We may earn an affiliate commission when you visit our partners.
Course image
Shams El-Din El-Fouly

It is a branch that can differentiate and integral without the need to use any of the sometimes complex differentiation and integration rules. It can create best fit models with just knowing a data set. It can create functions where the only thing we know is its derivative and a condition. And best of all, it can generate approximations that have such a low percentage error that they are as good as the true value.

But...

Read more

It is a branch that can differentiate and integral without the need to use any of the sometimes complex differentiation and integration rules. It can create best fit models with just knowing a data set. It can create functions where the only thing we know is its derivative and a condition. And best of all, it can generate approximations that have such a low percentage error that they are as good as the true value.

But...

There is a limitation to numerical methods. They depend of iterative calculations. If for example you want an approximation with a low error, for example 0.001%, this will require a large amount of calculations which can be sometimes impossible to do by hand not to mention tedious. This is where programming comes in.

In this course I will walk you through not only the workings of each technique but a step by step process on how to program each of these techniques and preform hundreds if not thousands of calculations with a click of a button using one of the most powerful softwares created, EXCEL. And I'll be using excel's inbuilt programming language, VBA.

The great thing about programming languages is they all follow the same programming structure, sequence, repetition and decision making. Meaning, if you know one language you can learn another very easily by just knowing how these structures are defined in the new language.

In this course you'll have a very good grasp of these structure so if you decide to learn another language afterwards it will be very easy.

This project was a means for me to give back and contribute. I hope you find some value in this course.

Thank you and Enjoy.

Enroll now

What's inside

Learning objectives

  • Program using vba programming language
  • Approximate functions using taylor series expansions
  • Approximate derivatives using conventional and high accuracy formulas
  • Approximate integrals using trapezoidal rule, simpson's 1/3 rule and romberg integration
  • Find roots of equations using bisection, false position, newton raphson and secant methods
  • Find analytically the optimum min and max of a function
  • Solve ordinary differential equations using runge kutta methods (i.e. euler, heun's, midpoint and ralston methods in addition to fourth order runge kutta method
  • Find numerically the optimum min and max using golden section search method, newton raphson technique and finally the gradient decent/ascent method
  • Solve systems of equations using gauss elimination, gauss jordan and lu decomposition and generate inverse matrices
  • Perform curve fitting using regression analysis including linear and polynomial regression in addition to linearization for fitting more complex functions
  • Perform curve fitting using cubic spline
  • Show more
  • Show less

Syllabus

Learn the fundamental of how to program in VBA includes creating functions, subroutines, variables, loops, decision structures and more
Setting Up Excel_VBA
Read more

