What is Exploratory Data Analysis?

Have you ever wondered how Data Analysts make sense of raw datasets? How do they figure out what the data is telling before the modeling task? Do they begin their data storytelling journey by mastering Data Analytics courses or with Statistics learning? Both are the learning paths to draw important conclusions about the data.

If you are familiar with Statistics, the concept of using Exploratory Data Analysis (EDA) to discover patterns and insights may not be strange to you. However, if you are new to the world of statistical learning, you will study EDA in the Data Analytics course. What’s more, neither do you need prior command over Statistics or knowledge of programming languages to conquer EDA.

Much like a starter to your full course menu, EDA kick starts your data exploration process and helps you get a handle on the data for making inferences.

So what is Exploratory Data Analysis (EDA)

EDA is the first method used in the data discovery process to help understand and investigate your dataset. What are the possibilities and relationships? What additional information does the dataset reveal? How many variables exist? Are there any missing values, or outliers? What method of analysis or statistical techniques could be most appropriate for further analysis? EDA answers these key questions during the initial exploration of the data.

It is the process used by Customer churn analysis and Data Scientists to conduct primary exploration on the dataset to uncover patterns and relationships, spot inconsistencies, test hypotheses, and check assumptions. This involves manipulating data sources and using various visual tools to achieve the outcomes.

Data Analysis and its types

Data Analysis is the process of applying statistical or logical techniques to describe, illustrate, and evaluate raw data. The purpose of Data Analysis is to extract useful information from the data by cleansing, transforming, and modeling the data for applying the information to data-driven decisions. Inferences are drawn from the data to differentiate “the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data” [Shamoo and Resnik (2003)].

The taxonomy of Data Analysis types helps to establish what type of graphical representations and summary statistics to create for analyzing a dataset. The common types are Descriptive Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis.

Descriptive Analysis summarises the dataset (the ‘what’), Diagnostic Analysis identifies patterns (the ‘why’), Predictive Analysis makes predictions about future outcomes based on historical or current data (the ‘how’) and Prescriptive Analysis uses the insights for deciding the course of action (‘what next’).

What are the Goals of EDA?

The goals of EDA are to maximize insight into the data and the basic structure of the dataset. It is done at the early stage of the Data Analysis lifecycle and decides what steps are taken for modeling or testing.

EDA is used for:

• Obtaining broad insights into the data;
• Understanding relationships in the data;
• Detecting anomalies;
• Spotting outliers;
• Identifying critical factors in the data;
• Experimenting with the assumptions;
• Estimating uncertainties; and
• Concluding which factors are statistically significant.

This includes the application of various graphical techniques for a view into the data, and to help develop predictive or explanatory models.

Types of Exploratory Data Analysis

EDA involves a mix of one or more of the following types of data-processing methods:

1. Univariate non-graphical – This is the simplest form of data analysis, where the raw data being analyzed has only one variable. The main purpose of the univariate analysis is to describe the data and find patterns that exist within it.
2. Univariate graphical – This analysis uses a graphical method like histogram or box plot, to display a complete picture of each variable in the dataset.
3. Multivariate non-graphical: This type of analysis is used to display the relationship between multiple variables, through cross-tabulation or statistics.
4. Multivariate graphical: Here, multivariate data is analyzed graphically to display relationships between two or more sets of variables, like a grouped bar plot.

1. Clustering method: Similar observations in the dataset are grouped distinctly to identify patterns in the data as clusters.

1. Dimensionality reduction: The number of input variables in a large dataset is reduced to capture the most variance in a lower-dimensional space.

The Value of EDA in Data Analytics

The main purpose of EDA is to examine the data before making any assumptions or going into statistical modeling. Exploratory churn analysis helps to validate the raw data and check for technical soundness, thus ensuring that the data was collected without errors. By asking the right questions, EDA also helps stakeholders gain deeper insights that provide relevance for business problems. It navigates the entire data exploration path, from understanding the raw data to the patterns, and visualizing the patterns for a robust understanding of the problem.

Once EDA is performed and data integrity established, the features can then be used for more sophisticated data analysis or modeling, without reverting to feature engineering.

Exploratory Data Analysis Tools

The most commonly used Data Analysis tools to perform EDA are:

Python – Python and EDA can be used together to identify missing values in a dataset, which is important to decide how to handle missing values for say, machine learning. The entire process is automated for time-reduction and value-addition like handling of outliers.

R – The R language is used in developing statistical observations and data analysis, and modeling. Similar to Python, R packages handle the functions of data processing and visualization, even for large datasets with ease.

Summary

EDA is an important step in data analysis, as it makes certain that the outcomes are valid, and interpreted correctly.

Passing over the EDA step can result in skewed data and inaccurate models. So mastering the techniques of EDA are important for any Data Analyst wannabe. Now that you know why EDA is important for analysis, you may want to know how to do it. Dive in and register for the Data Analytics course that will teach you the best practices for EDA.