New LinkedIn Learning Math Courses!

We are excited to announce the release of two brand‑new LinkedIn Learning courses created by Megan Silvey, an experienced data science educator and consultant. These courses—Complete Guide to Calculus Foundations for Data Science and Complete Guide to Differential Equations Foundations for Data Science—are designed to equip data professionals, analysts, and students with the rigorous mathematical tools they need to tackle real‑world problems and advance their careers.

1. Complete Guide to Calculus Foundations for Data Science

Calculus underpins much of modern data analysis, optimization, and machine learning. In this course, Megan Silvey breaks down the core concepts of limits, derivatives, and integrals—focusing on practical insights and examples that mirror a traditional university curriculum.

Key Topics Covered:

• Functions & Limits: Grasp continuity and limit definitions, and learn techniques for evaluating challenging limits

• Derivatives & Differentiation: Compute derivatives of algebraic, trigonometric, and composite functions; apply rules (product, quotient, chain)

• Integrals & Integration: Master definite and indefinite integrals, fundamental theorem of calculus, and methods such as substitution and integration by parts

• Applications in Data Science: Use calculus for gradient‑based optimization, curve fitting, and rate‑of‑change analyses

Ideal for data scientists, engineers, and quantitative professionals, this course will deepen your mathematical fluency and enhance your analytical toolkit.

2. Complete Guide to Differential Equations Foundations for Data Science

In this comprehensive course, Megan Silvey walks you through the essentials of differential equations—just like you’d find in a university‑level program. You’ll learn to identify and solve first‑, second‑, and higher‑order differential equations, understand linearity, and tackle more complex topics such as nonlinear and partial differential equations.

Key Topics Covered:

• Order & Linearity: Distinguish between orders of equations and recognize linear vs. nonlinear forms

• Common Solution Techniques: Integrating factors, characteristic equations, and method of undetermined coefficients

• Series & Systems: Power‑series solutions and systems of differential equations

• Laplace Transforms & Numerical Methods: Transform methods for solving ODEs and numerical approximation techniques

• Boundary Value Problems: Formulating and solving BVPs for physical and engineering applications

Whether you’re modeling population growth, electrical circuits, or heat transfer, this course will give you the confidence to apply differential equations in a data science context.

Why These Courses Matter

As the demand for sophisticated data analysis continues to rise, a strong grounding in mathematical fundamentals is more important than ever. Megan’s practical, example‑driven approach helps learners move beyond theory—empowering them to apply calculus and differential equations directly to predictive modeling, simulation, and advanced analytics projects.

By enrolling in these courses, you’ll:

• Build a robust mathematical foundation that supports advanced machine learning and statistical methods

• Gain confidence in interpreting and solving real‑world problems that involve rates of change and dynamic systems

• Access on‑demand video lessons, downloadable exercise files, and quizzes to reinforce learning

About Megan Silvey

Megan Silvey is a data science consultant and LinkedIn Learning instructor with a track record of translating complex quantitative topics into clear, actionable lessons. She has collaborated with major organizations, contributed to O’Reilly publications, and led training initiatives at both startups and Fortune 500 companies. Megan’s passion for teaching and her deep industry experience make her an ideal guide for anyone looking to master the mathematics that drives modern data science.

Don’t miss this chance to strengthen your analytical skills with university‑level math courses on LinkedIn Learning. Enroll today and take the next step in your data science journey!