Cfa L1 Quantitative Methods Core Readings: Master The Math

Master Time Value of Money, Probability, and Statistics—All Aligned with CFA Core Readings

What you'll learn

Understand and apply the time value of money, annuities, and discounting concepts.

Analyze probability, distributions, and statistical inference within a CFA exam context.

Interpret descriptive statistics and apply hypothesis testing techniques to financial problems.

Use correlation, covariance, and linear regression to model and interpret financial data.

Requirements

You should be a current or future CFA Level 1 candidate.

The CFA Institute requires either a bachelor’s degree or equivalent work experience.

A basic understanding of high school math or introductory statistics is helpful.

Willingness to practice and engage with formulas and calculator techniques is essential.

All recommended formulas, examples, and practice questions will be provided in the course materials.

Description

Quantitative Methods is the foundation for much of the CFA Level 1 exam, and one of the most intimidating sections for many candidates. This course breaks down complex quantitative concepts into digestible, easy-to-follow lessons aligned with CFA Institute Core Readings and Learning Outcome Statements (LOS).We will cover all key topics: time value of money (TVM), NPV, IRR, annuities, descriptive statistics, probability theory, statistical distributions, sampling, estimation, hypothesis testing, and linear regression. Each topic is explained step by step using real-world finance examples, exam-focused problem sets, and visual breakdowns. You’ll also learn calculator shortcuts and essential exam strategies for handling time pressure and numerical traps.Whether you are encountering these topics for the first time or reviewing them for the third time, this course is designed to help you focus on what matters most. Master the formulas, understand the logic behind the methods, and build the confidence to apply them on exam day.With lifetime access, downloadable notes, CFA-style practice questions, and instructor support, this course equips you to succeed in one of the most challenging but rewarding sections of Level 1.If you’re serious about passing the CFA exam and want to sharpen your quant skills, this is the place to start.

Overview

Section 1: Learning Module 1: Rates and Returns

Lecture 1 Learning Module 1: Rates and Returns

Section 2: Learning Module 2: The Time Value of Money in Finance

Lecture 2 Learning Module 2: The Time Value of Money in Finance

Section 3: Learning Module 3: Statistical Measures of Asset Returns

Lecture 3 Learning Module 3: Statistical Measures of Asset Returns

Section 4: Learning Module 4: Probability Trees and Conditional Expectations

Lecture 4 Learning Module 4: Probability Trees and Conditional Expectations

Section 5: Learning Module 5: Portfolio Mathematics

Lecture 5 Learning Module 5: Portfolio Mathematics

Section 6: Learning Module 6: Simulation Methods

Lecture 6 Learning Module 6: Simulation Methods

Section 7: Learning Module 7: Estimation and Inference

Lecture 7 Learning Module 7: Estimation and Inference

Section 8: Learning Module 8: Hypothesis Testing

Lecture 8 Learning Module 8: Hypothesis Testing

Lecture 9 Learning Module 8: Hypothesis Testing Part 2

Lecture 10 Learning Module 8: Hypothesis Testing Part 3

Lecture 11 Learning Module 8: Hypothesis Testing Part 4

Section 9: Learning Module 9: Parametric and Non-parametric Tests of Independence

Lecture 12 Learning Module 9: Parametric and Non-parametric Tests of Independence

Section 10: Learning Module 10: Simple Linear Regression

Lecture 13 Learning Module 10: Simple Linear Regression

Section 11: Learning Module 11: Introduction to Big Data Techniques & LM 12 - Appendices A-E

Lecture 14 Learning Module 11: Introduction to Big Data Techniques

CFA Level 1 candidates needing a focused, exam-aligned review of Quantitative Methods.,Professionals with math anxiety or limited statistics background who need a step-by-step breakdown.,Learners who prefer practical, example-driven explanations over theory-heavy textbook content.,Candidates preparing under tight timelines who need to study more efficiently.