Connecting and Transforming External Data: A Practical Guide

In today's data-driven world, valuable information is rarely stored in one neat place. To make informed decisions, you must know how to securely retrieve data from diverse external sources and clean it for analysis. This text-based course guides you from the absolute basics of data structures to building your first automated data transformation workflows. You will gain the confidence to handle messy real-world datasets and prepare them for downstream reporting or analysis. What you'll learn: - Understand core data formats including JSON, CSV, and modern formats like Parquet - Connect to external APIs and databases to retrieve raw data safely - Apply essential cleaning techniques to handle missing values, duplicates, and incorrect data types - Transform and reshape datasets using modern dataframe operations and filtering - Implement basic error handling to make your data connection pipelines resilient - Practice structuring data ingestion workflows through step-by-step written exercises You will start by mastering foundational data concepts and terminology before moving on to hands-on connection and transformation scenarios. Each module provides clear, text-based explanations and practical code examples to reinforce your learning. This course is designed for aspiring data analysts, developers, and beginners who want to work with external data. No prior data engineering experience is required. Start learning today and unlock the power of seamless data integration.