Financial Analyst: AI, Excel, and Power BI Skills

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Overview

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Become a job-ready Financial Analyst with hands-on training in AI, Excel, Power BI, and financial modeling. This Entry-Level Professional Certificate prepares you for entry-level financial analyst roles—even if you have no degree or prior experience. You’ll learn how to analyze financial statements, build forecasting models, evaluate investments, measure risk, and create executive-ready reports. Along the way, you’ll use Excel, Python, R, Power BI, and AI-driven tools to automate analysis and uncover insights. The program is fully scaffolded. You’ll move step-by-step from core accounting principles and budgeting to predictive modeling, credit risk analysis, and performance storytelling. Each long course builds toward real-world projects that simulate analyst tasks in corporate finance, FP&A, investment analysis, and risk management. You’ll complete portfolio-ready projects such as: Building a 12-month financial forecast with scenario analysis Evaluating credit risk models and recommending a lending strategy Designing a Power BI performance dashboard Assessing a capital investment opportunity with risk-adjusted returns Preparing an executive financial report with control insights You’ll also develop generative AI literacy for finance—learning how to use AI tools responsibly to automate forecasting, reconcile data, and enhance reporting. This program is ideal for career switchers, recent graduates, and professionals looking to enter financial analysis.

Syllabus

Course 1: Financial Modeling: Statements, Costs & Forecasts
Course 2: AI-Driven Financial Planning, Forecasting, and Automation
Course 3: Statistical and Predictive Modeling for Finance
Course 4: Financial Data Analysis with Excel, Python and Power BI
Course 5: Analysis, Budgeting, and Risk Management
Course 6: Financial Reporting, Controls, and Performance Storytelling

Courses

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12 hours 22 minutes
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Create multi-year financial forecasts, stress-test business plans, and automate analysis using AI-driven workflows. In this course, you’ll learn how modern financial analysts combine budgeting, predictive modeling, and automation to improve decision-making. You’ll apply zero-based budgeting to control costs and analyze budget-to-actual variances to identify root causes. Then, you’ll build integrated financial projections and stress-test them against adverse scenarios. You’ll explore supervised learning techniques to forecast key business metrics and uncover value drivers. Finally, you’ll evaluate AI models for credit-risk classification and design automated pipelines that update forecasts using structured financial data. What makes this course unique is its focus on AI in finance. You won’t just build static spreadsheets—you’ll design scalable, automated workflows that reflect how finance teams operate today. The course concludes with a portfolio-ready project where you prepare a 12-month financial forecast and scenario analysis brief for leadership review.
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13 hours 2 minutes
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Evaluate investments, manage financial risk, build operating budgets, and guide capital allocation decisions. In this course, you’ll develop the analytical skills financial analysts use to support strategic and risk-aware business decisions. You’ll differentiate asset classes such as stocks, bonds, mutual funds, and ETFs, and assess their risk and liquidity characteristics. You’ll calculate Value at Risk (VaR) using historical simulation methods and apply risk matrices to evaluate market, operational, and regulatory exposure. You’ll also create operating budgets using inflation adjustments and define SMART KPIs to measure financial performance. Finally, you’ll analyze economic signals and assess capital investment opportunities using risk-adjusted returns and scenario modeling. What makes this course unique is its integration of budgeting, investment analysis, and quantitative risk measurement. The course concludes with a portfolio-ready project where you evaluate a capital investment opportunity and present a risk-adjusted recommendation.
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12 hours 53 minutes
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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.
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11 hours 12 minutes
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Build financial models, analyze statements, calculate capital costs, and forecast revenue with scenario analysis. In this course, you’ll develop the core modeling skills used by financial analysts in corporate finance and investment settings. You’ll start by interpreting income statements, balance sheets, and cash flow statements to assess liquidity and performance. Then, you’ll calculate Weighted Average Cost of Capital (WACC), analyze cost variances, and connect financial statements into integrated three-statement models. You’ll also apply top-down and bottom-up forecasting techniques and build discounted cash flow (DCF) models with sensitivity analysis. What makes this course unique is its practical focus. You won’t just learn formulas—you’ll apply them in structured modeling exercises that mirror real analyst tasks. By the end, you’ll be able to build, test, and evaluate robust financial models with confidence.
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13 hours 5 minutes
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Prepare financial statements, apply internal controls, analyze audit risks, and present executive-ready performance reports. In this course, you’ll build the reporting and communication skills required of modern financial analysts. You’ll begin with double-entry bookkeeping and GAAP fundamentals to understand how transactions flow through financial statements. Then, you’ll apply segregation of duties and internal control principles to assess financial process risks. You’ll conduct vertical analysis to identify audit triggers and use DMAIC and SIPOC frameworks to improve financial workflows. Finally, you’ll design dashboards, interpret performance variances, and combine narrative insights with data visualizations to create executive-level reports. What makes this course unique is its focus on both technical accuracy and communication clarity. You won’t just analyze numbers—you’ll learn how to present findings with confidence. The course concludes with a portfolio-ready project where you prepare an executive financial performance report integrating risk, controls, and storytelling.
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9 hours 52 minutes
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Apply regression, statistical analysis, and supervised learning to evaluate financial performance and predict risk. In this course, you’ll build the quantitative skills used by financial analysts to interpret data and support investment and lending decisions. You’ll begin by calculating and interpreting alpha and beta using regression analysis. Then, you’ll examine the assumptions behind linear regression and test model reliability using residual analysis. You’ll apply descriptive statistics to summarize datasets and design A/B tests to measure financial impact. Finally, you’ll build supervised learning models, including decision trees, to predict financial risk and evaluate model accuracy. What makes this course unique is its focus on applied finance scenarios. Instead of abstract statistics, you’ll work with financial use cases such as portfolio measurement and credit risk classification. The course concludes with a portfolio-ready project where you evaluate credit risk models and recommend a lending strategy using data-driven insights.