Exploratory data analysis
Imagine you’re on an adventure, about to embark on a journey into the unknown. You’ve just been handed a treasure map, with the promise of valuable insights waiting to be discovered. This map is your data set, and the journey is exploratory data analysis (EDA).
As you begin your exploration, you start by getting a feel for the terrain. You take a broad, bird’s-eye view of the data, examining its structure and dimensions. Are you dealing with a vast landscape or a small, confined area? Are there any missing pieces in the map that you’ll need to account for? Understanding the overall context of your data set is crucial before venturing further.
With a sense of the landscape, you now zoom in to identify key landmarks in the data. You might look for unusual patterns, trends, or relationships between variables. As you spot these landmarks, you start asking questions: What’s causing that spike in values? Are these two factors related, or is it just a coincidence? By asking these questions, you’re actively engaging with the data and forming hypotheses that could guide future analysis or experiments.
As you continue your journey, you realize that the map alone isn’t enough to fully understand the terrain. You need more tools to bring the data to life. You start visualizing the data using charts, plots, and graphs. These visualizations act as your binoculars, allowing you to see patterns and relationships more clearly. Through them, you can uncover the hidden treasures buried within the data.
EDA isn’t a linear path from start to finish. As you explore, you’ll find yourself circling back to previous points, refining your questions, and digging deeper. The process is iterative, with each new discovery informing the next. And as you go, you’ll gain a deeper understanding of the data’s underlying structure and potential.
Finally, after your thorough exploration, you’ll have a solid foundation to build upon. You’ll be better equipped to make informed decisions, test hypotheses, and draw meaningful conclusions. The insights you’ve gained through EDA will serve as a compass, guiding you towards the true value hidden within your data. And with that, you’ve successfully completed your journey through exploratory data analysis.