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Institution: Pontifical Catholic University of São Paulo (PUC‑SP – Humanistic AI & Data Science • 5º Semester • 2026)
School: FACEI – Faculty of Interdisciplinary Studies
Course Repo: Cybersecurity and Social Engineering – 108 Hours
Professor: ✨ Eduardo Savino Gomes
Extensionist Activities: Extension projects and workshops using open‑source software and data‑driven consulting to support the community, aligned with the 20 official extension hours of the course.

Main Hub Repository for the course “Segurança Cibernética e Engenharia Social” of the Data Science and Artificial Intelligence program at PUC‑SP (FACEI, 5th semester), centralizing documentation and links to related project repositories focused on cybersecurity, social engineering, distributed systems, APIs, data analysis, and applied extension projects using Web Services and Machine Learning.

PUC‑SP is an institutional partner of Bloomberg and hosts a dedicated Bloomberg laboratory on campus, which provides access to Bloomberg data, terminals and APIs for students and faculty. This repository documents projects that make academic use of Bloomberg APIs together with public data sources (such as the Central Bank of Brazil) in the context of cybersecurity, financial intelligence and OSINT‑oriented analysis.

• Repository Overview
• Core Areas
• Course Weekly Roadmap
• High‑Level System Architecture
• Course Information
• Syllabus (Short Version)
• Learning Objectives
• Official Extension Project (Project Integrator 1 – 2 Subprojects)
  • Stage 1 – Consuming Third‑Party APIs, Dashboards and Prediction
  • Stage 2 – RESTful API Built on a SQL Database (Extension Requirement)
• Course Projects (Planned)
• Assessment
• Methodology
• Bibliography
• Suggested Folder Structure (Planned)

This main hub repository represents a complete cybersecurity and data intelligence framework, integrating:

- Cybersecurity and threat detection
- Social engineering and human behavior analysis
- Artificial intelligence and anomaly detection
- APIs, Web Services, and distributed systems
- Real‑world financial and OSINT data

Designed as an end‑to‑end secure pipeline, bridging academic knowledge with real‑world applications in the context of the course Cybersecurity and Social Engineering.

PUC‑SP is an institutional partner of Bloomberg and hosts a dedicated Bloomberg laboratory on campus, which provides access to Bloomberg data, terminals and APIs for students and faculty.

This creates a rare academic environment where cybersecurity, AI, and financial intelligence converge, enabling projects that combine Bloomberg academic APIs with public data sources such as the Central Bank of Brazil.

- Cybersecurity Engineering
- Social Engineering Analysis
- AI Security & Adversarial Systems
- Ethical Hacking & Threat Modeling
- Data Science & Web Intelligence
- Distributed Systems & APIs

These core areas align with the official syllabus topics such as information security , distributed systems , Web Services , Big Data , NoSQL and Spark .

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flowchart LR

    A["Bloomberg / Public APIs / OSINT Sources"] --> B["Data Ingestion"]
    B --> C["Processing & Feature Engineering"]
    C --> D["SQL / NoSQL Storage"]
    D --> E["Machine Learning & Anomaly Detection"]
    E --> F["Secure REST API"]
    F --> G["Dashboards, Reports & Applications"]

Understanding user behavior in relation to Information Technologies and proposing measures to increase the security of operations, combining information security, distributed systems, data analysis and real-world scenarios of cybersecurity and social engineering.

The course also discusses modern security practices, including principles aligned with Zero Trust architectures when dealing with distributed systems, APIs and data flows across institutional and external services.

Enable students to understand, model and analyze information systems and data in security-related contexts, identifying vulnerabilities, risks and protection strategies, grounded in database, distributed systems and cybersecurity concepts.

By the end of the course, students should be able to:

- Understand information security problems and propose mitigation measures related to user behavior and project management.
- Understand distributed systems, client–server architecture, HTTP servers and REST/JSON APIs.
- Apply structured data project methodologies (such as CRISP‑DM) to cybersecurity and social engineering contexts.
- Consume Web Services / APIs, process data and create visualizations (NumPy, Pandas, Plotly, Seaborn, etc.).
- Understand Big Data, NoSQL, Hadoop and Spark and their role in large-scale data processing in security scenarios.
- Develop and present group projects involving data acquisition, processing, visualization and prediction from Web Services.

The official extension project of this course is a Project Integrator for the first bimester, focused on “data analysis and prediction from Web Services”, composed of two subprojects (two stages).

Theme: Consuming APIs for data analysis and prediction using Web Services.

• Typical steps for the group:

1. Identify APIs of interest that provide data suitable for analysis and prediction.
2. Consume the APIs and generate in‑memory data tables.
3. Process the data applying filters, aggregations and totals.
4. Store filtered data in a SQL database.
5. Develop a dashboard to present the results.
6. Apply AI / ML techniques to generate predictions when possible.

• Expected deliverables:

- Notebook (Colab/Jupyter) with all code used in the project.
- Data set (if applicable).
- SQL database dump.
- PDF report with team, topic, objectives, API and data references, SQL schema and explanation of analyses, plots and AI techniques used.
- Presentation (.pptx) summarizing the report.

This stage counts towards the 20 extension hours, since it uses real or open data APIs in market or social contexts

The second subproject requires building an API using RESTful architecture on top of a SQL database with at least three tables.

Objective: design and implement a SQL database (≥ 3 tables) and expose processed/aggregated data through a RESTful Web Service.

Main tasks:

- Choose a real‑world context (company, NGO, market sector, social domain, etc.).
- Design the SQL database with at least 3 tables and produce its dump.
- Implement a RESTful API exposing the processed and aggregated data required by the use case.
- Integrate this API into the end‑to‑end workflow: external APIs → processing and storage in SQL → publication via your own REST API.

Planned internal structure (you can rename when you implement it):

- extension_official/stage2_rest_api_sql/
- SQL schema (DDL + ER/diagram).
- RESTicial extension project agreed with the professor, covering APIs, data pipelines, SQL design and RESTful Web Services.ful API implementation (e.g., FastAPI, Flask, Django REST).
- Documentation of endpoints, parameters, responses and basic security considerations.

Together, Stage 1 + Stage 2 form the off

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