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1- 🔐 Cybersecurity, Social Engineering and AI Security / Main Hub Repository

A Data-Driven Approach Integrating Web Services, Machine Learning, and Financial Data Infrastructure

Cybersecurity and Social Engineering Integrated Project - PUC-SP 5th Semester (2026)

Institution: Pontifical Catholic University of São Paulo (PUC‑SP – Humanistic AI & Data Science • 5º Semester • 2026)
School: FACEI – Faculty of Interdisciplinary Studies
Course Repo: INTEGRATED PROJECT: 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.

Note

⚠️ Heads Up

• Projects and deliverables may be made publicly available whenever possible.

• The course emphasizes practical, hands-on experience with real datasets to simulate professional consulting scenarios in the fields of Machine Learning and Neural Networks for partner organizations and institutions affiliated with the university.

• All activities comply with the academic and ethical guidelines of PUC-SP.

• Any content not authorized for public disclosure will remain confidential and securely stored in private repositories.

Table of Contents

• 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)

Repository Overview

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.

Strategic Context

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.

Core Areas

- 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 .

Course Weekly Roadmap

Week	Topics	Notes
1	Course introduction, bibliography, grading criteria, information security problems, project management.	Opening and context.
2	Distributed systems concepts, client–server architecture, HTTP server, REST architecture and JSON format.	Technical foundations.
3	RapidAPI platform introduction; testing APIs; each group selects an API to consume and generate plots.	Start of API-based project.
4	Data project methodology (e.g., CRISP‑DM).	Data mining methodology.
5	Project support (API + data analysis).	Follow-up.
6	Consuming Web Services, generating plots; review of NumPy, Pandas, Plotly, Seaborn etc.	Tools review.
7	Building dashboards in Python.	Dashboard construction.
8	Group presentations of the WebService project.	1st project evaluation.
9	Presentation of the final project statement (counting words in websites and/or social networks about a chosen theme).	Launch of final project.
10	Big Data concepts.	Theory.
11	NoSQL databases.	Non-relational models.
12	Hadoop ecosystem.	Distributed processing.
13	Spark.	Big Data processing.
14	Spark (continuation).	Applications.
15	Developing the final project.	Group work.
16	Developing the final project.	Group work.
17	Developing the final project.	Group work.
18	Final project presentations (groups).	2nd project evaluation.

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