Study-unit INTRODUCTION TO DATA ANALYSIS AND MODELING
| Course name | Engineering management |
|---|---|
| Study-unit Code | A002989 |
| Curriculum | Comune a tutti i curricula |
| Lecturer | Paolo Banelli |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2022 |
| Supplied | 2024/25 |
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Sector | ING-INF/03 |
| Type of study-unit | Opzionale (Optional) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | English |
| Contents | 0 - Introduction to Data Analysis and Modelling 1 - Recap of Probability, Random Processes, Linear Systems and their analysis. 2 - Multivariate Data Analysis, linear Regression and hypothesis testing 3 - Dimensions Reduction and Features Extraction 4 - Advanced Regression Analysis 5 - Time Series Analysis 6 - Applications and Case Studies |
| Reference texts | Books are still to be defined. The students will receive notes preparade by the teachers. |
| Educational objectives | To develop the ability to (statistically) analyze datasets, identifying possible statistical relationships and influencing factors/parameters, and data interpretation by exploiting (mostly linear) models. To acquire the foundation theories to identify the (significant) model parameters, the associated reliability, and their employment for data forecasting and smoothing. Applying all these techniques to specific problems in engineering management. |
| Prerequisites | Basic notions of statistics, probability theory, linear algebra, function optimization, matrix and vector calculus, linear system theory and their analysis by Fourier and Laplace Transforms. Basic principles of structured programming. |
| Teaching methods | Frontal lessons by the teacher, with the aid of electronic notes and dedicated software tools for (statistical) data analysis and visualization (i.e., Matlab, Python, R) |
| Learning verification modality | Data analysis project, to be implemented by using one of the software platforms introduced during the practical lessons. Oral interview, to discuss the project and to verify the knowledge of theoretical foundation of the teaching. Possibly, a written test with a mix of questions with open- and closed-answers. |
| Extended program | Tentative program: it will be adapted and finalized depending on the student's pre-requisite and in-class feedback. 0 - Introduction Overview of Data Analysis and Modelling, Importance in Management Engineering, Introduction to Analytical Tools and Software 1a - Recap of Probability and Random Processes: Probability, Random Variables, Distributions and Density Functions, Expectation, Variance, Moments, Gaussian random variable, Stochastic Processes 1b - Recap on Linear Systems Basics of Linear Systems, Fourier Transforms, Laplace, and Zeta Transforms, Transfer functions and system response. 2a - Multivariate Data Analysis (3h) Introduction to Multivariate Data, visualization and Pre-processing of Multivariate Data, recap on Matrix Algebra and Vector Spaces 2b - Least Squares and Linear Regression Analysis: (6h) Method of Least Squares, Linear Regression Models, Hypothesis Testing and Confidence Intervals, Model Adequacy Checking 3 - Dimension Reduction and Feature Extraction (9h): Principal Component Analysis (PCA), Factor Analysis, Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA) 4 - Advanced Regression Analysis (6h) Logistic Regression, Regularized Regression: Ridge and LASSO, Sparse Logistic Regression, Model Selection and Cross-Validation 5a - Time Series Analysis Foundations (9h) Introduction to Time Series Data, Stationarity and Autocorrelation, AR and ARMA Models, Estimation and Forecasting. 5b - Advanced Time Series Analysis (6h) (tentative) Predictive Models for Time Series, Adaptive Filtering, Nonlinear and Nonstationary Time Series Analysis Multivariate Time Series Analysis 6 - Application and Case Studies (9h) Application of Data Analysis in Management Engineering, Case Study_1: Optimization and Decision Making, Case Study_2: Supply Chain Management, Case Study_4: Operations and Production Management Student Project Proposal Development and Approval, Implementation of Data Analysis and Modelling Techniques, Results, Discussion, and Revision. Final Submission and Presentation |