Understanding Your Data: The Essentials of Exploratory Data Analysis (EDA)

Data science has become one of the fastest-growing fields with huge demands for skilled data scientists. Exploratory Data Analysis(EDA) is a popular method for analyzing and presenting data sets used by these professionals. Developed as the most comprehensive data analysis technique for data science projects, EDA has effectively contributed to providing maximum insight into the data set and data structures.
EDA is nothing but a data exploration technique to understand the various aspects of the data. It includes several techniques in a sequence that we have to follow.

The phases of exploratory data analysis can be summarized in 7 steps :

• Know which problem area you will be covering and which questions you would answer.
• Get a general idea of ​​the dataset.
• Define the types of data you have.
• Choose the type of descriptive statistic.
• Visualize the data.
• Analyze the possible interactions between the variables of the dataset.
• Draw some conclusions from all this analysis.

Exploratory Data Analysis Tools

1. Python – Python is an object-oriented programming language with high-level, built-in data structures. Other features like dynamic typing and dynamic binding work in favor of EDA. Python is extensively used to connect existing components and identify missing values in a data set.
2. Matplotlib – Matplotlib is one of the most widely used in data science for all kinds of graphics, such as bar charts, scatter charts, fever charts, and maps with Basemap, etc. Seaborn, another Python library based on Matplotlib, enables data scientists to create explanatory graphs from highly complex data.
3. R – R is an open-source programming language in statistical computing and graphics. It has a wide range of applicability in statistical observations and data analysis.
4. ggplot2 – ggplot2 is a library that allows bar, point, line, area, maps, and scale charts. ggplot2 depends on other packages that need to be downloaded and installed.

Importance of EDA

• Data Cleaning: Identifying and handling missing values, outliers, and inconsistencies.
• Feature Engineering: Creating new variables or transforming existing ones.
• Model Selection: Choosing appropriate models based on data characteristics.
• Insight Generation: Discovering hidden patterns and trends

By efficiently conducting EDA, you provide a solid basis for your data analysis journey. It enables you to discover the story concealed in your data and make informed decisions.