Quantitative Finance & Risk Modeling

Coursera via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Intermediate

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Overview

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Learn the complete quantitative finance toolkit through this comprehensive specialization that combines financial modeling, risk assessment, and advanced analytics. Starting with fundamental financial analysis techniques like WACC calculation and financial model construction, you'll progress through statistical methods, regression analysis, and A/B testing before advancing to machine learning applications for risk prediction and automated financial workflows. Through 16 project-based courses, you'll build practical expertise in Value at Risk (VaR) modeling, stress testing financial plans, data reconciliation, and predictive risk models using Python, R, and Excel. Each course features hands-on exercises with real financial datasets, enabling you to develop production-ready models for portfolio risk assessment, alpha-beta interpretation, and automated financial reporting. By completion, you'll possess the quantitative skills demanded by modern finance roles, from traditional financial analysis to cutting-edge AI-powered risk modeling, preparing you for positions in investment banking, risk management, quantitative research, and financial technology.

Syllabus

Course 1: Calculate WACC: Capital Costs
Course 2: Build and Evaluate Robust Financial Models
Course 3: Project and Stress-Test Financial Plans
Course 4: Analyze Financial Data: Reconciliation Fast
Course 5: Interpret Alpha & Beta with Regression
Course 6: Automate Financial Analysis with AI Pipelines
Course 7: Regression: Identify Assumptions & Apply Models
Course 8: Uncover Data's True Story: Statistics Unveiled
Course 9: Design A/B Tests for Financial Impact
Course 10: Predictive Models for Financial Risk
Course 11: Data Cleaning with Python for Finance
Course 12: Analyze Data: Visualize, Summarize, and R
Course 13: Transform Financial Data: Recall & Import
Course 14: Automate Excel Data with Power Query and Lookups
Course 15: Calculate VaR: Market Risk Measurement
Course 16: Assess Financial Deals & Manage Risk

