|SCIENTIFIC DISCIPLINARY SECTOR
The course introduces the basic techniques for the representation and exploratory analysis of data from a Business Intelligence perspective, with reference to descriptive analytics and exploratory data analysis methodologies aimed at supporting decisions in industrial and management fields.
AIMS AND CONTENT
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.
Python programming language
Main concepts of databases
Theoretical classes and PC labs
Introduction to EDA (Exploratory Data Analysis)
Structured and unstructured data
Data preprocessing and Data wrangling
Key Performance Indicators
Data warehousing and OLAP
Material provided by the teacher
Python libraries: SciPy https://scipy.org and in particular the Pandas library https://pandas.pydata.org
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
Ricevimento: By appointment.
Ricevimento: By appointment, scheduled by email.
DAVIDE ANGUITA (President)
LUCA ONETO (President Substitute)
L'orario di tutti gli insegnamenti è consultabile all'indirizzo EasyAcademy.
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.
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.