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  Foundations Introduction to Python for Data Science – syntax, Jupyter, libraries. Data Structures in Python – lists, dictionaries, sets, and tuples for data handling. NumPy for Numerical Computing – arrays, broadcasting, linear algebra. Pandas for Data Analysis – dataframes, cleaning, aggregation. Data Visualization Basics – Matplotlib, Seaborn, Plotly. Data Preparation Data Cleaning & Preprocessing

  AI, Data & Automation Build Your Own AI Chatbot with GPT-4/5 APIs – personalised assistants for study, work, or business. Stock Market Price Predictor using Machine Learning – train models on real-time financial data. Automated Resume Optimizer (ATS-Friendly) – parse and score resumes using Python & NLP. AI-Powered Blog Post Generator – generate SEO-optimised

Python Tutorial Walkthrough Table of Contents Add a header to begin generating the table of contents 1. Whetting Your Appetite If you do much work on computers, eventually you find that there’s some task you’d like to automate. For example, you may wish to perform a search-and-replace over a large number of text files, or

Table of Contents Add a header to begin generating the table of contents Scroll to Top General Python FAQ Frequently Asked Questions What is Python ? Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. It supports multiple programming paradigms beyond object-oriented

Terms Description Abstract base class Abstract base classes complement duck-typing by providing a way to define interfaces when other techniques like hasattr() would be clumsy or subtly wrong (for example with magic methods). ABCs introduce virtual subclasses, which are classes that don’t inherit from a class but are still recognized by isinstance() and issubclass(); see