Master analytics, one course at a time.
Practitioner-built courses across two tracks: enterprise risk analytics (credit, fraud, market, and operational risk, model governance, and AI risk) and the practical tools and automation that power analytics work (Excel VBA, R, Python, and more). Whether you are a new analyst, a seasoned data professional, or an entire team, QED Analytical meets you where you are and takes you further.
Available Courses
Enterprise Risk Analytics
Mortgage Credit Risk Modeling with R
This course takes you from R-comfortable analyst to building fully validated, production-grade mortgage credit risk models - the same models used by banks, GSEs, fintech mortgage lenders, and the regulators who examine them. Across the main core (Modules 0-12, including Module 2A on Quarto) and optional extensions (capstones and an ARM appendix), you will construct a complete PD/LGD/EAD/CECL modeling framework for conventional conforming fixed-rate mortgages using real Freddie Mac Single-Family Loan-Level data. You won't just learn about mortgage credit risk modeling - you will build every component yourself in R, step-by-step, with a practitioner guiding you through the exact workflow used in industry and reviewed by independent validation and regulatory examiners. What You'll Learn Set up a professional R environment with renv-locked reproducibility, R Project structure, and Git-based version control for sensitive loan data. Ingest, clean, and join Freddie Mac Single-Family Loan-Level origination and performance files, HMDA, FHFA HPI, and FRED macro data into a unified loan-month panel. Master mortgage mathematics for fixed-rate products - level-payment amortization, prepayment, curtailment, LTV trajectory, and HPI-driven mark-to-market LTV. Build a full Probability of Default scorecard using WoE/IV, logistic regression, monotonic binning, and points-to-double-the-odds scaling. Develop survival-based and competing-risks PD models - Kaplan-Meier, Cox proportional hazards, parametric Weibull, and Fine-Gray for prepayment vs. default. Build Loss Given Default models using beta regression, two-stage default-flag-plus-severity, and LTV-based curves with HPI stress sensitivity. Implement Exposure at Default using amortization schedules and curtailment behavior. Construct lifetime CECL reserves with vintage loss curves, roll-rate models, macro-linked satellite models, scenario blending, and Q-factor framework. Build a modern machine learning challenger - XGBoost with SHAP explainability and adverse action reason code generation. Conduct fair lending analysis using HMDA: denial rate disparities, rate spread analysis, redlining tests, BISG proxies, and disparate impact regression. Produce audit-ready documentation in Quarto: Model Development Documents, Validation Reports, Ongoing Monitoring Reports, and CECL disclosures. An optional appendix extends the framework to adjustable-rate mortgages using Freddie Mac's Non-Standard Dataset, with caps, floors, reset schedules, and payment shock. Who This Course Is For Mortgage credit risk analysts, model developers, and model validators. Bank, credit union, GSE, and fintech mortgage professionals. Data scientists transitioning into mortgage or consumer credit risk. Audit and Model Risk Management staff supporting mortgage portfolios. Career-changers preparing for risk, analytics, or model development roles in mortgage lending. Module 0 covers the R toolchain, RStudio configuration, package management, and Git basics from a practitioner's angle - but you should be comfortable with basic R syntax, data manipulation, and undergraduate-level statistics. This is a practitioner course, not an introduction to programming. Hands-On Projects You Will Build By the end of the main core, you will have created a reproducible RStudio project with Freddie Mac Single-Family Loan-Level data, renv-locked dependencies, and Git history; a working teaching subset of SFLLD built from raw quarterly text files via a documented Arrow/Parquet pipeline; a fully exploratory analysis of origination attributes and performance trajectories with vintage and seasoning views; a validated PD scorecard with KS, Gini, AUROC, calibration, and out-of-time / out-of-sample validation; a survival-based lifetime PD model with competing-risks treatment of prepayment; a two-stage LGD model with HPI stress sensitivity; an EAD model accounting for amortization and curtailment; a complete CECL engine with vintage curves, roll-rate models, scenario blending, and back-testing; an XGBoost challenger model with SHAP explanations and reason codes; a full HMDA-based fair lending analysis with disparate impact testing and remediation; and an audit-ready Model Development Document, Validation Report, and PSI/CSI monitoring dashboard. Optional capstones extend the framework against the full Freddie Mac dataset; an optional ARM appendix module applies the methodology to adjustable-rate mortgages. Every module ends with a tangible, portfolio-ready artifact. Every section ends with a hands-on lab. Every capstone ends with a complete documentation package. Course Structure The main core covers Modules 0 through 12, including Module 2A on Quarto for regulatory reporting. The Foundation phase (Modules 0, 1, 2, 2A) covers the R toolchain, mortgage credit risk fundamentals, data ingestion, and Quarto reporting. Mortgage Mechanics (Module 3) covers fixed-rate amortization, prepayment, LTV trajectory, and HPI mark-to-market. Core Models (Modules 4, 5, 6, 7) cover PD scorecards, survival PD, LGD, and EAD. Application (Modules 8, 9, 10) covers the ML challenger, fair lending, and CECL. Governance (Modules 11, 12) covers validation and ongoing monitoring. Optional extensions include capstones (Module 13) and an ARM appendix (Module 14). Every lecture includes a complete instructor script, branded slide deck, runnable R code, exercises, knowledge checks, and session notes - produced and quality-controlled to a single consistent standard. Why This Course Is Different Most credit risk courses teach theory on toy data. Most R courses don't touch regulation. This course does both - using real Freddie Mac Single-Family Loan-Level data spanning the 2005-2007 crisis vintages through recent originations, with every method implemented in production-quality R code and every output framed for SR 11-7-compliant model documentation. You'll learn not just how to build mortgage credit risk models, but how to defend them in independent validation, document them for regulatory examination, monitor them in production, and remediate them when fair lending or stability issues surface. By the end, you'll be able to independently develop, validate, document, and govern mortgage credit risk models in a real-world environment - across PD, LGD, EAD, and CECL, with the regulatory and fair lending lens that examiners actually apply.
