Data Acquisition and Cleaning Foundations

Raw data is rarely ready for analysis, often arriving incomplete, poorly formatted, or scattered across different sources. To make accurate data-driven decisions, you must first know how to gather and structure this information correctly. This course guides you through the foundational principles of data acquisition and cleaning. You will progress from understanding core data formats to importing files, querying APIs, and implementing modern tidy data principles that streamline all downstream analysis and reporting. What you'll learn: - Understand the foundational concepts of raw versus processed data and the structure of a complete data package. - Acquire data from diverse sources, including flat files, web scraping, databases, and public APIs. - Apply tidy data principles to reshape, merge, and clean messy datasets for efficient analysis. - Handle common data quality issues such as missing values, inconsistent formatting, and duplicate records. - Document your data preparation process using codebooks and clear processing instructions to ensure reproducibility. - Implement basic data validation checks to automate quality control before performing analysis. Starting with fundamental definitions, you will read through step-by-step explanations of data formats, API connections, and restructuring workflows, building a reliable methodology for preparing data. This course is designed for beginners with no prior data engineering experience, making it ideal for aspiring analysts, researchers, and business professionals who want to work confidently with real-world data. Start reading today to transform chaotic raw data into structured, reliable insights.