Enterprise Data Analytics

Teacher:

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

ESSE3 Link

• —

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.

• 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

• Slides link

• Webex doc with links

• Projects (draft) link

• Reference materials
  • Slides course.
  • Material provided by the teacher.
  • Examples
    • IoT Dataset
    • Examples
    • PySpark Dataframe reference
    • Spark Standalone cluster installation guide
  • Other materials
    • Docker Deepe Dive
    • Kubernetes: Up and Running
    • A Gentle Introduction to Spark
    • Projects completed on A.Y 2018-19 Description Projects
    • A formal definition of Big Data based on its essential features

Exam Dates A.Y. 2019/2020 (tentative)

• 06/18/2020
• 07/09/2020
• 07/30/2020
• 09/03/2020
• 10/01/2020
• 10/22/2020
• 12/10/2020
• 01/14/2021
• 02/11/2021

Exam rules:

• Writing Examination on the topics of the course
• Project lab (max 2 member per team)

Exam Results

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