BIG DATA – SYNCHRONOUS

This is a free program of 2 ECTS credits that will require that the student attends synchronously online for 16 hours of classes (see time schedule below), and additionally perform some autonomous work at home.

Each participant who completes this course, either synchronously or asynchronously, will receive a personalised certificate from EIT.

REGISTRATION CLOSED

Summary of the course

This introductory course on Big Data offers a concise overview of the fundamental concepts and technologies in large-scale data management. It covers the 5 V’s of Big Data, the role of Hadoop, Spark, NoSQL databases, and Data Lakes, as well as basic approaches to batch and streaming processing. Participants are introduced to core methods of data analysis and visualization, alongside critical discussions on governance, privacy (GDPR/LGPD), and ethical issues. Designed as a foundation, the course provides essential knowledge for students and professionals preparing to advance in the field of Big Data.

Program OF THEMES

1. Fundamentals and Concepts of Big Data

What is Big Data? The 5 V’s (Volume, Velocity, Variety, Veracity, Value)
Challenges and Opportunities of Big Data
Data vs. Information vs. Knowledge
Big Data in the Current Context (trends, use cases)

2. Big Data Ecosystem and Tools

Overview of the Hadoop Ecosystem (HDFS, YARN, MapReduce)
Introduction to NoSQL Databases (MongoDB, Cassandra, Neo4j – concepts and uses)
Real-Time Processing Tools (Kafka, Spark Streaming – introduction)
Concepts of Data Lakes and Data Warehouses

3. Storage and Processing of Massive Data

Hadoop Distributed File System (HDFS): Architecture and Basic Operations
Apache Spark: Core Concepts (RDDs, DataFrames, Spark SQL) and Applications
Batch vs. Stream Processing: Differences and Use Cases
Scalability and Fault Tolerance Challenges

4. Big Data Analytics and Visualization

Principles of Large-Scale Data Analysis
Introduction to Python for Data Analysis (Pandas, NumPy – brief overview)
Machine Learning in Big Data (overview of algorithms, examples)
Data Visualization Tools (Tableau, Power BI, or similar – concept introduction)

5. Governance, Security, and Ethics in Big Data

Data Quality and Governance in Big Data Environments
Data Security and Privacy (GDPR/LGPD and other regulations)
Ethics and Responsibility in the Use of Big Data
Compliance and Audit Challenges

6. IPR Strategies for Big Data

Copyright, database rights and licensing schemes
Assessment of strategies

7. Data-Driven Decisions: Foundations of Data Science

Data collection and visualization
Statistical analysis
Examples of enhancement in organizations strategies

Time schedule (all times in CET)

Day	Hours
November 4	17:30-19:30
November 6	17:30-19:30
November 11	17:30-19:30
November 13	17:30-19:30
November 18	17:30-19:30
November 24	17:00-18:30
November 25	17:00-18:30
November 27	17:00-18:30
November 28	17:00-18:30

TRAINERS

Filipe Madeira

Themes 1, 2, 3, 4 and 5

WEB

Sveva Ianese

Theme 6

WEB

Alessio Chisari

Theme 7

WEB

Tomás Matos

Themes 1, 2, 3, 4 and 5

WEB