Python for Data Science #2: Numbers, Strings and Functions

NB Link - https://github.com/abhirajsuresh/Python-for-Data-Science Welcome back to our "Python for AI" series! In this second episode, we dive deeper into the essential Python building blocks that are crucial for any aspiring data scientist or AI practitioner. Building on the fundamentals from Week 1, this video will guide you through more intermediate concepts to strengthen your programming foundation. What you'll learn in this video: - Numbers & Math: Go beyond basic arithmetic and explore useful math and statistics libraries in Python. - String Manipulation: Master advanced string operations including slicing, common methods, and powerful f-strings for formatting. - Python Collections: A deep dive into lists, tuples, dictionaries, and sets, including their methods and when to use each. - Functions & Lambdas: Learn how to write your own functions, understand variable arguments (*args & **kwargs), and use anonymous (lambda) functions. - Iteration Helpers: Use powerful built-in functions like range, enumerate, and zip to write cleaner and more efficient loops. - Error & File Handling: Learn how to gracefully handle errors using try/except blocks and how to read from and write to files on your system. - Imports & Modules: Understand how to import and use code from other Python files and modules, a key skill for building larger projects. By the end of this tutorial, you will have a robust understanding of the Python concepts that form the backbone of data analysis, machine learning, and AI development. Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest content! Chapters: 0:00 - Introduction & Recap of Week 1 0:59 - Setting Up the Environment: Anaconda & Jupyter Notebook 1:34 - Agenda Overview for Week 2 2:12 - Numbers & Math in Python 2:53 - Using Math & Statistics Libraries 5:39 - String Manipulation: Slicing, Methods & F-Strings 8:21 - Python Collections: Lists (Mutable) 9:39 - Python Collections: Tuples (Immutable) 10:03 - Python Collections: Dictionaries (Key-Value Pairs) 10:33 - Python Collections: Sets & Set Operations 11:28 - Comprehensions (List, Dict, Set) 12:39 - Deep Dive into Python Functions 14:14 - Lambda Functions, Map, and Filter 15:28 - Iteration Helpers: Range, Enumerate, & Zip 16:40 - Error Handling in Python (Try/Except) 18:40 - Working with Files (Read & Write) 20:05 - Importing Modules and Local Scripts 21:49 - Conclusion and Next Steps