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Career center

Learners who complete Numerical Methods in EXCEL/VBA PROGRAMING will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Numerical Methods in EXCEL/VBA PROGRAMING.
Comprehensive textbook on numerical analysis for graduate students. It covers a wide range of topics, including linear algebra, approximation theory, and differential equations, and provides a thorough theoretical foundation for numerical methods.
Is excellent for gaining a broad understanding of matrix operations and linear algebra fundamentals. It is widely used as a textbook in undergraduate courses and provides a solid foundation with a focus on applications. It is particularly helpful for students new to the topic or those needing to solidify their basic understanding.
More than just a basic Excel guide, this book focuses on applying Excel to real-world data analysis and business problems. It's ideal for those looking to deepen their understanding of using Excel for practical applications, including newer features like dynamic arrays and Power Query. is commonly used as a textbook and strong reference for business professionals.
Is considered a foundational text in convex optimization, a subfield with wide applications. It provides a comprehensive introduction to the subject, covering theoretical concepts and practical algorithms. It is widely used as a textbook in universities and is an excellent reference for researchers and practitioners. While mathematically rigorous, it emphasizes intuitive understanding and practical problem-solving.
Provides a comprehensive and current introduction to the foundations of linear regression analysis. It is widely used as a textbook in academic institutions and is suitable for gaining a broad understanding. The latest edition includes new material on generalized regression techniques and updated examples.
Classic reference on numerical methods, and has been used by generations of scientists and engineers. It covers a wide range of topics, from basic techniques such as interpolation and integration to more advanced topics such as differential equations and Monte Carlo methods.
This practical guide is specifically tailored for those interested in financial modeling using Excel. It provides step-by-step instructions and focuses on applying Excel features to solve common business and financial problems. It's a valuable resource for students and professionals in finance and business roles looking to solidify their modeling skills.
A very popular undergraduate textbook that provides a comprehensive introduction to linear algebra and matrix operations with a strong emphasis on applications across various fields. is valuable for students looking to see how matrix operations are used in practical contexts and is often a standard text for a first course.
This comprehensive graduate-level text delves deeply into matrix theory and analysis, covering a wide range of advanced topics. It is an essential reference for researchers and graduate students specializing in matrix operations and their applications in various scientific fields.
Is essential for understanding the crucial aspects of accuracy and stability in numerical computations. It delves into the potential pitfalls of floating-point arithmetic and provides rigorous analysis of algorithms. It's a must-read for anyone serious about the reliability of numerical methods, particularly at the graduate level and for professionals.
A comprehensive and well-regarded textbook focusing on numerical methods for optimization. It covers both unconstrained and constrained optimization problems and is known for its clear explanations of algorithms. is suitable for graduate students and researchers and serves as a valuable reference for those implementing optimization techniques.
Focuses specifically on Power Query, a powerful tool within Excel and Power BI for connecting, transforming, and combining data from various sources. It's highly relevant for contemporary data analysis workflows and is essential for users who need to clean and prepare data efficiently before analysis. This book adds significant depth to data manipulation skills within Excel.
Combines financial analysis and modeling concepts with the application of Excel and VBA. It's a valuable resource for those in finance who want to leverage Excel's power for complex analysis and automation. It deepens understanding by integrating financial theory with practical Excel implementation.
Focuses on the algorithms and computational aspects of linear algebra and matrix operations. It is highly relevant for students and professionals in scientific computing, engineering, and data science who need to understand how matrix operations are performed efficiently and accurately on computers.
Is highly recommended for those who want to master financial modeling in Excel. It simplifies complex financial concepts and provides in-depth techniques for building, developing, and deploying financial models. It's a valuable resource for finance professionals and students seeking to deepen their financial modeling expertise.
Offers a detailed look at Power Query, focusing on practical techniques for data collection, combination, and transformation. It's an essential resource for anyone dealing with messy data and provides valuable skills for efficient data preparation in Excel and Power BI. This book significantly deepens understanding of data wrangling.
Provides a comprehensive introduction to numerical partial differential equations. It covers a wide range of topics, including finite difference methods, finite element methods, and spectral methods.
Comprehensive guide covering a vast range of Excel topics from beginner to advanced levels. It's an excellent resource for gaining a broad understanding of Excel's capabilities and serves as a valuable reference tool for various functions, formulas, and features. While published in 2018, its foundational content remains highly relevant for solidifying core Excel skills.
Considered a classic in financial modeling, this book focuses entirely on using Excel to implement financial models. It's a thorough resource for those seeking a deep understanding of financial modeling principles and their practical application in Excel, drawing on real-world company data.
Offers a more abstract and theoretical approach to linear algebra, focusing on vector spaces and linear operators. It is ideal for deepening understanding, particularly for mathematics majors and those preparing for graduate studies. It widely respected text for a second course in linear algebra.
A solid reference book that covers a wide range of theoretical aspects in regression analysis. While extensive, some users note the code examples might be in a less common dialect. It valuable resource for deepening understanding and is often used at the undergraduate and graduate levels.
Provides a comprehensive introduction to numerical analysis, covering a wide range of topics from interpolation and approximation to differential equations and optimization. It is written in a clear and concise style, and includes numerous examples and exercises.
Focuses on modern data analysis techniques within Excel, including Power Query and Power Pivot. It's a contemporary resource for users looking to update their data cleaning and reporting processes and perform more rigorous data analysis directly within Excel. It's highly relevant for professionals working with data regularly.
Provides a comprehensive introduction to numerical linear algebra. It covers a wide range of topics, including matrix computations, eigenvalues and eigenvectors, and linear systems.
Provides a comprehensive treatment of numerical methods for ordinary differential equations. It covers a wide range of topics, including single-step and multi-step methods, boundary value problems, and stiff equations.
Provides a practical and engaging introduction to regression analysis and other statistical methods, focusing on real-world data and research questions. It's suitable for a broad audience and emphasizes the importance of data visualization and model checking.
A comprehensive guide specifically for Excel 365, covering its features and capabilities. Similar to the Excel 2019 Bible, this book offers broad coverage and serves as a valuable reference for users of the subscription-based version of Excel, ensuring relevance to the latest updates.
