# FONDAMENTI DI ANALISI DATI E LABORATORIO

Academic Year 2018/2019 - 1° Year - Curriculum Data Science
Teaching Staff
Credit Value: 9
Scientific field: INF/01 - Informatics
Taught classes: 36 hours
Exercise: 24 hours
Laboratories: 12 hours
Term / Semester:

## Learning Objectives

• Fundamental of Data Analysis

Basic introduction to state of the rt data analysis and automated classification.

• Laboratory of Foundamental of Data Analysis

The objective of the course is the acquisition of knowledge of:

• Practical tools for the management and analysis of data;
• Tools for data visualization and exploration;
• Help the understanding of theoretical concepts and models through the implementation of algorithms and/or the analysis of existing implementations;
• Practical methodologies to train and use machine learning and data analysis algorithms to build automated systems for decision support;
• Tools to produce reports detailing a data analysis process.

## Course Structure

• Fundamental of Data Analysis

Frontal lectures in classroom

• Laboratory of Foundamental of Data Analysis

Laboratory lectures

## Detailed Course Content

• Fundamental of Data Analysis

Data visualization, descriptive statistics

regression and correlation. Logistic regression.

Bayes approach to learning. MAP.

TS, CS, trainign and generalization error. Confuson matrix. ROC.

LDA, SVM.

Kernel trick: non linear SVM

PCA, non linear techniques for dimension reduction

K-nn

CART.

Clustering: k-means and hierarchical clustering.

Ensblem techniques. Boosting

• Laboratory of Foundamental of Data Analysis
• Introduction to Python for scientific calculus;
• Descriptive statistics
• Gaussian distribution;
• Linear regression;
• Correlation and logistic regression;
• Probability distributions;
• Threshold based classification, ROC curves, confusion matrix;
• MAP classification, classification based on Mahalanobis distance, Naive Bayes classification
• Principal Component Analysis, Linear Discriminant Analysis
• Support Vector Machine
• K-Nearest Neighbour
• Clustering: K-Means and Mixtures of Gaussians
• Decision Trees for classification

## Textbook Information

• Fundamental of Data Analysis

Teacher's handouts

• Laboratory of Foundamental of Data Analysis

Teacher's handouts.