The art of data exploration is not easy and has no shortcuts to it.  Times may arise when it is difficult to improve a model’s accuracy, and knowledge of various data exploration techniques will be a saving grace. In the field of machine learning, the area of data exploration has become quite an area of interest. It might be still evolving; however, by employing machine learning and understanding common patterns, it is indeed possible to identify relationships, patterns or algorithms in a given set of data.

Employment of machine learning is crucial as it mitigates manual labour and time involved in data exploration and reduces the errors that may occur in the employment of manual inspection, trial and or error, or other traditional exploration techniques.

  1. What is Data Exploration
  2. Steps in Data Exploration
  3. Data Exploration Techniques
  4. Why is data exploration important in data analytics?

1) What is Data Exploration

Data exploration is a methodology that is very much like initial data analysis. A data analyst utilizes visual exploration to comprehend the contents of a dataset and its attributes, instead of using data management systems. These attributes could include the size, culmination, accuracy, potential connection between different data components or data files/ tables.

Both manual (drill down or filtering of data to understand similar patterns in data) and automated (data profiling or visualization) methods may be used for data exploration.

Essentially, Data exploration is pruning of data to remove unusable parts and identify potential relationships between different types of data.

2) Steps in Data Exploration

Before delving into the steps involved in data exploration, it is essential to understand that the quality of output is directly proportional to the quality of input. In data exploration, a significant amount of project time is spent on preparation and cleaning of data.

Given below are certain steps that are to be followed while prepping data to build a predictive model-

  • First, it is necessary to identify the input and output variables. Post that, the type and category of the data variables must be made clear.
  • In the next stage, each variable is to be explored independently; one by one. The method used for such analysis can be decided based on whether the variables are categorical or continuous.  

 In the instance that the variables are continuous, the central tendency, as well as the spread of the variable, must be understood. Central tendency is measured using mean, median, mode, min, max etc., and measure of dispersion is through the range, quartile, IQR, Variance, standard deviation, skewness and kurtosis, etc. Visualization methods of Histogram and Box Plot are usually adopted.

 In the case of Categorical variables, a frequency table that reads the percentage of values using count and count% metrics must be used to understand the distribution of each category.

  • To understand the relationship between two variables, the bivariate analysis must be adopted. Here, the association and disassociation between pre-defined significant variables are considered. The variables can be in the following combinations:

 Categorical and categorical: To identify the relationship between two categorical variables two-way table, stacked column chart and chi-square test methods may be used.

 Categorical and continuous: In understanding the relationship between categorical and continuous variables, box plots for each categorical variable level are to be drawn.

 Continuous and continuous: Here, the pattern of scatter plot must be looked into while conducting an analysis between two continuous variables as a scatter plot defines whether the relationship can be linear or nonlinear.

3) Data Exploration Techniques

There are various approaches/ techniques that may be adopted in data exploration. Some of them are:

  • Use of unique value count of categorical columns.
  • To detect how frequently individual values occur in a column. This will give an insight into the content of categorical variables.
  • In analyzing numeric values, the minimum, maximum and variance of the data values provide a good indication of the spread of values.
  • Pareto analysis is effective in data exploration as well.
  • Histogram can be used to get information for a range of values falling in the majority sector. It points out any skew in the data and indicated the maximum and minimum values of the data as well.
  • A correlation heat map between all numeric columns is a great way to understand the relationship between various types of data.
  • The method of Pearson correlation is used to understand the trend between two numeric columns.
  • Another effective data exploration is Cramer V that correlated between all categorical columns.
  • Cluster size analysis is often adopted to tackle huge amounts of data wherein data is split into different groups/ clusters and then analyzed
  • Outlier detection is used when there is something unusual in the data. Here, standard deviation analysis methods or algorithms like Isolation forest are used to obtain outlier values in numeric columns. Outlier methods can be used for multiple columns.
  • Specialized visualization moves from bar charts and scatters plots to radar charts, neural network visualization and Sankey charts.

4) Why is data exploration important in data analytics?

Human beings measure visual data better than mathematical data; hence it is quite challenging for data scientists and data analysts to allocate significance to thousands of rows and columns of data and to impart said information without any visual parts.

Data visualization in data exploration uses familiar visual signals, for example, shapes, dimensions, colours, lines and angles with the goal that data analysts can successfully imagine and characterize the metadata and post that, perform data exploration. Playing out the underlying advancement of data exploration, data analysts are empowered to comprehend and distinguish variations and possible relationships that may have otherwise gone undetected.


At the end of the day, data exploration might take some efforts. It might involve large sets of data being identified and sorted using various techniques. These techniques may require time and efforts to understand and adopt. However, it differentiates a good model from a bad one.

In a world where data is often accumulated in large, unstructured volumes from sources all across the world, it is essential to understand and have a comprehensive view of the data. Such a comprehensive view Is necessary to be able to use the data collected for further analysis.

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