DATA ANALYSIS DATA ANALYSIS

The course is among the core educational activities characterizing the three-year degree course in Chemistry and Sustainable Technologies (path in Science and Technology of Bio and Nano-materials), aimed at providing students with the theoretical knowledge and the appropriate methods to model an experiment and to analyze its outcomes by using appropriate softwares.

KNOWLEDGE AND UNDERSTANDING
Knowledge and understanding of the fundamental principles of the main methodologies for the modelling and analysis of the experimental data, also by using appropriate softwares.
ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
Ability to use the concepts learned in order to model the phenomenon and to choice among the different techniques available for the data analysis, to use them for analyzing scientific data, and to properly employ the corresponding results using the software discussed in the course.
ABILITY TO JUDGE
Ability to express a judgment on the outcomes of an experiment.

Basic knowledge in calculus.

Mathematical models of an experiment and experimental measurements. Introduction to the different scales of measurements. Probability distributions: binomial, Poisson, Gaussian (or Normal), Maxwell, Rayleigh, and their applications (examples with R commander) . Introduction to the MATLAB/OCTAVE software for data analysis. Basic concepts of numerical calculus with the aid of MATLAB/OCTAVE on the following topics: linear algebra, calculus, minima and maxima of functions, gradient, divergence, curl, Laplacian, integration and numerical computation of two-dimensional integrals - double or surface integrals, linear and non-linear models, methods for solving ordinary differential equations, with application to chemistry and spectroscopy, eigenvalues, eigenvectors and singular value decomposition (SVD), applications for the analysis of chemical and spectroscopic data, Fourier transform, convolution, deconvolution. Basic concepts on signal sampling and some noise reduction techniques (FFT, moving average, Savitzky-Golay). Applications of data analysis (also by using MATLAB/OCTAVE and/or R commander) with examples to chemistry and spectroscopy.

Mainly lecture notes.
Philip. R. Bevington, D. Keith Robinson “Data Reduction and Error Analysis for the Physical Sciences”, McGraw-Hill Education, 2003.

Oral examination (generally about 20'-25’).
The students will be asked to present and discuss one example of applications of the methods (covered in the course) to scientific data using the MATLAB/OCTAVE software, and to answer some questions about the different topics covered in the course.

Lectures coupled to examples on the use of some dedicated software packages (mainly MATLAB/OCTAVE, some examples also by using R commander).
The slides employed during each lecture (and the corresponding supplementary material) will be downloadable from the MOODLE web pages.

Italian

Accessibility, Disability and Inclusion

Accommodation and support services for students with disabilities and students with specific learning impairments:
Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). In the case of disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

STRUCTURE AND CONTENT OF THE COURSE COULD CHANGE AS A RESULT OF THE COVID-19 EPIDEMIC.

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