Showcasing data modeling, analytical techniques, and forensic standards for systems requiring high-integrity evidence and pattern detection.
This repository details the analytical methodologies, data management models, and techniques used to transform raw data into legally sound evidence and actionable intelligence across diverse high-stakes projects. It demonstrates deep expertise in Data Analytics and Data Forensics.
This section highlights the technical rigor used to structure and manage data to withstand legal scrutiny, drawing on MCSE/MCSA expertise.
- Evidence Structure & Schema: Public documentation of high-level, normalized data schemas designed to support complex, relational queries and maintain data integrity throughout the analysis lifecycle.
- Data Integrity Checkpoints: Outlines protocols for where data quality and integrity checks are implemented within the analytics pipeline to ensure data has not been compromised before being used for pattern detection.
- Audit Logging Standards: Commitment to internal audit logging of all analytical queries and data transformations, ensuring that the analytical chain of custody is as robust as the evidence collection chain.
This focuses on the use of data science and SQL proficiency to discover complex relationships and systemic failures.
- Relational Entity Mapping: Demonstrates how SQL/T-SQL relational data structures are used to link disparate events, individuals, and systems to visually establish complex relationship networks for pattern and conspiracy detection.
- Pattern-of-Life Analysis: Documentation detailing how event data is analyzed against baseline operational expectations to flag unusual procedural deviations or statistical anomalies.
- Statistical Analysis Frameworks: Scripts (e.g., Python/Pandas) for data cleaning, aggregation, and statistical testing used to confirm if detected patterns are statistically significant or merely random occurrences.
- Thresholds & Criteria: Analytical definitions and criteria used to distinguish routine errors from statistically significant patterns indicative of willful blindness or negligence.
This section demonstrates the standards necessary for producing data suitable for litigation and investigation.
- Forensic Tool Interoperability: Documentation on standards and formats that ensure data collected and analyzed can be reliably integrated with external forensic tools and legal platforms.
- Data Export Verification: Protocols for securely packaging evidence (including manifest files and hash verification procedures) to ensure integrity during data handoff for investigative or legal review.