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Course Outline
Establishing the Business Automation Environment
- Configuring Python 3.12+ for business automation workflows
- Managing dependencies using pip and virtual environments
- Installing and reviewing key libraries: pandas, openpyxl, xlwings, requests, and schedule
- Structuring Python projects to ensure maintainable business scripts
Excel Integration and Workbook Automation
- Reading and writing Excel files using openpyxl
- Programmatically formatting cells, adding formulas, and creating charts
- Leveraging xlwings for real-time Excel interaction and macro replacement
- Integrating pandas with Excel for large-scale data import and export
- Automating the generation of multi-sheet reports and template population
Constructing Automated Quota and Target Systems
- Modeling sales territories, quotas, and performance targets in Python
- Calculating attainment, variance, and forecasts using pandas
- Generating quota assignment matrices and distributing them via Excel
- Building dashboards and summary reports for sales leadership
- Ensuring quota data integrity and managing edge cases
Optimizing Data Analysis
- Implementing efficient data loading and memory management with pandas
- Performing vectorized operations to avoid iterative row-by-row processing
- Utilizing NumPy for numerical optimization and aggregation
- Aggregating and pivoting business data to derive actionable insights
- Connecting to databases and APIs for live data retrieval
Advanced String Processing and Regex for Business Data
- Executing pattern matching and data extraction with regular expressions
- Cleaning and standardizing business text data such as names, addresses, and identifiers
- Validating formats for 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 according to business rules
Web Data Extraction for Business Intelligence
- Fetching and parsing HTML content using requests and BeautifulSoup
- Extracting pricing, competitor, and market data from public sources
- Managing 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
- Dispatching 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 using schedule and cron
- Chaining dependent tasks into end-to-end workflows
- Managing execution logs and output directories
- Implementing error handling and retry strategies for production automation
Debugging, Testing, and Performance Tuning
- Utilizing Python debugging tools to trace automation failures
- Writing assertions and unit tests for business logic components
- Profiling script performance to identify bottlenecks
- Adhering to 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 within a single project
- Applying quota logic, data analysis, and report generation to a real-world scenario
- Conducting review, feedback sessions, and outlining next steps for continued automation development
Requirements
- A solid understanding of Python fundamentals, including variables, loops, functions, and basic data structures.
- Practical experience with file handling and basic data manipulation in Python.
- Knowledge of spreadsheet concepts and basic business reporting workflows.
Target Audience
- Business analysts and operations professionals possessing intermediate Python skills.
- Data analysts aiming to automate reporting and Excel integration workflows.
- Sales operations teams seeking to build and manage quota systems programmatically.
- Professionals tasked with optimizing repetitive data analysis and reporting duties.
21 Hours
Testimonials (3)
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
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Interesting knowledge
