CODE 98238 ACADEMIC YEAR 2022/2023 CREDITS 6 cfu anno 3 INGEGNERIA GESTIONALE 10716 (L-9) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW 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 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 DAVIDE ANGUITA Ricevimento: By appointment. LUCA ONETO Ricevimento: By appointment, scheduled by email. Exam Board DAVIDE ANGUITA (President) ALBERTO OLIVERI FABIO ROLI 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.
