Data Importing and Cleaning with Python

Before you can build predictive models or generate business insights, you must know how to handle messy, incomplete, and disorganized data. This course teaches you the foundational techniques to gather data from various sources and transform it into a clean, structured format ready for analysis. You will start by understanding essential data quality concepts, terminology, and the structure of tabular datasets before moving into practical manipulation. Through step-by-step written explanations, you will learn how to read diverse file formats, interact with web APIs, scrape web pages, and clean up common data anomalies. What you'll learn: Understand core data quality principles and identify common data anomalies; Import data from CSV files, Excel spreadsheets, relational databases, and web APIs; Clean messy datasets by handling missing values, duplicate records, and incorrect data types; Apply regular expressions and string manipulation to standardize text data; Use modern pandas workflows and modern dataframe libraries to filter and merge datasets; Validate data integrity and handle outliers using programmatic checks. The course begins with foundational definitions of data structures, moving systematically through ingestion, structural cleaning, and advanced validation techniques. This course is designed for beginners who have a basic understanding of Python syntax and want to build practical data preparation skills, with no prior data science experience required. Start preparing your data for professional analysis today.