InteractiveExcel VBA Programming: The Complete Guide
Welcome to Excel VBA Programming: The Complete Guide, the most comprehensive VBA course! Visual Basic for Applications (VBA) is a powerful language built on top of popular Microsoft Office applications such as Excel, Access, and Outlook. It allows developers to write procedures called macros that perform automated actions. Anything that you can do in Excel, you can automate with VBA! Over the course of more than 18 hours of content, we cover VBA from the ground up, beginning with the fundamentals and proceeding to advanced topics including:
- The Excel Object Model
- The Visual Basic Editor
- Objects and methods
- Variables and data types
- Writing your own procedures
- Workbooks and workbook objects
- Worksheets and worksheet objects
- Range references
- Range actions
- Conditional logic
- Iteration
- Alerts
- Configuring Excel functionality
- Custom functions
- Arrays
- Debugging, even procedures, and user forms
No programming experience is required; complete beginners are more than welcome! VBA is a great language to start with because it lets you master the fundamentals of programming in a familiar work environment. No extra software is necessary: VBA is bundled with all modern versions of Excel. Excel is the World's most popular spreadsheet software and is available on over 750 million computers worldwide. Whether you use it for professional or personal reasons, VBA can help you remove redundancy in your workflows and accelerates your productivity drastically! Thanks for checking out this course!
All code and supporting files for this course are available https://github.com/PacktPublishing/Excel-VBA-Programming---The-Complete-Guide" target="blank.
Learning objectives:
- Automate tasks and procedures in Excel using Visual Basic for Applications (VBA). Utilize objects in the Excel Object Model to emulate user actions in Excel. React dynamically to user events such as entering a cell value or opening a workbook. Identify the fundamentals of computer programming. Identify the importance of the k-nearest neighbors (KNN) algorithm in machine learning. Define neural networks and clustering techniques. Evaluate the performance of machine learning models
