Overview

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In this intermediate-level specialization, you will validate and transform financial data for accurate analysis. You'll work with Power Query for data preparation, use Copilot to automate validation checks, and establish data governance controls. These skills form the foundation for accurate financial reporting and analysis while ensuring data integrity throughout your analytical workflow. This course is for financial professionals with 1–3 years of experience, including analysts, who have a foundational understanding of financial principles and standard data tools. By the end of this course, you will be able to automate financial data validation using Copilot-generated Excel formulas, transform multiple data sources using Power Query, apply data governance frameworks and controls to ensure data integrity, and build data quality scorecards to monitor financial data completeness and accuracy. This course requires Power BI Desktop, which runs on Windows PCs or Macs with Parallels Desktop. Excel Desktop with Microsoft 365 is required for Power Query and Copilot features.

Syllabus

Data Validation: Automate with Copilot

Transform manual data checking into automated validation using Copilot. You'll learn to prompt Copilot effectively to generate complex validation formulas that catch common GL data issues.

Data Transformation: Power Query Cleansing

Use Power Query to systematically clean and combine monthly financial data files. You'll build a repeatable process to handle common data issues and create a single source of truth.

Data Validation: Quality Assessment

Learn to systematically evaluate validation results, categorize data quality issues, and make decisions about data fitness for analysis. You'll develop judgment for when data requires further investigation.

Data Validation: Create Master Dataset

Bring together all validation and cleansing work to create a trusted, analysis-ready dataset. You'll establish documentation and refresh procedures that ensure ongoing data quality.

Data Integration: Multi-Source Merging

Master Power Query joins to combine data from different financial systems. You'll learn to handle common integration challenges, such as mismatched keys and differing granularities.

Data Integration: Ensure Consistency

Develop systematic approaches to verify data relationships and ensure consistency across integrated datasets. You'll learn to identify and resolve alignment issues before they impact analysis.

Data Integration: Advanced Transformations

Go beyond basic transformations to reshape complex financial data using advanced Power Query techniques. You'll build reusable transformation patterns for common financial data challenges.

Data Controls: Automated Reconciliation

Build automated reconciliation processes that compare data across systems and flag discrepancies. You'll create reusable templates that reduce manual effort while improving accuracy and compliance.

Data Controls: Discrepancy Detection

Develop detective skills to systematically identify data quality issues using governance frameworks. You'll apply control thresholds to surface discrepancies, breaks, and anomalies requiring investigation.

Data Controls: Quality Monitoring

Build comprehensive monitoring solutions that track data quality metrics over time. You'll create scorecards that provide visibility into data health, drive accountability, and support continuous improvement.

Project Module: Financial Data Pipeline Implementation

Build an end-to-end financial data pipeline that transforms messy monthly data into a governed, analysis-ready dataset. You'll implement validation checks, integrate multiple sources, and establish monitoring controls that ensure ongoing data quality. This project simulates the creation of a repeatable monthly close data process.