Make Python useful in everyday data and AI work. Clean datasets, visualize results, apply machine learning, and automate recurring tasks through hands-on examples.
Key Features
- Practical Python coverage spans data preparation, visualization, AI, and everyday automation
- Business and science focus makes data analysis and AI useful beyond professional programming
- Detailed examples connect Python tools to file handling, visual analysis, and routine tasks
Book Description
Everyday data work is a practical starting point for Python, even if programming is not your profession. The opening material introduces the language, file handling, Visual Studio Code, and Jupyter Notebooks. You then use NumPy and pandas to load, clean, filter, and transform data from formats such as CSV, Excel, and JSON, turning raw inputs into material ready for analysis. Visualization brings those results into focus through charts and interactive plots with Matplotlib. From there, the discussion moves to machine learning methods, including linear regression, decision trees, and clustering, before introducing deep learning and AI applications. Text analysis, sentiment analysis, and image classification show how these techniques support different kinds of data projects. Later sections extend Python into APIs, web tools, databases, and routine automation. Flask and Streamlit support lightweight applications, while practical tasks include web scraping, PDF processing, document generation, scheduled data retrieval, and email notifications. By the end of this book, you will be able to connect data preparation, visualization, AI methods, and automation in work relevant to business and science.What you will learn
- Clean and transform data with NumPy and pandas
- Create charts and interactive plots with Matplotlib
- Apply regression, decision trees, and clustering methods
- Analyze text and classify images with AI techniques
- Build lightweight web tools with Flask and Streamlit
- Automate PDF processing, data retrieval, and email notifications
