Understanding Your Data: The Essentials of Exploratory Data Analysis

Have you ever seen a crime scene? Exploratory Data Analysis (EDA) is like the detective work of data science. Before the exciting phase of modelling and predictions in data science, there is always the need first to understand the data you're working with. Like a crime detective, this phase is all about the following:

1. Understanding Your Data
   Begin by figuring out what kind of data you are working with. Are you dealing with integers, floats, categories (objects), dates, or something else? Knowing this informs what tools and techniques to use. You also need to understand the source of your data, whether it is from a survey or a database, as this also informs how you should treat the data.

2. Cleaning Your Data
   Identify issues with your data. These might include missing values, errors, or outliers (unusual data points that don’t fit the pattern). Depending on what you find, you might need to drop a column, fill in missing values, correct errors, scale features, or decide whether to keep or discard outliers. Clean data is the foundation of reliable analysis.

3. Performing Descriptive statistics:
   Calculating the mean, median, and standard deviation gives you a quick sense of the data's shape and tendencies. Here you also look at the distribution of your data, and identify any clusters or gaps.

4. Visualising the Data
   Charts and graphs such as scatter plots, bar charts, or line graphs are powerful tools that reveal trends, patterns, relationships, and correlations that aid analysis. With this, you can compare different groups within your data to identify important relationships.

5. Hypotheses Formulation - Asking the Right Questions
   With all the information garnered from step 1 - 4, you can now begin to formulate your hypotheses. Like a crime detective asks questions during an investigation, you begin to ask the right questions. Questions like: Why did sales rise/fall in the last month? and so on. By attempting to answer these questions, you start to decide what variables to include when you build a predictive model, as you now know the important features in your data.

In conclusion, Exploratory Data Analysis (EDA) is to data what a foundation is to a building. EDA is an essential part of data analysis and must be performed before any major analysis or predictive modelling is done.

Happy exploration!