Prepare your team for modern software development with Python Programming for AI/ML Enablement and Excel VBA Modernization, a specialized training program designed to develop practical Python skills for emerging AI and machine learning applications while supporting the modernization of traditional Excel VBA solutions.

 

Duration 5 days – 35 hrs

 

Overview

This course develops practical Python programming skills for developers who will support artificial intelligence, machine learning, data-processing, and business automation initiatives. The program places particular emphasis on transitioning legacy Excel VBA macro processes into maintainable Python applications.

Participants begin with core Python programming and progressively apply these skills to structured-data processing, Excel automation, data preparation, machine learning, testing, logging, and solution deployment. By the end of the course, participants will produce a working Python prototype that either modernizes an Excel macro process or supports a practical AI/ML use case. 30% instructor-led discussion and demonstrations; 70% guided coding laboratories, code reviews, and workplace-based exercises. 

After the training, the development team should be able to identify suitable Excel macro processes for modernization, reduce dependency on VBA, create more supportable automation, prepare quality data for AI/ML initiatives, and develop proof-of-concept solutions for internal business use.

 

Learning Objectives

• Support AI and machine learning initiatives through data preparation and model development.
• Replace or enhance Excel VBA macros with reusable Python automation.
• Improve the maintainability, reliability, scalability, and auditability of existing business processes.
• Develop structured Python programs using functions, modules, classes, and standard coding practices.
• Process and validate business data from Excel, CSV, JSON, and text files.
• Assess an existing VBA macro and identify its inputs, business rules, dependencies, and outputs.
• Rebuild common VBA macro processes using Python, pandas, and openpyxl.
• Create automated Excel reports, validation files, exception reports, and consolidated workbooks.
• Clean, transform, analyze, and prepare data for AI and machine learning use cases.
• Develop and evaluate foundational machine learning models using scikit-learn.
• Apply logging, error handling, testing, documentation, and deployment practices to Python automation.
• Present a working automation or AI/ML solution suitable for further internal development. 

 

Target Audience

• Software developers
• Application support developers
• Automation developers
• Data analysts transitioning into Python development
• Technical teams supporting AI, machine learning, reporting, and Excel macro modernization initiatives

 

Prerequisites 

• Basic programming logic or experience in VBA, JavaScript, C#, Java, SQL, or another programming language.
• Familiarity with Microsoft Excel and current Excel macro-based processes.
• Basic understanding of files, folders, and data tables.
• Participants should bring a laptop with Python, Visual Studio Code, and Microsoft Excel installed.

Course Outline 

 

Day 1 — Python Programming Fundamentals for Automation Development

• Introduction to Python for enterprise automation, data processing, AI, and machine learning 
• Python versus VBA: key differences in structure, execution, reuse, and maintainability 
• Python installation, interpreter, virtual environments, and package management 
• Visual Studio Code configuration, terminal usage, and debugging tools 

• Python syntax, indentation, comments, and code readability 
• Variables, data types, type conversion, operators, and expressions 
• Strings, formatting, and user-friendly output 
• Conditional logic using if, elif, and else 
• Iteration using for and while loops 
• Functions, parameters, return values, and reusable logic 
• Exception handling using try, except, else, and finally 

       Hands-On Activities

• Configure the Python development environment. 
• Create and execute Python scripts from VS Code and the terminal. 
• Build a business-rule calculator using variables and conditions. 
• Develop a data-validation script for employee, customer, or transaction records. 
• Use loops to process multiple records. 
• Create reusable functions for tax, discount, status, or approval calculations. 
• Add error handling for invalid user input. 
• Debug a faulty Python script using breakpoints and error messages. 

 

Day 2 — Data Structures, File Processing, and Business Data Validation

• Lists, tuples, sets, and dictionaries 
• Choosing appropriate data structures for business requirements 
• Nested dictionaries and lists for multi-record data 
• Iterating through structured business records 
• List and dictionary comprehensions 
• Working with dates, times, numeric values, and text cleanup 
• Reading and writing text and CSV files 
• JSON data structures and integration-ready formats 
• Creating reusable Python modules 
• Introduction to logging and traceability 
• Separating configuration, program logic, and output files 

       Hands-On Activities

• Create and process lists of employee, customer, inventory, or transaction data. 
• Store records using dictionaries and nested structures. 
• Build routines to identify missing, duplicate, or invalid records. 
• Read a CSV export from a business application. 
• Standardize dates, phone numbers, names, and status values. 
• Generate a cleaned CSV output file. 
• Convert a CSV file into JSON format. 
• Add logging for successful, skipped, and failed records. 
• Organize scripts into reusable modules. 

