# Data Analytics Tools for Investigations Course (Self-Paced)

Canonical URL: <https://www.graduateschool.edu/courses/data-analytics-tools-for-investigations-course-self-paced>

## Overview

This seminar is a condensed, quick-paced overview of the principles, tools, techniques, and applications of data analytics to support forensic investigative work in detecting possible fraud. Large amounts of electronic data present an enormous challenge and opportunity to identify trends, correlations, activity, risks, red flags of possible fraud, and otherwise hidden anomalies, or association connections. This seminar will demonstrate the application of software and case studies to demonstrate the power of available tools to extract, sort, and identify specific information from databases and the cloud. This seminar will also demonstrate approaches to displaying and visualizing data for reporting and evidential presentation.

## What you'll learn

- Explain the importance of data analytics in support of investigations and fraud detection.
- Identify patterns and outliers quickly to assess possible improper activities.
- Describe the difference between structured and unstructured data.
- Use the Data Analysis Maturity Model to identify or search for specific red flags of fraud.
- Practice on multiple case studies, analyzing with specific forensic data analysis tools.
- List common data analysis tools that can be used in support of investigations.
- Explain various trends in data analysis, data architecture, and data governance, and their implications on investigations.

## Curriculum

#### Module 1: Why Data Analytics in Investigations

- Explain why analytics is essential to fraud detection and investigative triage.
- Discuss real-world fraud examples and how data revealed the schemes.
- Frame the seminar’s goals, outcomes, and hands-on approach with real data.
- Review the day agenda and expectations for participation.

#### Module 2: Data vs. Information & Structure

- Differentiate raw data from actionable information and why context matters.
- Compare structured (tables/databases) and unstructured (PDFs, email, images) data.
- Identify tools and effort needed to tag, normalize, and search unstructured sources.
- Practice enriching data to answer investigative questions.

#### Module 3: Internal & External Data Sources

- Inventory common internal sources (ERP, HR, POS, T&E, financial systems).
- Use public datasets (SAM/UEI, OFAC, CMS provider data, OIG exclusions, BLS, GSA rates).
- Explore state/city open data, SIC codes, and oversight.gov reports.
- Understand deep/surface web research considerations and data quality/privacy risks.

#### Module 4: Transactional vs. Analytical Systems

- Contrast systems optimized for entry/storage with those built for analysis.
- Join multiple sources (e.g., sales, vendor, HR) to answer cross-cutting questions.
- Introduce data marts/warehouses and denormalized models for analysis.
- Work through data quality issues that arise when combining systems.

#### Module 5: Governance, Privacy & Compliance

- Define metadata and the role of a data dictionary for consistent definitions.
- Review PII/PHI handling and investigative safeguards.
- Summarize GDPR, CCPA/CPRA, VCDPA obligations and breach/reporting expectations.
- Connect governance to reliable, defensible investigative analytics.

#### Module 6: Analytics Maturity & Methodology

- Use the Data Analytics Maturity Model to assess current capabilities.
- Plan a discovery-to-improvement pathway with risk, budget, and benefits in mind.
- Survey big data, IoT, and cloud impacts on investigative workflows.
- Outline a repeatable analysis process from scoping to results.

#### Module 7: Data Visualization Fundamentals

- Explain why visuals accelerate insight and learning.
- Start with high-level dashboards, then drill for anomalies and red flags.
- Evaluate static vs. dynamic visualizations and storytelling best practices.
- See example public-health/census maps and translate lessons to investigations.

#### Module 8: Tools Landscape

- Compare reporting and visualization platforms (Excel, Power BI, Tableau, InfoZoom).
- Review investigation-oriented tools (ACL/Arbutus/IDEA, TeamMate).
- Introduce statistics/programming tools (SPSS, SAS, R, SQL, Python) for advanced work.
- Note emerging trends: in-memory analytics, cloud services, continuous monitoring.

#### Module 9: Excel for Investigations

- Use AutoSum, descriptive functions, sorting, and filters for quick cuts.
- Build pivot tables/charts to summarize claims, vendors, offices, and dates.
- Format and troubleshoot formulas; manage large datasets efficiently.
- Create shareable charts that communicate investigative findings.

#### Module 10: InfoZoom Essentials

- Navigate Table, Compressed, and Overview views to profile data fast.
- Use Attributes, Mark Selection, and Sum to build “pivot-like” analyses.
- Create interactive charts/reports and link/join external sources.
- Practice with sample .fox datasets to answer investigative questions.

#### Module 11: Core Investigative Exercises

- Initial discovery & stratification to surface outliers and risk areas.
- Duplicates: single/multiple attributes; vendor/employee/address normalization.
- Cardholder/vendor limit testing; identify split transactions.
- MCC-code checks for restricted or personal transactions; Top-10 vendor analyses.

#### Module 12: Dates, Patterns, Benford & Automation

- Day-of-week and key date comparisons (post vs. transaction; invoice vs. PO).
- Apply Benford’s Law and interpret signals vs. noise in ledgers.
- Design sampling approaches for follow-up testing and fieldwork.
- Automate “yes/no” logic to scale repeatable fraud detection tests.

#### Module 13: Trends & Advanced Considerations

- Discuss cloud tiers, enterprise services, and security considerations.
- Set realistic KPI targets; build goal-oriented visuals (trendlines, YoY deltas).
- Plan for continuous monitoring with governance and change control.
- Summarize takeaways and additional software/data trends to watch.

## Pricing

**Tuition:** $649