Data Analysis and Interpretation: From Cleaning to Regression

Raw data is meaningless without the skills to clean, analyze, and interpret it. This text-based course guides you through the essential foundations of modern quantitative analysis, turning messy datasets into clear, actionable insights. You will start with the absolute basics of data terminology and structure before moving on to practical data preparation. By the end of this course, you will understand how to evaluate data quality, measure relationships using correlation, and construct regression models to predict outcomes. What you'll learn: - Understand foundational data concepts, scales of measurement, and descriptive statistics. - Prepare and clean raw data using modern validation techniques to ensure quality and consistency. - Analyze scale internal consistency to evaluate the reliability of your data. - Measure linear relationships between variables using the Pearson correlation coefficient. - Build and interpret multiple regression models to understand complex data relationships. - Draw valid, data-driven conclusions while avoiding common analytical pitfalls. The course begins with core definitions and data preparation workflows, then advances step-by-step through statistical relationships, correlation, and regression analysis. Through clear written explanations and practical data scenarios, you will build a solid analytical foundation. This course is designed for absolute beginners, aspiring data analysts, and researchers who want to learn data analysis from the ground up. No prior statistical or programming experience is required. Start reading today to unlock the power of data-driven decision-making.