 

Day 3 — Excel Automation and VBA Macro Migration Using Python

• Excel automation architecture using Python 
• Overview of pandas and openpyxl 
• Understanding common VBA macro patterns: 
  • Reading data from worksheets 
  • Looping through rows 
  • Applying formulas and formatting 
  • Creating reports 
  • Consolidating workbooks 
  • Validating data 
• VBA-to-Python migration methodology: 
  • Identify inputs and outputs 
  • Document business rules 
  • Separate logic from Excel presentation 
  • Rebuild and validate the process 
• Reading Excel sheets, ranges, tables, and cell values 
• Writing Excel output files, worksheets, formulas, and formatting 
• Filtering, grouping, aggregating, and merging Excel data 
• Creating exception reports and audit worksheets 
• Managing missing worksheets, invalid values, and file-access errors 
• Comparing maintainability of VBA procedures and Python modules 

       Hands-On Activities

• Review a sample VBA macro and document its workflow. 
• Identify the macro’s business rules, input file, output file, and exceptions. 
• Read Excel data into a pandas DataFrame. 
• Replace row-by-row VBA-style processing with pandas operations. 
• Create an automated summary report. 
• Consolidate multiple workbooks into one master workbook. 
• Apply formulas, styles, headers, and column widths using openpyxl. 
• Create an exception worksheet for invalid or incomplete data. 
• Refactor the solution into functions and modules. 

 

Day 4 — Data Analysis and Machine Learning Foundations

• AI, machine learning, and data-driven automation concepts 
• Typical AI/ML project lifecycle: 
  • Problem definition 
  • Data collection 
  • Data preparation 
  • Model training 
  • Testing and evaluation 
  • Deployment and monitoring 
• Introduction to NumPy and numerical data processing 
• DataFrames and analysis using pandas 
• Data quality assessment 
• Handling missing values, duplicates, inconsistent values, and outliers 
• Data transformation, filtering, grouping, merging, and aggregation 
• Introduction to charts and exploratory data analysis 
• Features, labels, training datasets, and test datasets 
• Introduction to scikit-learn 
• Classification and regression concepts 
• Model performance measures: 
  • Accuracy 
  • Precision 
  • Recall 
  • Confusion matrix 
  • Mean absolute error 

       Hands-On Activities

• Load a business dataset from Excel or CSV. 
• Profile the dataset to identify quality problems. 
• Clean missing, duplicate, and invalid records. 
• Create business summaries using grouping and aggregation. 
• Visualize trends and patterns in the data. 
• Define a machine learning problem and identify features and labels. 
• Split data into training and testing datasets. 
• Build a basic classification or prediction model. 
• Evaluate model performance and identify improvement opportunities.

 

Day 5 — Building Maintainable Python Solutions and Capstone Development

       Day Purpose: Integrate Python, Excel automation, and AI/ML skills into a practical, maintainable workplace solution.

• Designing a Python solution for maintainability and supportability 
• Project folder structure and separation of concerns 
• Using modules, functions, classes, and configuration files 
• Managing dependencies through requirements.txt 
• Logging, exception handling, and operational troubleshooting 
• Introduction to unit testing and output validation 
• Data privacy and secure handling of business files 
• Scheduling Python automation using Task Scheduler, cron, or orchestration platforms 
• Preparing solutions for deployment, handover, and support 
• Documentation requirements: 
  • Business purpose 
  • Input and output files 
  • Business rules 
  • Error conditions 
  • Operating instructions 
  • Support and enhancement considerations 

Capstone Project Options

Each participant or group will develop one practical prototype:

• Convert an existing VBA macro into a Python application. 
• Create an automated Excel report-consolidation solution. 
• Build an Excel data-validation and exception-reporting tool. 
• Develop a Python process for recurring data cleanup and report generation. 
• Create a machine learning model for classification, prediction, or prioritization. 
• Combine Excel automation with AI/ML-based data analysis. 

Capstone Deliverables

• Working Python source code 
• Sample input and output files 
• Error handling and processing logs 
• Requirements file and setup instructions 
• Technical documentation 
• Brief demonstration of the working solution 
• Recommendations for production deployment or future enhancement