This comprehensive course blends foundational cybersecurity principles with essential data science skills.
Students will learn to secure web applications and apply data science techniques such as statistical analysis, data management, machine learning, and data visualization.
Cybersecurity Introduction
Understanding cybersecurity threats and vulnerabilities
Common vulnerabilities (SQL injection, XSS, CSRF)
Implementing HTTPS and SSL/TLS
Foundations of Data Science
Overview of the data science workflow
Tools and libraries (Python, Pandas, NumPy, Jupyter Notebooks)
Data Management
SQL vs NoSQL databases
Working with MongoDB and MySQL
Machine Learning
Supervised vs unsupervised learning
Key algorithms (linear regression, decision trees, clustering)
Visualization Techniques
Principles of effective visualization
Tools and libraries (Matplotlib, Seaborn, D3.js, Plotly)
OWASP ZAP
Burp Suite
Wireshark
Metasploit
Python, Pandas, NumPy, scikit-learn, TensorFlow
MongoDB, MySQL
Matplotlib, Seaborn, D3.js, Platy, Dash
Git, GitHub, Heroku, Vercel, Netlify, AWS
Network Intrusion Detection System (NIDS)
Develop a system that monitors network traffic for suspicious activity and potential threats, such as attempted breaches or abnormal traffic flows, using machine learning algorithms.
Skills Developed
Network traffic analysis
Machine learning implementation
Data preprocessing and visualization
Secure File Storage System
Develop a web-based application that allows users to store files securely with end-to-end encryption. Focus on secure upload, storage, and retrieval mechanisms.
Skills Developed:
Encryption and decryption techniques
Web application development
Understanding of cybersecurity principles for data at rest and in transit