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Course Outline
Setting Up the Business Automation Environment
- Configuring Python 3.12+ for business automation workflows
- Managing dependencies with pip and virtual environments
- Installing and overview of key libraries: pandas, openpyxl, xlwings, requests, schedule
- Structuring Python projects for maintainable business scripts
Excel Integration and Workbook Automation
- Reading and writing Excel files with openpyxl
- Formatting cells, adding formulas, and creating charts programmatically
- Using xlwings for real-time Excel interaction and macro replacement
- Integrating pandas with Excel for large-scale data import and export
- Automating multi-sheet report generation and template population
Building Automated Quota and Target Systems
- Modeling sales territories, quotas, and performance targets in Python
- Calculating attainment, variance, and forecasting using pandas
- Generating quota assignment matrices and distributing them via Excel
- Building dashboards and summary reports for sales leadership
- Validating quota data integrity and handling edge cases
Data Analysis Optimization
- Efficient data loading and memory management with pandas
- Vectorized operations and avoiding iterative row-by-row processing
- Using NumPy for numerical optimization and aggregation
- Aggregating and pivoting business data for actionable insights
- Connecting to databases and APIs for live data retrieval
Advanced String Processing and Regex for Business Data
- Pattern matching and data extraction with regular expressions
- Cleaning and standardizing business text data (names, addresses, identifiers)
- Validating formats such as emails, phone numbers, and invoice codes
- Applying regex to log files and unstructured business documents
File and Document Automation
- Processing CSV and JSON data for ETL and reporting pipelines
- Reading and extracting data from PDFs for invoice and statement processing
- Automating Word document generation for contracts and proposals
- Organizing, renaming, and archiving files based on business rules
Web Data Extraction for Business Intelligence
- Fetching and parsing HTML content with requests and BeautifulSoup
- Extracting pricing, competitor, and market data from public sources
- Handling pagination, authentication, and API rate limits
- Storing scraped data into structured formats for downstream analysis
Automating Reports and Communication
- Generating formatted HTML and Excel reports from analysis results
- Sending automated emails with attachments using SMTP
- Creating scheduled summary reports for stakeholders
- Templating dynamic content based on business logic and thresholds
Scheduling and Orchestrating Business Processes
- Automating script execution with schedule and cron
- Chaining dependent tasks into end-to-end workflows
- Managing execution logs and output directories
- Error handling and retry strategies for production automation
Debugging, Testing, and Performance Tuning
- Using Python debugging tools to trace automation failures
- Writing assertions and unit tests for business logic components
- Profiling script performance and identifying bottlenecks
- Best practices for writing reliable and maintainable automation code
Capstone: End-to-End Business Automation Workflow
- Designing a complete automation pipeline from raw data to final report
- Integrating Excel, pandas, email, and scheduling in a single project
- Applying quota logic, data analysis, and report generation to a real scenario
- Review, feedback, and next steps for continued automation development
Requirements
- A solid understanding of Python fundamentals, including variables, loops, functions, and basic data structures.
- Experience in working with file handling and basic data manipulation in Python.
- Familiarity with spreadsheet concepts and basic business reporting workflows.
Audience
- Business analysts and operations professionals with intermediate Python skills.
- Data analysts seeking to automate reporting and Excel integration workflows.
- Sales operations teams looking to build and manage quota systems programmatically.
- Professionals responsible for optimising repetitive data analysis and reporting tasks.
21 Hours
Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Examples/exercices perfectly adapted to our domain
