1
Front Matter
5 chapters2
Part 1: Getting Started with Python
9 chapters- Part 1: Getting Started with Python
- Chapter 1: Why Python? The Business Case for Coding
- Chapter 2: Setting Up Your Python Environment
- Chapter 3: Python Basics — Variables, Data Types, and Operators
- Chapter 4: Control Flow — Making Decisions in Your Programs
- Chapter 5: Loops and Iteration — Automating Repetitive Tasks
- Chapter 6: Functions — Building Reusable Business Logic
- Chapter 7: Data Structures — Lists, Tuples, Dictionaries, and Sets
- Chapter 8: Error Handling — Writing Robust Business Applications
3
Part 2: Working with Business Data
9 chapters- Part 2: Working with Business Data
- Chapter 9: File I/O — Reading and Writing Business Data
- Chapter 10: Introduction to pandas: Your Business Data Toolkit
- Chapter 11: Loading and Exploring Real Business Datasets
- Chapter 12: Cleaning and Preparing Data for Analysis
- Chapter 13: Transforming and Aggregating Business Data
- Chapter 14: Introduction to Data Visualization with matplotlib
- Chapter 15: Advanced Charts and Dashboards with seaborn and plotly
- Chapter 16: Excel and CSV Integration — Python Meets Spreadsheets
4
Part 3: Automation and Productivity
9 chapters- Part 3: Automation and Productivity
- Chapter 17: Automating Repetitive Office Tasks
- Chapter 18: Working with PDFs and Word Documents
- Chapter 19: Email Automation and Notifications
- Chapter 20: Web Scraping for Business Intelligence
- Chapter 21: Working with APIs and External Data Services
- Chapter 22: Scheduling and Task Automation
- Chapter 23: Database Basics — SQL and Python with SQLite and PostgreSQL
- Chapter 24: Connecting Python to Cloud Services
5
Part 4: Business Analytics
9 chapters- Part 4: Business Analytics
- Chapter 25: Descriptive Statistics for Business Decisions
- Chapter 26: Business Forecasting and Trend Analysis
- Chapter 27: Customer Analytics and Segmentation
- Chapter 28: Sales and Revenue Analytics
- Chapter 29: Financial Modeling with Python
- Chapter 30: HR Analytics and People Data
- Chapter 31: Marketing Analytics and Campaign Analysis
- Chapter 32: Inventory and Supply Chain Analytics
6
Part 5: Advanced Business Applications
9 chapters- Part 5: Advanced Business Applications
- Chapter 33: Introduction to Machine Learning for Business
- Chapter 34: Predictive Models — Regression and Classification
- Chapter 35: Natural Language Processing for Business Text
- Chapter 36: Automated Report Generation
- Chapter 37: Building Simple Business Applications with Flask
- Chapter 38: Deploying Python to the Cloud
- Chapter 39: Python Best Practices and Collaborative Development
- Chapter 40: Building Your Python Business Portfolio
7
Appendices
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