OVERVIEW

The course introduces the basic techniques for the representation and exploratory analysis of data from a Business Intelligence perspective.

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of the course is to introduce the student to the fundamental concepts related to EDA (exploratory data analysis) using the Python language as a tool and, in particular, the components of the SciPy library for statistical processing and data visualization. The course will provide knowledge on the main EDA techniques from a theoretical point of view and will develop the practical part by introducing the related SciPy constructs for data analysis and visualization. As part of the course, the student will acquire the skills to design and create simple applications for dashboard design and allow the analysis and representation of data from different sources. The student will develop the ability to choose the best approaches in relation to the particular data processed and the task to be performed.

AIMS AND LEARNING OUTCOMES

The student will be able to design and build a simple dashboard using data from different sources.

PREREQUISITES

Python programming language

Main concepts of databases

TEACHING METHODS

Theoretical classes and PC labs

SYLLABUS/CONTENT

Introduction to EDA (Exploratory Data Analysis)

Structured and unstructured data

Data preprocessing and Data wrangling

Key Performance Indicators

Date visualization

Dashboard design

Data warehousing and OLAP

Data Quality

Data Privacy

RECOMMENDED READING/BIBLIOGRAPHY

Material provided by the teacher

Python libraries: SciPy https://scipy.org and in particular the Pandas library https://pandas.pydata.org

Optional material:

C.C Aggarwal, Data mining: the textbook. Springer, 2015. [Chap.2,3]
J.V. Guttag, Introduction to computation and programming using Python. MIT Press, 2013. [Chap. 16]
M.J.Zaki, M.Wagner Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms. Cambridge University Press, 2019. [Chap. 1-7]
S.Few, Information Dashboard Design, 2nd Ed., Analytics Press, 2013.
D. Parmenter, Key Performance Indicators, 2nd Ed., 2010.
W. McKinney et al., Pandas: powerful Python data analysis toolkit, 2021

TEACHERS AND EXAM BOARD

Exam Board

DAVIDE ANGUITA (President)

ARMANDO TACCHELLA

LUCA ONETO (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/10716/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The student will develop independently (individually or in cooperation with other students) a case study of their choice, among those proposed by the teacher, using one of the methodologies illustrated during the course. The oral exam will focus on the discussion of the case study.

ASSESSMENT METHODS

The oral exam will allow to verify the ability to analyze and represent a set of data from different sources in order to make them usable by a hypothetical end user identified with the case study.

Exam schedule