Problem-First Data Analysis: Applied EDA with Python and PPDAC

Many aspiring analysts make the mistake of diving straight into coding and datasets without a clear plan. True data analysis begins with a well-formulated problem, not just a set of tools. This text-based course shifts your mindset from a tool-focused coder to a strategic thinker. By adopting the structured PPDAC (Problem, Plan, Data, Analysis, Conclusions) cycle, you will learn how to approach any dataset with purpose, structure your investigations, and deliver actionable insights using modern Python. What you'll learn: Understand the foundational concepts of Exploratory Data Analysis (EDA) and the PPDAC lifecycle; Formulate precise analytical questions and design structured plans before writing code; Clean, filter, and prepare raw data using modern Python dataframe libraries; Apply statistical and logical exploration techniques to uncover hidden patterns and anomalies; Synthesize findings into clear conclusions that directly answer your initial business questions; Write clean, readable analysis code that follows modern Python best practices. You will begin by mastering core terminology and the theory behind structured problem-solving. From there, you will read through step-by-step analytical workflows, studying how to translate real-world questions into programmatic Python solutions. This course is designed for beginner analysts, data enthusiasts, and programming novices who want to build a strong analytical foundation. No prior data science experience is required. Start thinking like a seasoned analyst and elevate your Python data skills today.