Intro to Python and AI

Your first day will be an introduction to programming concepts in Python. You will be able to answer questions like: How do computers understand instructions? How is Python used in real life? How are tech companies using Python? We’ll work on setting up your development environment using an online Python interpreter (Google Colab) and you will get a glimpse of how variables and string manipulation are used.

You’ll review previous concepts before doing a deep dive into functions - creating and calling functions, parameters and return values, and interactive coding with AI assistants.

We’ll use AI a personal tutor to help debug our code (and discover its limitations as well) which we build a multi-function program that uses variables, strings, and user input.

Introduction to data structures such as lists, dictionaries, and arrays. You’ll practice creating, modifying, and accessing items in each data structure and start to make real-world connections on how how platforms like Spotify use data structures. We’ll discover how playlist creation algorithms and recommendation systems work.

Since you’ve been practicing with lists and dictionary operations at this point, you will be introduced to advanced list and dictionary operations. Comparing and analyzing data with the Python library “Seaborn” using its visualization functions will be one of the major highlights of the class. You’ll learn how to programmatically create graphs from datasets, interpret visual data, use AI to explain data visualization techniques.

Students will master Python file handling as they learn to read, write, and manipulate CSV files containing real-world datasets. You’ll explore functions in depth, creating reusable code blocks that efficiently process data while understanding parameter passing and return values. By class end, students will select their own dataset (climate patterns, sports statistics, or entertainment trends), load it into your project environment, and begin initial data exploration with pandas.

Building on your dataset exploration, students will develop advanced data extraction techniques to identify patterns and insights. You’ll learn to craft compelling narratives from numerical data, transforming abstract numbers into meaningful stories. With individual coaching and AI assistant support, you’ll structure your data projects, implement custom functions for analysis, and prepare the initial framework for your data narrative presentation.

You’ll discover how to amplify your Python capabilities through packages and APIs. You’ll learn to install and utilize third-party libraries that extend Python's functionality, connect to public APIs for real-time data access, and integrate external functions into their projects. Practical demonstrations will include accessing weather data, sports statistics, or entertainment information APIs, allowing students to enhance their projects with dynamic, real-world data sources.

The final class transforms students into professional developers as you learn GitHub basics for code sharing and version control. After creating your first repository and pushing their project code, students will present their data analysis findings in 5-minute showcases. The session concludes with structured peer feedback, guided reflection on their coding journey, and a celebration of their transformation from coding novices to data scientists capable of telling compelling stories through Python.