Ervads — Learn statistics, machine learning and quantitative finance

Learn statistics, machine learning, quantitative finance and data science through interactive, code-driven courses — with an AI Statistical Advisor to help you pick the right method for your problem.

Learning tracks

CFA Level I — All ten topic areas, 1,619 practice questions, and a full 180-question mock exam.
CFA Level II — All ten topic areas in the exam’s own item-set format — 1,214 questions and a full 88-question mock.
FRM Level I — All four exam areas and all 62 readings: 1,140 four-option questions and a full 100-question mock.
Survival Analysis — From your first censored observation to multi-state, joint and causal survival models: four courses, one path.

Courses

A Short Introduction to Causal Statistics — This course is a practical, beginner-friendly introduction to causal inference for data scientists and analysts who already know some statistics and Python. It starts from the fundamental problem of causal inference —…
Advanced CCAR, DFAST, and CECL Modeling – Part I: Top-Down Methods — This advanced course focuses on portfolio-level and aggregate modeling approaches used in regulatory stress testing, capital planning, and allowance estimation under the Comprehensive Capital Analysis and Review…
Advanced Credit Risk Modeling — A rigorous quantitative course covering the full spectrum of credit risk measurement, from foundational Expected Loss components through portfolio correlation models, structural and actuarial frameworks, to credit…
Advanced Econometrics – Panel Data Analysis — A rigorous graduate-level treatment of econometric methods for longitudinal (panel) data, where multiple cross-sectional units are observed over time. Students move from static linear models through dynamic GMM…
Advanced PPNR Modeling — A practitioner-grade course on Pre-Provision Net Revenue (PPNR) modeling for the banking industry. Students master the full PPNR forecasting pipeline — from regulatory context (CCAR, DFAST, IFRS 9) to advanced…
Advanced Regression and Modern Statistical Modeling — A systematic examination of how classical OLS assumptions fail in real-world data and what methods replace them. Organized around the failure-modes-of-OLS framework, each module identifies a specific violated…
Advanced Survival Analysis: When the Cox Model Isn't Enough — The Cox model is the most useful tool in survival analysis and also the most over-extended. It assumes one event, one transition, one measurement occasion, independent subjects, exact event times, a hazard ratio that…
Applied Time Series, Part 1: Statistics and Econometrics of Data with Memory — Learn to analyze and forecast data that remembers its past. This introductory statistics and econometrics course builds the complete applied time series workflow in Python: the time series mindset (why temporal…
Applied Time Series, Part 2: Multivariate Models, Cointegration, and Dynamic Econometrics — Move from one series to systems of series that move together. This is Part 2 of the two-part Applied Time Series sequence; its prerequisite is Part 1, "Applied Time Series: Statistics and Econometrics of Data with…
Applied Time Series, Part 3: Advanced Topics — State Space, Regimes, and Modern Forecasting Practice — The final course of the three-part Applied Time Series sequence, taking graduates of Part 1, "Applied Time Series: Statistics and Econometrics of Data with Memory" (applied-time-series), and Part 2, "Applied Time…
Basic Introduction to Survival Analysis — A beginner-friendly introduction to survival analysis — the branch of statistics that deals with time-to-event data. The course builds intuition first: why ordinary regression and t-tests fail when observations are…
Basics of Statistics — A beginner-friendly 1-day course introducing fundamental statistical concepts through data exploration. Learn what data is, calculate basic measures, and create simple visualizations using the mtcars dataset with…
CFA Level 1 Exam Prep: Microeconomics & Macroeconomics — A precision-engineered CFA Level 1 Economics prep course covering two high-yield modules: Microeconomics & Macroeconomics, and International Trade & Currency Markets. Built around the CFA Institute's Learning Outcome…
CFA Level 1: Alternative Investments — A comprehensive standalone course covering the full CFA Institute Level 1 Alternative Investments curriculum. Structured around the seven official learning modules — alternative investment features, methods, and…
CFA Level 1: Derivatives — A comprehensive standalone course covering the full CFA Institute Level 1 Derivatives curriculum. Structured around the official readings — derivative markets and instruments, forward commitments, contingent claims,…
CFA Level 1: Financial Statement Analysis — Develop the analytical framework professional analysts use to evaluate a corporation's financial health, detect earnings manipulation, and build the adjusted financial data that drives equity valuation and credit…
CFA Level 1: Quantitative Methods — Master the mathematical, statistical, and probabilistic tools required to analyze asset returns, evaluate corporate projects, and interpret financial research. This course serves as the mathematical engine for the…
CFA Level 2: Alternative Investments — Master the CFA Level II Alternative Investments topic area. This course covers all four official 2026 Learning Modules across four teaching chapters: commodities and commodity derivatives — the sectors and their life…
CFA Level 2: Corporate Issuers — Master the CFA Level II Corporate Issuers topic area. This course covers all four official 2026 Learning Modules across four teaching chapters: dividend policy and what it signals — the effect of every payout action on…
CFA Level 2: Derivatives — Master the pricing and valuation of forward commitments and contingent claims tested in the CFA Level II Derivatives topic area. This course covers the full 2026 curriculum: pricing equity forwards and futures with the…
CFA Level 2: Economics — Master the CFA Level II Economics topic area. This course covers both official 2026 Learning Modules across five teaching chapters: how currencies are quoted and traded — bid-offer spreads, cross rates from the correct…
CFA Level 2: Equity Valuation — Master the CFA Level II Equity Valuation topic area. This course covers all six official 2026 Learning Modules, one chapter each: the valuation process — intrinsic value, sources of perceived mispricing, going concern,…
CFA Level 2: Ethical and Professional Standards — Master the CFA Level II Ethical and Professional Standards topic area. This course covers all three official 2026 Learning Modules across seven teaching chapters: the Code of Ethics and Standards of Professional…
CFA Level 2: Financial Statement Analysis — Master the CFA Level II Financial Statement Analysis topic area. This course covers all six official 2026 Learning Modules across six teaching chapters: intercorporate investments — the classification, measurement, and…
CFA Level 2: Fixed Income — Master the term structure, arbitrage-free valuation, embedded-option, and credit analysis techniques tested in the CFA Level II Fixed Income topic area. This course covers the full 2026 curriculum: connecting spot,…
CFA Level 2: Mock Exam — A full-length, exam-realistic CFA Level II mock exam: 88 original questions delivered entirely in the exam's own item-set format — 22 vignettes of exactly four questions each, split into two timed 132-minute sessions…
CFA Level 2: Portfolio Management — Master the CFA Level II Portfolio Management topic area. This course covers all six official 2026 Learning Modules, one chapter each: how economic factors price assets through default-free rates, expected cash flows,…
