What is eda?

Exploratory Data Analysis (EDA) is the first step in analyzing a new data set. It involves using simple statistics and visual tools to get a feel for what the data looks like, spot patterns, spot problems, and decide what further analysis might be useful. Think of it as “getting to know” your data before you start building models or drawing conclusions.

Let's break it down

  • Collect the raw data from its source.
  • Clean it: handle missing values, correct errors, and standardize formats.
  • Summarize with basic numbers (mean, median, count, min, max).
  • Visualize using charts like histograms, box plots, scatter plots, and bar graphs.
  • Spot outliers, trends, and relationships between variables.
  • Ask questions based on what you see, then decide which deeper analyses or models to try next.

Why does it matter?

  • It helps you understand whether the data is reliable or needs more cleaning.
  • Early insights can reveal hidden opportunities or risks that would be missed later.
  • It guides the choice of appropriate statistical tests or machine‑learning models.
  • By catching problems early, you save time, money, and avoid drawing wrong conclusions.

Where is it used?

  • Data science projects - before training any model.
  • Business analytics - to understand sales trends, customer behavior, or operational performance.
  • Finance - to explore market data, risk factors, and portfolio performance.
  • Healthcare - to examine patient records, trial results, or disease patterns.
  • Research - in any scientific field where data is collected, from ecology to physics.

Good things about it

  • Simple tools (spreadsheets, Python/R libraries) make it accessible to beginners.
  • Visualizations turn numbers into stories that are easy to share with non‑technical people.
  • Quickly identifies data quality issues, saving effort later.
  • Helps generate hypotheses that can be tested more formally.
  • Low cost: most EDA can be done with free software.

Not-so-good things

  • Results can be subjective; different people may interpret the same plot differently.
  • Over‑reliance on visuals may miss subtle statistical relationships.
  • Large data sets can make interactive visual exploration slow or require more powerful tools.
  • EDA does not replace rigorous statistical testing; it only points you in the right direction.
  • Poorly documented EDA steps can make the analysis hard to reproduce.