This comprehensive calculus text presents a unified approach to calculus and linear algebra. It includes a thorough treatment of sequences and series, with detailed explanations of Taylor series and their convergence. It's a strong resource for ambitious undergraduates seeking a deeper and broader mathematical foundation.
Provides a rigorous and comprehensive treatment of numerical analysis, suitable for advanced undergraduate and graduate students. It delves into the mathematical theory behind the methods and strong resource for deepening understanding. It is often used as a textbook in mathematics departments.
This recent book connects linear algebra, including matrix operations, to the concepts of data science and machine learning. It is suitable for students and professionals interested in the contemporary applications of matrix operations in these rapidly evolving fields.
Is particularly well-suited for engineering and science students due to its strong emphasis on applications and its integration with MATLAB. It helps solidify understanding by demonstrating how numerical methods are used to solve practical problems. It's a popular textbook in applied fields.
Key resource for those wanting to deepen their understanding of numerical linear algebra, a critical component of many numerical methods. It covers both theoretical aspects and practical implementation, including the impact of modern computer architectures. It is well-suited for graduate students and researchers.
Provides a focused and detailed treatment of Taylor series and their connection to analytic functions. It explores advanced topics related to convergence, properties, and applications of Taylor series in complex analysis. Suitable for graduate students and researchers interested in the theoretical aspects.
This textbook popular choice for undergraduate numerical analysis courses, offering a balanced introduction to the theory and application of numerical methods. It includes a good selection of topics and is known for its clear presentation, making it suitable for students gaining a broad understanding.
This handbook provides a solid foundation in financial modeling concepts and their implementation, often using Excel as a tool. It's a good resource for those who want to understand the theory behind financial models while also learning how to build them practically. It's suitable for both students and professionals.
Offers a clear and accessible introduction to real analysis, bridging the gap between calculus and more abstract analysis texts like Rudin. It covers sequences, series, and the theoretical basis for Taylor series in a way that is understandable for upper-level undergraduate students. It's a good resource for solidifying the theoretical concepts behind Taylor series.
Authored by a leading figure in convex optimization, this book provides a concise and advanced treatment of the subject. It is highly theoretical and suitable for graduate students and researchers with a strong mathematical background interested in the cutting edge of convex optimization theory.
Emphasizes the practical application of regression analysis through numerous examples. It's an excellent resource for understanding how to perform regression and interpret the results, making it suitable for both broad understanding and practical application. The focus is on exploratory data analysis rather than just theory.
Offers a rigorous introduction to linear optimization, a cornerstone of the field. It covers fundamental concepts, simplex method, duality, and network flow problems. It is often used as a textbook for introductory graduate courses in optimization and operations research. It solid foundation for understanding more advanced topics.
Offers in-depth coverage of regression analysis and generalized linear models with a focus on applications, particularly in the social sciences. It balances theoretical concepts with practical examples and is suitable for both students and researchers. The latest edition includes new chapters on mixed-effects models.
Offers a balanced approach to numerical methods, covering both the theoretical aspects and computational implementation. It is well-regarded for its clear exposition and comprehensive coverage of topics typically found in undergraduate courses. It serves as a good textbook and reference for solidifying understanding.
Another highly-regarded introduction to real analysis for undergraduates, Abbott's book provides intuitive explanations alongside rigorous proofs. It covers the necessary background on sequences and series for understanding Taylor series convergence and properties. is valuable for students looking for a more conceptual understanding of analysis.
Provides a rigorous foundation in convex analysis and its applications in optimization. It delves into duality theory, minimax problems, and constrained optimization. It valuable resource for those seeking a deep theoretical understanding of convexity in optimization and is suitable for advanced graduate students and researchers.
Offers a practical and accessible introduction to the parts of linear algebra, including matrix operations, most relevant to engineering and data science applications. It is suitable for undergraduates and professionals seeking a strong foundation in applied linear algebra.
Known as "Big Rudin," this graduate-level text covers measure theory, functional analysis, and complex analysis. It builds upon the foundations of "Baby Rudin" and discusses Taylor series in the context of complex analytic functions and their properties. Essential for graduate students in mathematics.
A more advanced text covering a wide range of statistical learning techniques, including various forms of regression. It provides a deeper theoretical understanding and key reference for researchers and professionals in the field. It is more mathematically rigorous than 'An Introduction to Statistical Learning'.
A standard introductory textbook for numerical analysis, this book explains how Taylor series are used to approximate functions and derive numerical methods for solving problems. It's crucial for understanding the computational aspects of Taylor series and their role in numerical algorithms. Suitable for undergraduate and graduate students in STEM fields.
An updated guide to VBA and macros in the latest Excel versions, this book strong resource for automating tasks and extending Excel's functionality. It's suitable for users who want to move beyond basic formula and function usage and into more powerful automation.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Numerical Methods in EXCEL/VBA PROGRAMING.
Numerical Methods for Engineers
Most relevant
Value and Individual Decision Making
Most relevant
Automize Monthly Report Creation with Power Automate
Most relevant
Data Cleaning in Excel: Techniques to Clean Messy Data
Most relevant
Excel Skills for Business: Intermediate II
Most relevant
Computational Fluid Dynamics Fundamentals Course 3
Most relevant
Basics of Numerical Methods for Machine Learning & Engg.
Most relevant
Build Python Programs with ChatGPT with Zero Coding Skills
Most relevant
Python for Excel: Use xlwings for Data Science and Finance
Most relevant

Udemy Buyer Protection & Guarantee

Verified partner policies & purchase protections

Global Marketplace Leader
Free Trial & Audit

Free Lecture Previews

Free preview video lectures available for all courses; optional 7-day free trial on Personal Plan subscriptions (cancel before trial ends to avoid conversion).

Return & Refund Policy

30-Day Money-Back Guarantee

30-day money-back guarantee on all eligible individual course purchases (provided course content has not been substantially consumed or downloaded). Subscription fees are non-refundable once billed.

Buyer Protections

Verified Academic & Industry Partner

Lifetime access to purchased individual courses and future updates, verifiable certificate of completion, and direct student Q&A forums.

Key Purchase Protections
30-day 100% money-back guarantee on course purchases
Lifetime access on web, mobile, and offline
Certificate of completion included with purchase
Direct instructor Q&A and active student community
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser