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Arkadi Avanesyan

"Microsoft Excel VBA and Macros for Automation and Productivity" is a practical course designed to help office professionals to improve their efficiency in using Excel for data manipulation and analysis tasks.

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"Microsoft Excel VBA and Macros for Automation and Productivity" is a practical course designed to help office professionals to improve their efficiency in using Excel for data manipulation and analysis tasks.

With the ever-increasing speed of business today, especially in digital work environments, our time becomes more valuable than ever. This means that repetitive tasks involving data manipulation can be not only time-consuming but also lead to mistakes. Working carefully with data helps us avoid these mistakes and keeps our projects on track. This course offers a solution by teaching participants how to automate repetitive tasks using Excel VBA and macros.

This course will cover foundational concepts of Excel programming and the Visual Basic for Applications (VBA) tool designed to enhance productivity and automate manual tasks. By mastering these tools, participants can save valuable time and focus on higher-level activities such as analysis and strategic decision-making.

Through simplified explanations and practical examples, this course will help to navigate through the Excel interface and the VBA editor, record and write your macros. We will cover advanced data manipulation and analytics through real-life examples and write advanced macros necessary for such tasks.

This course is designed for professionals and students who manage, analyze, and interpret data, including administrative employees, analysts, accountants, finance professionals, business intelligence specialists, and STEM students. It offers valuable tools for automating Excel tasks, enhancing workflow, and boosting productivity.

Participants should have a computer with Microsoft Excel installed and basic knowledge of MS Excel. Familiarity with Excel’s data analytics functions is beneficial, as the course builds on these foundations to introduce task automation and productivity improvements. An interest in automating tasks and improving efficiency with Excel VBA and Macros is essential.

Upon completing this course, learners will understand the basics of Excel VBA and Macros and their applications. They will gain skills to design and implement macros to automate routine tasks, enhancing productivity. Additionally, participants will learn to evaluate and debug VBA code, ensuring efficient data manipulation and analysis.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on Microsoft Excel, which is widely used in the workplace
Provides foundational knowledge of Excel programming, VBA, and Macros
Covers advanced data manipulation and analytics through real-life examples
Taught by Arkadi Avanesyan, a recognized expert in Excel VBA and Macros
Includes hands-on exercises and real-world examples to reinforce learning
Suitable for those seeking to enhance their productivity in data manipulation and analysis

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Reviews summary

Practical excel vba & macros

According to learners, "Microsoft Excel VBA and Macros" is a highly practical course that effectively teaches how to automate repetitive tasks in Excel, significantly enhancing productivity and efficiency. Many students highlight the clear explanations provided by the instructor, making complex VBA concepts accessible even for those new to programming. The course is particularly praised for its hands-on activities and real-world examples, which enable immediate application of learned skills. While it provides a strong foundational understanding, some advanced users found it could delve deeper into more complex topics. Learners are advised that basic Excel knowledge is a crucial prerequisite for optimal engagement.
Strong foundation, but may not satisfy advanced learners' needs.
"This course is excellent for beginners to VBA, but I was hoping for a bit more depth on advanced optimization techniques."
"It gave me a solid start, but I feel I'll need another course to dive into more complex scenarios and larger projects."
"As an experienced Excel user, I found parts of it a little basic, though it's a great intro for newcomers."
Assumes and builds upon basic knowledge of Microsoft Excel.
"It's important to have a good grasp of basic Excel functions before taking this course, as it moves quickly."
"I struggled a bit at the beginning because my Excel knowledge wasn't as strong as it should have been."
"The course clearly states prerequisites, and having solid basic Excel skills truly helps you get the most out of it."
Instructor actively updates content based on user feedback.
"I noticed improvements in some of the newer modules, which shows the instructor is responsive to feedback."
"It's great to see that the course is continuously being updated, making it even more relevant."
"The instructor seems to address past suggestions, which is a positive sign for the course's longevity and quality."
Engaging exercises reinforce concepts and build practical skills.
"The hands-on exercises were crucial for solidifying my understanding; I learned best by doing."
"I loved the practical demos and opportunities to code along, it made the learning process much more effective."
"The exercises provide a great way to practice and internalize the VBA concepts taught."
Complex VBA concepts explained clearly and comprehensively.
"The instructor broke down seemingly complex VBA concepts into easily digestible lessons, which I truly appreciated."
"Even as someone with no prior programming experience, I found the explanations very clear and easy to follow."
"Lectures were well-structured, and the instructor's delivery made learning VBA very engaging."
Automate tasks and boost efficiency in real-world scenarios.
"I immediately applied the macros learned to automate daily reports, saving hours of manual work."
"This course helped me streamline several repetitive tasks at my job, which has been incredibly valuable."
"I found the practical examples directly applicable to my work, and I've already implemented several automations."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Microsoft Excel VBA and Macros with these activities:
Collaborative VBA Development
Connect with peers to discuss VBA concepts, share code snippets, and troubleshoot challenges.
Show steps
  • Join or create a study group
  • Set up regular meeting times
  • Present your VBA projects for feedback
  • Collaborate on projects
Hands-on VBA Coding Exercises
Dive into practical coding exercises to enhance your VBA skills and master complex data manipulation tasks.
Browse courses on Excel Automation
Show steps
  • Set up a practice workbook
  • Design and code VBA macros for specific tasks
  • Test and debug your macros
  • Iterate and refine your code
  • Experiment with different VBA functions and techniques
Custom Excel Spreadsheet with VBA Automation
Conceive and create your own Excel spreadsheet, leveraging VBA to automate tasks, enhance functionality, and streamline your workflow.
Browse courses on Data Management
Show steps
  • Identify a problem or task you want to automate
  • Plan the structure and layout of your spreadsheet
  • Design and code VBA macros to perform the desired actions
  • Test, refine, and document your spreadsheet
  • Share your spreadsheet with others
Three other activities
Expand to see all activities and additional details
Show all six activities
Contributing to Open Source VBA Projects
Engage in open source VBA projects to enhance your skills, contribute to the community, and discover innovative solutions.
Browse courses on Excel Automation
Show steps
  • Identify relevant open source VBA projects
  • Review the project documentation and code
  • Suggest improvements or bug fixes
  • Submit pull requests
  • Collaborate with other contributors
VBA-Powered Data Analysis Project
Embark on a project that utilizes VBA to analyze large datasets, generate insights, and present your findings effectively.
Browse courses on Advanced Data Analysis
Show steps
  • Gather and preprocess data
  • Develop custom VBA functions for data manipulation and analysis
  • Create interactive charts and dashboards to visualize your results
  • Compose a report summarizing your findings
Automated Data Management System
Develop a fully automated data management system using VBA, streamlining data entry, processing, and retrieval for your organization.
Browse courses on Excel Automation
Show steps
  • Gather requirements and design the system
  • Create VBA macros for automated data entry
  • Develop macros for data processing and validation
  • Implement a user-friendly interface for data retrieval
  • Test, deploy, and maintain the system

Career center

Learners who complete Microsoft Excel VBA and Macros 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 Microsoft Excel VBA and Macros.
Created by the original architect of pandas, this masterwork teaches the canonical data structures and vectorized operations essential for slicing, cleaning, merging, and reshaping complex tabular datasets in Python.
Presents the modern tidyverse grammar for transforming messy data into structured insights. Covers tibbles, dplyr pipelines, tidyr pivoting, and purrr iterations for rapid exploratory data manipulation.
Classic work on the economics of efficiency. Pigou, a leading economist, analyzes the factors that determine economic efficiency and how it can be improved.
Demystifies relational data transformation through practical PostgreSQL projects. Teaches window functions, subqueries, regular expressions, and cross-tabulations required to manipulate data directly inside the database.
This comprehensive handbook covers the entire field of robotics and automation, from the basics to the latest developments. It is an essential resource for anyone who wants to learn about this rapidly growing field.
Delivers a comprehensive, project-driven guide to wrangling messy timeseries, handling missing values, and restructuring dataframes with pandas. Emphasizes defensive coding and performance profiling for large analytical workloads.
A more recent iteration of the comprehensive 'Bible' series, this book covers the features of Excel 2021. It serves as an excellent reference and learning tool for users who want a deep understanding of the software, from basic to advanced functionalities. It's suitable for a wide range of users.
Dives into contemporary Excel topics like Power Pivot and Power Query, which are crucial for handling and analyzing large datasets. It's a key resource for users looking to move beyond basic data analysis and leverage Excel's business intelligence capabilities. Recommended for intermediate to advanced users and professionals.
Explores the mathematical and algorithmic techniques for transforming raw tabular data into predictive signals. Details numeric scaling, log transforms, categorical encoding, text vectorization, and automated feature extraction.
Guides engineers through extracting, cleaning, and transforming irregular data from diverse formats including JSON, XML, PDF, and messy CSVs. Focuses on automation, error detection, and parsing web feeds reliably.
Provides a deep, foundational look into R core data objects, subscripting mechanics, factor conversions, string manipulation, and reshaping vectors. Offers timeless clarity on base R data processing paradigms.
Specifically targets financial modeling using Excel, a key application in finance and accounting. is essential for students and professionals in these fields who need to build and analyze financial models. It covers relevant formulas, functions, and best practices.
Lays out a systematic workflow for profiling, assessing, and structuring untidy business data. Connects data preparation directly to downstream analytics, preventing upstream bias and data transformation bottlenecks.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Focuses on creating effective and advanced data visualizations using Excel, going beyond the default chart types. It's highly relevant to contemporary data analysis and communication. It provides step-by-step guides for creating various chart types and is valuable for users at all levels looking to improve their data storytelling.
An in-depth book that provides step-by-step instructions for creating macros in Microsoft Excel. Covers the basics of VBA, the programming language used to create macros.
A focused guide on Power Query, a powerful tool for data extraction, transformation, and loading within Excel and Power BI. is crucial for anyone dealing with data from multiple sources and needing to clean and prepare it for analysis. It's a valuable resource for data professionals.
Presents a proactive architectural framework for monitoring data pipelines and catching silent transformation bugs. Teaches automated anomaly detection, schema drift prevention, and lineage tracking for robust data operations.
Addresses contemporary data analytics practices within Excel, focusing on modern techniques for cleaning, transforming, and analyzing data. It's highly relevant for those looking to update their skills and leverage Excel for more sophisticated data analysis tasks.
Delivers fast, battle-tested techniques for cleaning and pivoting irregular spreadsheet exports. Shows how to master Power Query, dynamic arrays, and nested lookups to clean corporate datasets with surgical speed.
Collects cautionary case studies from seasoned data practitioners detailing how hidden encoding bugs, missing fields, schema mismatches, and parsing failures occur, with concrete recipes for defensive data hygiene.
Exposes the most widespread and damaging traps in relational data manipulation and query construction. Illustrates how to avoid Cartesian explosion, improper NULL comparisons, and flawed table structures with cleaner SQL alternatives.
Focuses on applying Excel to solve business problems and analyze business data. It's a practical guide for students and professionals in business-related fields who need to use Excel for decision-making and analysis.
Ideal for beginners, this book provides a solid foundation in Excel basics with step-by-step guides. It covers a wide range of fundamental topics, making it a great starting point for those new to spreadsheets. While suitable for initial learning, it also touches upon more complex areas.
Dives deep into array formulas, a powerful technique for complex calculations in Excel. It's for users who want to deepen their understanding of advanced formulas and push the boundaries of what Excel can do. Recommended for intermediate to advanced users.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Explores advanced in-process analytical querying and columnar data manipulation using DuckDB. Teaches students how to construct complex analytical pipelines, process multiterabyte Parquet tables locally, and embed SQL transformations into Python.
Introduces high-performance dataframe processing using Polars and Apache Arrow memory layout. Demonstrates lazy evaluation, query optimization, and multicore parallel execution for multiterabyte data manipulation in Python.
A focused guide on mastering Excel formulas and functions, which are fundamental to using Excel effectively. is helpful for users looking to deepen their understanding of how to perform calculations and manipulate data. Relevant for all levels beyond the absolute beginner.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Offers a hands-on approach to learning Excel VBA, with step-by-step instructions and exercises designed for quick learning. It's suitable for users who want to rapidly acquire VBA skills for automation and enhancing their Excel capabilities.
Covers distributed data transformation patterns across large compute clusters using Apache Spark. Explains DataFrame and Dataset operations, resilient distributed transformations, and partitioning optimization.
Explores modern columnar in-process analytical SQL transformations using DuckDB. Teaches students how to manipulate large Parquet and CSV files with lightning-fast vectorized execution directly from Python and R environments.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Systematizes data preparation for statistical analysis in R, emphasizing string manipulations with stringr, date transformations with lubridate, and automated web scraping for data ingestion.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
Provides a beginner-friendly introduction to data analysis techniques using Excel. It covers essential tools and methods for analyzing data within Excel, making it suitable for those new to data analysis or using Excel for this purpose.
Explores the relationship between innovation and efficiency. Jorgenson, a prominent economist, offers evidence that innovation can lead to significant improvements in productivity and economic growth.
Supplies rapid, practical recipes for migrating pandas workflows to blazing-fast Polars pipelines. Walks through streaming chunk processing, complex string parsing, and timeseries window calculations with minimal RAM overhead.
A comprehensive guide aiming to take users from beginner to expert. It covers a wide range of topics and can be used for both gaining a broad understanding and deepening knowledge. Its step-by-step approach makes it accessible for self-learners.
A beginner-friendly approach to learning Excel VBA programming. breaks down complex VBA concepts into easy-to-understand steps, making it accessible for those new to automation. It's a good starting point before moving on to more advanced VBA resources.
Provides a concise, pragmatic reference for modern ELT data ingestion and transformation patterns. Covers incremental loading, surrogate key generation, data staging hygiene, and orchestrating batch transformations reliably.
Introduces interactive visual data manipulation, table restructuring, recoding variables, and outlier detection within statistical workflows. Bridges raw data entry with exploratory data mining.
Teaches probability and statistics using a computational approach with Python. It's ideal for students and professionals with programming experience who want to understand statistical concepts by doing. It helps solidify understanding through hands-on application.
Provides a rigorous mathematical treatment of rule-based data validation, deductive imputation, and error localization. Outlines systematic methodologies for certifying data quality before modeling.

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