability of new business projects (business plan), from a multidisciplinary (economic, business and legal) perspective ability to assess the entrepreneurial potential associated with the development of an innovation by a company (learning area 2) ability to acquire and select relevant information to frame cases of innovation (product, service, social, managerial organisational), also different from the contexts studied Autonomy of judgement Acquire the ability to analyse complex entrepreneurial problems, such as the elaboration and evaluation of an entrepreneurial project (business plan) or the development of a new product. Acquire the ability to make predictions, such as analysing the future consequences of entrepreneurial, managerial and operational choices. Autonomy of judgement is developed in the training activities carried out for the preparation of the thesis, as well as in the exercises that accompany the lectures and that involve group discussions and the comparison of individual analyses carried out by students in preparation for the lecture. Communication skills Acquire the ability to describe and communicate in an intercultural context, in a clear and precise manner, problematic situations typical of the management of a new enterprise and the development of innovation, such as, for example, the conditions for the validation of a problem or solution, the prospects and risks associated with a business model or an innovation project. The development of communication competences assumes heterogeneous situations such as, for example, the presence of internal stakeholders (e.g. colleagues, managers, owners), or external stakeholders (e.g. potential investors, suppliers and other business partners) and the ability to sustain an adversarial process. The achievement of these objectives is assessed in the course of the training activities already mentioned, as well as in the discussion of the final thesis. Learning ability Acquire the ability to study independently, to prepare summaries. Acquire the ability to identify thematic connections and to establish relationships between different cases and contexts of analysis Acquire the ability to frame a new problem systematically and to generate appropriate taxonomies. Acquire the ability to develop general models from the phenomena studied.

Additional educational objectives and learning outcomes
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Assessment
Assessment (for both attending and non-attending students): - Written Exam: Exercises and review questions (65% of the final grade). - Data Analysis Project: Group project in which students select and analyze an interesting dataset using the tools learned in the course. Groups will present their work at the end of the course (35% of the final grade; optional). Notes: - For students who do not complete the project, the written exam will count for 100% of the final grade. - Project grades remain valid for one academic year.

Evaluation criteria
Written exam: understanding of statistical concepts, correct interpretation of results of statistical analyses, clarity and precision of explanations. Data Analysis Project: Quality and clarity of the presentation, adequacy and appropriateness of analyses with respect to dataset characteristics

Required readings

James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Springer, 2013. Freely available at http://www- bcf.usc.edu/~gareth/ISL/ 

Slides and lecture notes provided

Supplementary readings

Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.

Agresti, A., Finlay, B. Statistica per le scienze sociali, Pearson, 2009. 

Hyndman, R.J. and Athanasopoulos, G. Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, 2018. 

Cicchitelli, Giuseppe. Statistica. Principi e metodi. Pearson, 2008. 

Azzalini, Adelchi, and Bruno Scarpa. Data analysis and data mining: An introduction. OUP USA, 2012. 

Grigoletto, Matteo, Laura Ventura, and Francesco Pauli. Modello lineare: teoria e applicazioni con R. G Giappichelli Editore, 2017. 

Johnson, Richard A., and Dean W. Wichern. "Applied multivariate statistical analysis." New Jersey 405 (1992). 

Further information
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