# Data Analytic Tools for Financial Management Course (Self-Paced)

Canonical URL: <https://www.graduateschool.edu/courses/data-analytic-tools-for-financial-management-course-self-paced>

## Overview

This seminar is a condensed, quick paced overview on the principles, tools, techniques, and applications of data analytics within a contemporary financial management environment. Large amounts of electronic data present an enormous challenge and opportunity to identify trends, correlations, compliance, cost/expense activity, risks, possible fraud, errors, and otherwise hidden actions, and effects in financial and operational performance. This seminar will demonstrate the application of software and a case study to demonstrate the power of available tools to extract, sort and identify specific information from databases and the cloud. This seminar will also explore approaches to using data to identify risks and outliers, monitor activity, display, and chart results for reporting.

## What you'll learn

- Explain the importance of data analytics in financial management, performance assessment, and accountability.
- Identify patterns and outliers quickly to make decisions on what to analyze.
- Describe the difference between structured and unstructured data.
- Use the Data Analysis Maturity Model and identify your organization’s maturity.
- Practice on multiple case studies, analyzing with financial assessment and specific data analysis tools.
- List common data analysis tools that can be used in financial, activity, and performance assessment.
- Explain various trends in data analysis, data architecture, and data governance.

## Curriculum

#### Module 1: Introduction to Data Analytics in Financial Management

- Understand the role of analytics in performance, oversight, and accountability
- Differentiate between structured and unstructured data
- Assess your agency’s maturity using the Data Analysis Maturity Model
- Review common tools used in audit and financial data analytics

#### Module 2: Exercise – Initial Discovery

- Explore a new dataset to assess structure and context
- Identify missing values, outliers, and data quality concerns
- Use Excel and InfoZoom to perform discovery

#### Module 3: Duplicate Detection

- Detect potential fraud or inefficiencies through duplicate transactions
- Analyze single and multiple attributes (e.g., transaction ID, amount)
- Identify duplicated addresses, phone numbers, or vendor entries
- Apply normalization strategies to improve matching

#### Module 4: Stratification

- Group data into bands or categories for risk or anomaly detection
- Identify unusually high or low transactions by strata
- Use visual analysis tools to surface outliers

#### Module 5: Improper Payments Analysis

- Assess whether payments were appropriately made or processed
- Spot possible duplicates, ineligible recipients, or timing issues
- Apply criteria to isolate suspect transactions

#### Module 6: Aging Analysis

- Analyze outstanding receivables by age category
- Determine risks of uncollectible or aging debt
- Prioritize collection or follow-up actions using visual dashboards

## Pricing

**Tuition:** $579