Enterprise Data Analytics

Exam 2020-07-30 results: partial results are available here. Complete results will be available after project evaluation.

Exam rules update: students must confirm their presence to the next exam sessions at least 5 days before the exam date. Confirmation must be send to by mail to the teacher, esse3 registration is not sufficient.

Exam 2020-07-09 results: partial results are available here. Complete results will be available after project evaluation.

Exam 2020-06-19 results: partial results are available here. Complete results will be available after project evaluation.

Exam update: exam rules are available here. Note that exam will be done via Webex and students must have Acrobat Reader installed.

Course update: students are asked to complete the assessment questionnaire for this course.

Material update: Other materials section updated with EDA projects A.Y 2018-19.

Project update: projects and IoT dataset have been updated.

Project update: a draft of available projects is available here.

Google meet: due to some possible problems with Webex, lesson will be done via Google Meet. Instruction here. Room name edaunicam2020.

Course start: the first lesson is planned for 12th of March 2019, only via webex room.

Teacher:

Dr. Massimo Callisto De Donato <massimo.callistodedonato[at]gruppofilippetti.it>

ESSE3 Link

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Webex Link

https://unicam.webex.com/meet/massimo.callisto

Lessons schedule:

42 h - lectures, exercise sessions
Schedule on Thursday from 14:00am to 18:00pm
LB1 room
Schedule (tentative)
1. ~~12-mar-20 - 14:00-18:00~~
2. ~~19-mar-20 - 14:00-18:00~~
3. ~~26-mar-20 - 14:30-17:30~~
4. ~~02-apr-20 - 14:00-18:00~~
5. ~~09-apr-20 - 14:00-18:00~~
6. ~~16-apr-20 - 14:00-17:00~~
7. ~~23-apr-20 - 14:00-18:00~~
8. ~~30-apr-20 - 14:30-17:30~~
9. ~~07-mag-20 - 14:00-18:00~~
10. ~~14-mag-20 - 14:00-18:00~~
11. ~~21-mag-20 - 14:00-18:00~~
12. ~~28-mag-20 - 14:30-16:30~~

Students Office hours:

Send an e-mail to the teacher to fix an appointment.

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.