Raw Data Analysis
Raw Data Analysis is the process of examining, cleaning, transforming, and modeling raw data to discover useful information, draw conclusions, and support decision-making. It involves handling unstructured or unprocessed data directly from sources like sensors, logs, or databases, often using statistical and computational techniques. This foundational step is critical in data science, business intelligence, and research to extract insights before further processing or visualization.
Developers should learn Raw Data Analysis to effectively work with real-world data in fields like data science, machine learning, and analytics, where raw data is messy and requires preprocessing for accurate models. It's essential for tasks such as data cleaning, exploratory data analysis (EDA), and feature engineering, enabling better data-driven decisions in applications like fraud detection, customer behavior analysis, or scientific research. Mastering this skill improves data quality and reliability in downstream processes.