Data Manipulation with Pandas: Building a Gradebook System

Managing and calculating student grades manually can be time-consuming and prone to errors. By leveraging Python and the Pandas library, you can automate these repetitive tasks and gain deep insights into student performance. This course teaches you how to transform raw academic data into a structured, automated grading system. You will start by learning foundational data structures in Pandas, such as Series and DataFrames, before moving on to practical data manipulation techniques. Through written explanations and code examples, you will learn how to clean messy data, handle missing values, and combine multiple datasets. You will also explore modern Python practices, including basic type hints and modern virtual environments, to keep your code clean and maintainable. What you'll learn: - Load and inspect raw student data from various file formats - Clean messy datasets by handling missing values and correcting data types - Merge and join multiple data tables securely using key identifiers - Calculate weighted averages, curves, and final letter grades using Pandas operations - Export formatted summaries and performance reports for easy distribution - Apply modern Python practices like type hints to write robust grading scripts This course begins with essential data concepts and step-by-step setup instructions before guiding you through the logic of data aggregation and grading formulas. You will read through practical scenarios that mirror real-world academic administration tasks. This course is designed for educators, administrators, and beginning data analysts who want to learn Pandas through a practical, hands-on project. No prior data analysis experience is required, though a basic familiarity with Python variables is helpful. Start reading today to automate your grading workflows and master the fundamentals of Pandas.