Cybersecurity-focused data science tasks using Python, Jupyter Notebook, and real-world datasets. Includes log analysis, anomaly detection, and network monitoring techniques from UFCE8B module at UWE Bristol
📝 Grade Awarded: 78/100 (Distinction)
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| Task | Description |
|---|---|
| Task 1 | Analysed logon activity using pandas to identify anomalies. |
| Task 2 | Visualised Windows Defender alerts to detect suspicious processes. |
| Task 3 | Performed time-series analysis on endpoint data for persistence detection. |
| Task 4 | Investigated network traffic using packet size, duration, and protocol metadata to spot outliers. |
- Python
- Jupyter Notebook
- Pandas, Matplotlib, Seaborn
- Wireshark (for PCAP analysis)
- CSV datasets and Windows log files
- Applied exploratory data analysis (EDA) to endpoint and network datasets
- Detected suspicious patterns using visualisation
- Connected data findings to MITRE ATT&CK techniques
- Documented findings clearly with code and commentary