Courses

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This short, hands-on course helps you think like a financial analyst by mastering how to calculate, verify, and interpret the weighted average cost of capital (WACC) — one of the most important metrics in corporate finance. Through a blend of real-world case examples, guided dialogues, and interactive activities, you’ll learn to connect capital costs to business strategy. You’ll practice breaking down debt and equity components, apply the WACC formula step by step, and interpret what your results mean for risk and value creation. Designed for professionals with basic finance experience, this course moves beyond theory to practical application — giving you the confidence and tools to explain WACC results to leadership, evaluate project returns, and make smarter, data-driven funding recommendations. By the end, you’ll have a reusable WACC calculator, analytical insights, and the strategic mindset to support capital decision-making in any organization.
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Turn raw numbers into real insight. In this short, hands-on course, you’ll learn how to summarize data accurately and interpret what it means. You’ll explore key measures of central tendency, recognize when skewed data make the median a better choice, and apply descriptive statistics to reveal data patterns. Using Excel or RStudio, you’ll calculate, visualize, and communicate results clearly for professional audiences. By the end, you’ll know how to transform large datasets into credible, decision-ready summaries that tell the true story behind the numbers.
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In this module, you’ll learn the foundations of working with R to explore categorical financial data. You’ll start by setting up R, managing packages, and inspecting data frames to understand their structure and data types. Then, you’ll apply frequency analysis using simple commands, such as table() and count(), to identify activity patterns across departments, vendors, and expense categories. Through guided practice, hands-on labs, and interactive coaching, you’ll build confidence in interpreting categorical patterns and preparing financial data for deeper analysis.
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Financial plans are only as strong as the assumptions behind them. In this hands-on course, learners build a multi-year P&L projection that connects top-down market forecasts with bottom-up sales and cost assumptions, and then stress-test the plan to evaluate resilience under pressure. By the end of the course, learners will confidently model revenue and expenses over three years, run downside scenarios, and propose margin-preserving actions—all core skills for analysts and managers in FP&A, strategy, or operations.
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Financial analysts spend hours manually reformatting data feeds—time that could be spent on analysis. This intermediate course teaches you to recognize data structures and automate transformations using Power Query, turning repetitive cleanup into one-click refreshes. You'll start by classifying structured, semi-structured, and unstructured data across typical financial sources—understanding how each format affects accuracy, governance, and reporting workflows. Then you'll master Power Query to import JSON feeds, flatten nested hierarchies, and create automated refresh pipelines that keep dashboards current without manual intervention. Through short videos, practical readings, and hands-on labs, you'll connect data concepts to daily analyst work—from explaining structure types in governance meetings to building repeatable transformation workflows. Real-world examples from firms like PwC and EY show how data literacy and automation drive accuracy, efficiency, and compliance. By the end, you'll transform messy JSON into clean tables, automate refresh workflows, and build the foundation for reliable, efficient financial reporting that scales.
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Value at Risk (VaR) is one of the most widely used tools for measuring market risk; however, understanding what it truly represents is essential for making informed financial decisions. In this intermediate-level course, you’ll learn how to interpret, calculate, and communicate VaR results using real-world financial data. You’ll start by exploring the purpose and limitations of VaR, why it became the global standard for summarizing portfolio exposure, and where it can fall short during extreme market events. Then, you’ll apply the historical simulation method to estimate potential losses and identify meaningful outliers that shape weekly risk dashboards. Through short videos, guided readings, and hands-on labs, you’ll translate quantitative findings into clear, decision-ready insights. By the end, you’ll be able to compute VaR confidently, explain its meaning to diverse audiences, and use it responsibly as part of professional market risk analysis and reporting.
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Learn how to make your regression models trustworthy, not just accurate. In this short, hands-on course, you'll explore the key assumptions behind classical linear regression and practice verifying them in RStudio. You'll fit an Ordinary Least Squares (OLS) model, visualize residuals, and detect patterns like heteroscedasticity that can distort financial forecasts. With guided discussions, a coding lab, and diagnostic interpretation, you'll build the confidence to present reliable, evidence-based results. By the end, you'll know how to test assumptions, interpret residuals, and communicate findings clearly to both analysts and decision-makers.
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In this hands-on course, you’ll learn how to analyze transaction-level financial data and ensure its accuracy across enterprise systems. You’ll build pivot-based margin reports, uncover revenue and cost drivers, and practice full reconciliations between ERP, General Ledger, and Data Warehouse datasets. By combining analytical precision with accountability, you’ll develop the confidence to explain discrepancies, document findings, and communicate financial insights that leaders trust. Ideal for analysts seeking to strengthen their financial data integrity and reporting skills.
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Automation in Excel isn’t just about saving time—it’s about building reliable, transparent systems that scale with your data. In this intermediate-level course, you’ll learn how to use Power Query to clean and transform recurring datasets, and how to apply advanced lookup formulas to design dynamic, flexible reporting tools. Through short videos, readings, and guided practice, you’ll import real financial data, automate cleansing steps, and document your logic for audit and reuse. Then, you’ll build a lookup-driven template that retrieves statement lines instantly when users select a fiscal year. By the end, you’ll have a fully automated reporting workflow—one that reduces manual effort, strengthens data quality, and supports confident decision-making across finance and analysis teams.
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Predictive Models for Financial Risk is a short, practical course for financial analysts, interns, and early-career professionals who want to use supervised machine learning responsibly in finance. Many predictive models fail not because of poor algorithms, but because key workflow steps—data preparation, validation, or transparent communication—are skipped. In this course, you’ll learn how to follow a complete supervised learning workflow, from defining a predictive question to evaluating results. You’ll build and test a decision tree classifier in Python, apply it to financial data, and report accuracy and insights in clear business language. Through short videos, guided readings, and hands-on labs, you’ll practice turning financial datasets into transparent, data-driven risk assessments. The course concludes with a project where you train and evaluate your own model, communicate performance results, and reflect on fairness and trust in financial predictions.