Financial Data Analysis with Excel, Python and Power BI

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Overview

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Clean, transform, analyze, and visualize financial data using Excel, Python, R, and Power BI. In this course, you’ll develop practical data analysis skills used by financial analysts to turn raw data into clear business insights. You’ll start by building structured financial budgets in Excel using advanced formulas and lookup tools. Then, you’ll clean and transform datasets using Python and apply statistical summaries in R. You’ll learn how to import and reshape structured and semi-structured data, including JSON files. Finally, you’ll design interactive Power BI dashboards, apply star-schema modeling, and implement row-level security for controlled reporting. What makes this course unique is its multi-tool approach. You won’t rely on one platform—you’ll learn how Excel, Python, R, and Power BI work together in real finance workflows. The course concludes with a portfolio-ready project where you transform retail financial data and build a performance dashboard for business reporting.

Syllabus

Excel: Build Financial Budgets Fast: Formula Foundations — Getting It Right the First Time

You will recall the syntax for common spreadsheet functions and understand how they support accurate financial calculations. You’ll practice applying formulas correctly to ensure reliable budgeting and reporting.

Excel: Build Financial Budgets Fast: Build and Automate a Financial Budget Template

You will apply advanced spreadsheet functions to construct a financial budget template. You’ll automate summary values and compare actual results against targets to evaluate budget performance.

PowerBI: Visualize & Publish Data: Get to Know the BI Landscape

You will recognize the core components within a business intelligence suite and understand how they connect to financial reporting workflows.

PowerBI: Visualize & Publish Data: Create and Publish Insights in Power BI

You will apply data visualization tools to create and publish a chart from a dataset. You’ll refine visuals for clarity and share insights through Power BI Service.

Data Cleaning with Python for Finance: Getting to Know Your Financial Data

You will recognize the purpose of fundamental functions for data loading and initial inspection. You’ll explore dataset structure and identify potential quality issues before analysis.

Data Cleaning with Python for Finance: Cleaning Data for Financial Analysis

You will apply data cleaning techniques to a specified dataset using a computational notebook. You’ll standardize formats, resolve missing values, and prepare data for reliable analysis.

Analyze Data: Visualize, Summarize, and R: Prepare and Inspect Data in R

You will recall commands to manage packages and inspect data frames. You’ll review data structure and confirm readiness for categorical analysis.

Analyze Data: Visualize, Summarize, and R: Summarize and Visualize Categorical Data

You will apply frequency analysis to summarize the distribution of categorical data. You’ll interpret patterns and prepare results for reporting.

Transform Financial Data: Recall & Import: Classify Data Structures in Finance

You will recall the definitions and examples of structured, semi-structured, and unstructured data. You’ll understand how data structure affects financial reporting and governance.

Transform Financial Data: Recall & Import: Import and Transform JSON in Power Query

You will apply data import tools to transform semi-structured JSON into a tabular format. You’ll automate transformation workflows to support scalable reporting.

Model Power BI Data with Security: Build a Star Schema for Financial Data

You will create a star-schema data model by defining table relationships and calculated columns. You’ll structure financial datasets to support accurate and efficient reporting.

Model Power BI Data with Security: Secure and Publish Power BI Reports

You will apply row-level security rules to a published report. You’ll ensure sensitive financial information remains protected while supporting collaboration.

Automate Excel Data with Power Query and Lookups: Automate Data Cleansing with Power Query

You will apply Power Query to automate data cleansing and document the transformation logic. You’ll build repeatable workflows that reduce manual effort and improve reliability.

Automate Excel Data with Power Query and Lookups: Retrieve Data Dynamically with Advanced Lookups

You will apply advanced lookup formulas to create flexible data retrieval tools. You’ll design dynamic templates that retrieve financial data efficiently and accurately.

Project: Transform Retail Financial Data and Build a Performance Dashboard

In this project, you will clean and transform raw retail financial data and build a structured performance dashboard for executive review. You will standardize inconsistent data fields, convert semi-structured expense data into tabular format, and calculate key financial metrics including sales, cost of goods sold, and operating profit. Using the cleaned dataset, you will design a clear and professional dashboard that visualizes sales trends, expense breakdown, and store performance. You will also provide written insights explaining financial trends and the importance of data cleaning before analysis. This project simulates a real financial data transformation and reporting assignment commonly performed by entry-level financial and business analysts.