Introduction to Python for Data Science and Data Cleaning

Every successful data science project relies on clean, reliable data. Before you can build predictive models or generate stunning insights, you must first learn how to read, explore, and clean messy datasets. This text-based course guides you through the foundational concepts of Python and the pandas library, giving you the exact skills needed to tackle real-world data quality issues. You will transition from writing your first line of code to confidently handling missing values, filtering datasets, and structuring data for analysis. What you'll learn: Understand core Python programming concepts, data types, and control flow structures; Explore datasets using Jupyter notebooks to perform initial data quality checks; Manipulate and filter data efficiently using pandas Series and DataFrames; Detect and resolve missing values, duplicates, and formatting inconsistencies in raw data; Apply modern data science best practices, including type hinting and basic vectorization techniques; Export cleaned datasets into optimized formats ready for downstream analysis. Your learning journey begins with essential data science terminology and Python basics before moving step-by-step into hands-on data manipulation and cleaning workflows. This course is designed specifically for beginners with no prior programming experience who want a clear, structured path into data analysis. Start reading today to build a strong foundation in modern data preparation.