CFA Level 2: Quantitative Methods — Master the multiple regression, time-series, machine learning, and big data techniques tested in the CFA Level II Quantitative Methods topic area. This course covers the full 2026 curriculum: formulating and diagnosing…
CFA Level I: Corporate Issuers — Master the Corporate Issuers section of the 2026 CFA Level I exam — 8–10% of total exam weight, translating to approximately 14–18 questions. This course covers the complete CFA Institute curriculum for the topic area:…
CFA Level I: Equity Investments — Master the Equity Investments section of the 2026 CFA Level I exam — 11–14% of total exam weight, translating to 20–25 questions. This course builds the analytical bridge between qualitative industry factors and…
CFA Level I: Ethical and Professional Standards — Complete exam-prep course for the Ethical and Professional Standards section of the CFA Level I exam — 15–20% of total exam weight, translating to 27–36 questions and the single topic where strong performance reliably…
CFA Level I: Fixed Income — Master the Fixed Income section of the 2026 CFA Level I exam — 11–14% of total exam weight, translating to 20–25 questions. This course is structured around the six official CFA Institute readings, supplemented by an…
CFA Level I: Mock Exam — A full-length, exam-realistic CFA Level I mock exam: 180 original multiple-choice questions delivered in two timed 135-minute sessions of 90 questions each, exactly mirroring the structure, pacing, topic weights, and…
CFA Level I: Portfolio Management — Master the Portfolio Management section of the 2026 CFA Level I exam — 5–8% of total exam weight, translating to 9–14 questions. This course maps directly to the official CFA Institute Learning Outcome Statements (LOS)…
Common Mistakes in Machine Learning — Master the art of avoiding critical errors that can derail your ML projects. Learn to identify and prevent common statistical biases, validation errors, and data leakage issues that plague machine learning…
Deposit Runoff Modeling: Survival Analysis and Behavioral Modeling of Non-Maturity Deposits — Build production-grade models of deposit decay for bank Asset-Liability Management. This advanced course develops the full modeling stack for non-maturity deposit (NMD) runoff: from account-level survival and…
FRM Level I: Financial Markets and Products I — The third course of the FRM Level I series, covering the first eleven official 2026 GARP readings of Financial Markets and Products — the largest area on the exam at 30%. Banks and their capital, insurance companies…
FRM Level I: Financial Markets and Products II — The fourth course of the FRM Level I series, covering the last nine official 2026 GARP readings of Financial Markets and Products — the largest area on the exam at 30%. Options markets and the no-arbitrage properties…
FRM Level I: Foundations of Risk Management — The first course of the FRM Level I series and 20% of the exam. Build the vocabulary and machinery every later topic assumes: what risk is and how taking it differs from managing it, expected versus unexpected loss,…
FRM Level I: Mock Exam — A full-length FRM Level I mock exam in the live format: 100 original standalone questions with four options each, in a single 240-minute session at 2.4 minutes a question. Questions are weighted to GARP's official…
FRM Level I: Quantitative Analysis — The second course of the FRM Level I series and 20% of the exam. All fifteen official 2026 GARP readings for Quantitative Analysis, taught on one fund and one loan book: probability and Bayes' rule, the eleven named…
FRM Level I: Risk Models — The fifth course of the FRM Level I series, covering the first eight official 2026 GARP readings of Valuation and Risk Models — VaR and expected shortfall, the three ways to compute them, volatility forecasting with…
FRM Level I: Valuation Models — The sixth course of the FRM Level I series, covering the last eight official 2026 GARP readings of Valuation and Risk Models — discounting, arbitrage and STRIPS, spot, forward, par and swap rates, bond yields and P&L…
Introduction to Applied Statistics — This course provides a comprehensive foundation in the practical application of statistical methods, transforming raw data into actionable insights through rigorous quantitative analysis. Students will explore…
Introduction to Credit Risk Modeling — This course provides a foundational overview of the quantitative techniques and regulatory frameworks used to assess and manage credit risk. It covers the end-to-end process of developing, validating, and implementing…
Introduction to Genomic Data Analysis: Biostatistics and Machine Learning for High-Dimensional Biology — Learn to analyze genomic data the way working computational biologists and biostatisticians actually do — from a public repository accession number to a defensible, reproducible result. This intermediate course assumes…
Introduction to Maximum Likelihood Estimation — Maximum Likelihood Estimation (MLE) is one of the foundational pillars of modern statistics and machine learning. This course provides a rigorous yet intuitive introduction to the theory, mechanics, and practical…
Introduction to Regression Analysis — A comprehensive introduction to regression analysis and statistical learning. This course covers foundational concepts like statistical learning principles, simple and multiple linear regression, classification…
Introduction to Survival Analysis — Survival analysis is a specialized branch of statistics focused on modeling time-to-event data. Unlike traditional regression methods, it uniquely accounts for censoring — when the event of interest (patient death,…
Investments: An Empirical and Quantitative Approach — A rigorous, data-driven introduction to modern investment theory and empirical asset pricing designed for upper-level undergraduate and early graduate finance students. The curriculum bridges theoretical…
Machine Learning Basics — A comprehensive beginner-friendly course covering fundamental machine learning concepts, algorithms, and practical applications. Master supervised and unsupervised learning, model evaluation, and real-world…
Medical Coding AI — Part 1: Clinical Data Foundations & NLP Engineering — This course builds the clinical domain knowledge and NLP engineering foundation required for AI-powered medical coding. Students learn ICD-10-CM coding standards, risk adjustment mechanics, clinical documentation…
Medical Coding AI — Part 2: Deep Learning, Transformers & Advanced Architectures — This course builds a complete deep learning system for automated ICD-10-CM coding from clinical notes. Starting from classical ML baselines, students progress through neural word embeddings, CNN and LSTM sequence…
Medical Coding AI — Part 3: Generative AI, Compliance & Production Deployment — This course takes the multi-label coding model built in Part 2 and transforms it into a production-grade clinical AI system. Students survey the landscape of medical large language models (Med-PaLM 2, GPT-4,…
Survival Prediction Modeling: Building and Validating Risk Models — A practical, intermediate course on turning survival models into trustworthy risk predictions. Fitting a Cox model is the easy part; knowing whether its predictions can be believed is the hard part, and it is where…
Transformers: From Theory to Practice — A comprehensive course covering transformers from fundamental concepts to practical implementation. Learn self-attention, build models from scratch, fine-tune pre-trained models, and deploy transformer applications…
Understanding Large Language Models (LLMs) — A comprehensive introduction to Large Language Models covering fundamental concepts, architecture, training, and applications. Learn how LLMs work, from tokenization and embeddings to transformer architecture and…