137 lectures · 47h 36m
$49.99Enterprise Risk Management in US Banks and Credit Unions with Python
Most risk training gives you vocabulary. This course gives you the work. 36 modules. 392 lectures. Python from the first line to the last, on two institutions you come to know so well that by the time you stress their capital you already know how their balance sheets behave. Two institutions, taught side by side Acme Bank is a US commercial bank, supervised by the OCC, FDIC, Federal Reserve, and CFPB as applicable. Acme Credit Union is a federally-chartered credit union, supervised by the NCUA. Both appear in every module, so familiarity compounds instead of restarting. Almost every ERM course picks one and leaves you to translate. This one teaches both regimes together. You will work CET1, Tier 1, Tier 2, and the leverage ratio on one side, and net worth ratio, risk-based capital, and CCULR on the other. You will produce a Call Report and a 5300. If you move between the two sides of the industry, and many risk careers do, you will not be starting over. The same balance sheet under four economic regimes Both institutions carry synthetic history across four regimes, so you see the same portfolio behave differently under each: Through-the-cycle baseline - the long-run reference every deviation is measured against Financial crisis, 2008-2009 - credit-led stress, collateral collapse, capital depletion COVID shock, 2020 - a sudden exogenous shock, policy support, deposit surge, forbearance distortion Rate shock, 2022-2023 - securities repricing, deposit flight, unrealized loss Point-in-time versus through-the-cycle measurement is introduced in Module 10, constructed mathematically in Module 13, and revisited in every module where it applies. The recurrence is designed, not accidental. Free tools, nothing proprietary Python for all analytics. VS Code as the sole IDE. Quarto for every rendered output. Git and GitHub on the free tier. No R, no SAS, no proprietary risk platform, and no license to buy. Module 1 installs and verifies the whole toolchain starting from nothing on your machine, and Module 9 returns to Quarto, project structure, and Git at depth once you have something worth rendering. Reproducible, or it does not ship Every module honors the same six standards without exception: Seeds set at the top of every script Dependencies pinned, and the environment reconstructible from scratch Synthetic data generation code always included and documented Relative paths only, resolved from a project root marker Every rendered document produces identical output on any machine No hand-typed figures in any report - every number is read from a committed artifact A reproducibility checklist closes every module. That is not a style preference. It is what makes your work defensible on the day an examiner asks how you arrived at the number. Regulation verified, not recited Regulatory content is checked against primary sources before it reaches a lecture. Anything recently changed, currently in flux, or commonly misstated is tracked and resolved to a cited source. Where a point genuinely is unsettled, the course says so plainly rather than smoothing it over. The same standard applies to the code. No library method, parameter, or CLI flag appears in a lecture without being confirmed against the version you install. Start from zero. Finish expert. No prior exposure to enterprise risk management is required. Not the vocabulary, not the regulations, not the frameworks. The course builds all of it from first principles. That is a statement about where the course starts, not where it stops. The aim is comprehensive coverage of every ERM topic that matters in a US bank or credit union, at the depth a practitioner actually works at: the assumptions behind each method, where it breaks, what an examiner challenges, and what you do when the standard approach does not fit.
392 lectures · 136h
$29.99Coming Soon
New tracks in development across R, Python, and Julia - designed for anyone who works with data. Create a free account to be notified the moment each course launches.
Mortgage Credit Risk Modeling with Python
28 lectures · 7h 30m
Loss Forecasting
20 lectures · 5h
Pricing with R
18 lectures · 4h 30m
Pricing with Python
18 lectures · 4h 30m
Fraud Analytics with R
22 lectures · 6h
Fraud Analytics with Python
22 lectures · 6h
Quarto with R, Python, and Julia
14 lectures · 3h 30m
What sets these courses apart
Training built the way practitioners actually work - rigorous, reproducible, and ready to apply on Monday morning.
Real data, real code
Every lecture works with real, production-grade datasets and reproducible R, Python, or Julia code you can run, adapt, and take straight to your own portfolio - never toy examples.
Regulator-ready methods
You learn the same PD, LGD, EAD, CECL, and model-validation techniques examiners expect - documented the way a model development document actually should be.
Built by a practitioner
Courses are taught by someone who has developed and validated these models in production, so you learn what holds up in review, not just in theory.
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