Enterprise Data Analytics

This is an old revision of the document!

New exam date: 5th of december, room to be fixed
Exam results: results for the writing session of 5th of September 2019 are available here
Exam rules update: students must confirm their presence to the next exam sessions at least 5 days before the exam date.
Projects update: projects are available here
Exam results: results for the writing session of 27th of June 2019 are available here
Course update: projects have been published here. More details will be given during the next lessons.
Lessons update: for the second part of the lesson planned for 16th of May, students are invited to attend the seminary “Data-driven methods for modeling critically-ill patients affected by atrial fibrillation” 16:30 room AB1.
Lessons update: students should bring their own laptop for lab exercises.
No lesson on 4th of april 2019: lecture suspended for this week.
No lesson on 14th of march 2019: lecture suspended for this week.
Course started: course content updated.

Teacher:

ESSE3 Link

Lessons schedule:

Students Office hours:

knowledge about business data analysis and modern scenarios such as the Internet of Things.
Understanding of main differences between classical methods in data analysis and new modern scenarios.
Knowledge and expertise on Big Data methodologies and technologies, basic principles and concepts, techniques that enable data analysis and management.
Knowledge of the main Big Data technological frameworks and application in real case studies.
Highlight some Intelligent Data Analysis techniques.

Course Contents

Acquire knowledge and competence on Big Data methodologies, techniques and technologies.
Know most common techniques of Big Data analysis and how they apply to real world examples.
Apply Big Data Analysis techniques into practical case studies.

Introduction to enterprise and data management.
Analysis of scenarios and contexts of data generation: from the inter-organizational model to the Internet of things.
Introduction to the classic data analysis techniques: ETL (Extract, Transform, Load), Business Intelligence, Reporting Tools.
Methodologies and technologies for the management and analysis of large amounts of data: introduction to the Big Data model, concepts, principles and technological frameworks.
Data Analytics methodologies and techniques: batch analysis models, streaming computation.
Data Analytics evolution towards intelligent data understanding models.

Course Slides

Exam Dates A.Y. 2018/2019

Exam rules:

Writing Examination on the topics of the course
- Open or multiple-choice questions + Exercise
- 2 h
Project lab (max 2 member per team)

Project files:

Exam Results