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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 overviewing 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.
- Utilizing 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.
- Implementing 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.
- Adopting 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 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 looking to automate reporting and Excel integration workflows.
- Sales operations teams aiming to build and manage quota systems programmatically.
- Professionals responsible for optimizing 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
