Intermediate Python for Data Science and Analysis

Working with real-world data requires moving beyond basic syntax to structured data manipulation and reliable code design. This comprehensive written course guides you through practical techniques to clean, transform, and structure datasets using intermediate Python programming concepts. You will begin with essential terminology and core data manipulation rules before advancing to modular programming patterns. What you'll learn: - Clean and normalize unstructured text strings using robust string methods and regular patterns - Parse, calculate, and format complex date and time records for time-series investigations - Apply object-oriented programming principles and custom classes to streamline data workflows - Integrate modern Python features such as type hints and defensive error handling - Transform messy nested collections into structured formats ready for analysis - Structure modular Python scripts following standard readability and maintainability practices You will read through clear technical explanations, explore annotated code samples, and reinforce concepts with focused written exercises designed to simulate realistic business scenarios. This course is ideal for learners who understand basic loops and functions and wish to take the next step in data-focused programming without complicated prerequisites. Begin reading today to build dependable, professional data workflows.