BIG DATA – ASYNCHRONOUS

This is a free asynchronous program of 2 ECTS credits that consists of ten compulsory themes plus two optional ones.

Please note that, although a number of optional themes are offered, the student must select only two among them.

The period for completion of the course will start on November 17th, and finish on December 8th.
Each participant who completes this course 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.

Compulsory 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

OPTIONAL THEMES

(Students must select only two when registering to the course)

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

8. SportTech

SportTech provides the interdisciplinary field that studies, develops, and applies technological innovations in the sports context, aiming to optimize athletic performance, enhance the spectator experience, improve training methods, increase athlete safety, and make sports management more efficient.
The field integrates knowledge from sports science, engineering, computer science, biomechanics, physiology, data analysis, and artificial intelligence, promoting advances in both professional and amateur sports.

9. Introduction to General Data Protection Regulation (GDPR) principles

This course is designed to provide a comprehensive understanding of the fundamental principles of the General Data Protection Regulation (GDPR). Emphasis will be placed on key data protection terminology, core principles and legal bases for processing, the concept of privacy by design, and the management of data subjects’ rights from the data controller’s perspective. The course will also explore real-world examples from research and academia and examine the evolving role of GDPR in the context of artificial intelligence.

10. AgroTech

AgroTech provides the interdisciplinary field that applies technological, scientific, and digital innovations to optimize and transform agricultural production systems, aiming for greater efficiency, environmental sustainability, productivity, and food security.
This area encompasses the development and use of technologies such as biotechnology, sensors, artificial intelligence, the Internet of Things (IoT), automation, drones, big data, genetic engineering, and agricultural management systems, with the aim of addressing challenges such as climate change, natural resource scarcity, and increased global food demand.