From the blog

Variation, Sampling Distributions, and the Reference Distribution (Experimenting to Learn, Part II) 2026-10-11
Is a difference real, or the kind chance produces anyway? Part II of a series built on Box, Hunter and Hunter: why independence matters far more than normality, how a plant's own history and a coin toss each give you a yardstick, and how one paired design finds an effect another analysis misses.
Why You Have to Interfere: Science, Models, and the Learning Cycle (Experimenting to Learn, Part I) 2026-10-11
Watching a process, however carefully, cannot tell you what will happen when you change it. Part I of a series built on Box, Hunter and Hunter's Statistics for Experimenters: why experiments interfere on purpose, how deduction and induction drive each other, and what an observation is made of.
What Statistical Theory Is For (Part II) 2026-09-24
Part I argued that choosing the model is where most of the risk in an analysis sits, and that statistical theory is least helpful exactly there. So what does help? Five considerations, not one of which is about fitting the data well — and they openly contradict each other.
What Statistical Theory Is For (Part I) 2026-09-24
Statistical theory sounds like a complete guide to learning from messy data. It isn't. It only goes to work after you have chosen what kind of model to fit, and it does that part extremely well. Choosing the model is left to you — and that is where most of the risk sits.
Practical Usefulness of LLMs (Part I) 2026-09-21
Stop asking whether large language models think like us and start asking what they move. The bulldozer analogy, the three jobs LLMs are genuinely good at, and why the operator still matters.
Practical Usefulness of LLMs (Part II) 2026-09-21
Where the cognitive bulldozer actually gets pointed: hyper-personalised education built on a dependency tree, and consulting that maps the whole solution space before anyone writes a recommendation.