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What is Data Exploration or Exploratory data analysis?

Data Exploration or Exploratory data analysis (EDA) provides a simple set of exploration tools that bring out the basic understanding of real-time data into their analytics. The outcomes of the data exploration can be playing a powerful factor in understanding the structure of data, values distributions, and interrelationships. The data exploration can also be helpful for data scientists to gain proper insights into business data that was not easily dome in older days. By measuring unlimited features of data exploration, we can say that it is key to the success of these tools and analytics.

Data exploration is the first step in data analytics. Understanding business data is essential for making a well-planned decision, which usually involves summarizing on the main feature of a data set such as its size, pattern, characteristics, accuracy, and more.
The entire process is conducted by a team of data analysts using visual analysis tools and some advanced statistical software like R. Data exploration can use a combination of manual methods and automated tools such as data visualization, charts, and preliminary reports.

Related Content: R vs Python for Data Science

What is Data Preparation?

Data preparation is typically used for proper business data analysis. The data preparation process involves collecting, cleaning, and consolidating data into a file that can be further used for analysis.

Why Data Preparation is necessary?

  • To filter unstructured, inconsistent and disordered data
  • Connecting data from real time multiple data sources
  • For quick reporting of data
  • To handle data collected from a scraped file like PDF document

Procedure of Data Preparation

data exploration and data preparation

Here we will discuss the standard data preparation procedure which has been followed by every business.

  1. Gather data

This is an initial process for each business. In this phase it is necessary to collect data from various sources, the sources can be of any type such as from catalogs or ad-hoc can be added.

  1. Discover data

Next step is discovering the data, here it is very important to understand the data and categorized into different datasets. This step might take long time to filter because of huge collection of datasets. Here be patience in the work as this can be helpful in next process.

  1. Cleaning and validating the data

This is necessary to remove faulty and critical data that you think may not be useful in the next step. Important steps need to be taken here:

  • Removing unnecessary data and outliers.
  • Use appropriate pattern for refining all the data.
  • Use lock to protect your sensitive data.
  • Fill the empty space for data flow.

After cleaning the data, it should go through the test team where all the refine data has to be rechecked.

  1. Transforming the data

Transforming the data defines maintaining the format or value entries in order to meet well define output and can clearly understand the wider audience.

  1. Storing of data

This is the final step after going through all the above processes. Once the data is cleaned it is ready to offer third party tools such as business intelligence tools for analysis.

Benefits of Data Preparation

Here are few benefits of data preparation and why it is preferred by all the business before analysing the data.

  • Quick response in fixing the error before processing.
  • Producing fine data by cleaning and reformatting the datasets.

Higher quality data helps you to analysed more effectively and quickly the data and to take proper business decisions.

Data Exploration Role

Here we will describe how many cases are in the data set, which variables are included, how many missing values and what hypothesis is used. Once data discovery has revealed the relationships between different variables, organizations can continue the process of building data models to obtain and receive data.

Data Exploration methods

There are two formats of data exploration automatically and manual. Mostly analysts preferred automated methods such as data visualization tools because of their accuracy and quick response. Where as manual data exploration methods include filtering and drilling down into data in Excel spreadsheets or writing scripts to analyse raw data sets.

data exploration and data preparation

Data exploration plays an essential role in the data mining process.

There are several techniques of analysing data such as:

Univariate analysis: It is the simplest form of analyzing data. Univariate as suggested means that there is only one variable in your data.

Bivariate analysis: It is the simplest form of quantitative analysis. It includes the analysis of two variables (as x,y) particular used for calculating the empirical relationship between two variables.

Multivariate analysis: Multivariate Analysis can be used to refer to any analysis that involves more than one variable (e.g. in Multiple Regression or GLM ANOVA).

Principal components analysis: The analysis and conversion of possibly correlated variables into a smaller number of uncorrelated variables.

The next step after data exploration is data discovery. In this phase business intelligence tools are used to inspect trends, sequences and events and creating visualizations to present to business managers.

Data Exploration tools

Many business intelligences tools and data visualization software are available. Some commonly used tools used data analysts are Microsoft Power BI, Qlik and Tableau.

Many business intelligences tools and data visualization software are available. Some commonly used tools used by data analysts are Microsoft Power BI, Qlik and Tableau.

Data Exploration and Preparation steps

The quality of the output always depends on the quality of the input. So, make the input value so versatile so that the output remains constant.

Below are the steps to understand, clear and prepare your data for building your predictive model:

  1. Variable Identification
  2. Univariate Analysis
  3. Bi-variate Analysis
  4. Missing values treatment
  5. Outlier treatment
  6. Variable transformation
  7. Variable creation

Let’s start discussing each step-in detail

Variable Identification

In this step you have to first identify the input and output variables. Then identify the datatype and category of the variables.

Let focus more by applying one real-time example

Suppose a school want to predict the ratio (pass or fail) of student result. Here you need to collect predictor variables, target variables, data types and category of the variable.

Below, the variables have been defined in different category:

data exploration and data preparation

data exploration and data preparation

Univariate Analysis

In univariate analysis variables are explore one by one. This method depends on whether variable type is categorical or continuous.

Categorical Variables: This is also called as discrete variable that has two or more categories(values). It can be measured using two metrics, Count and Count% against each category. Bar chart can be used as visualization.

Continuous Variables: Continuous variable is a quantitative variable in which the data is measured in height, weight or time. Continuous variables can take on almost any numeric value and can be meaningfully divided into smaller increments, including fractional

Bi-variate Analysis

Bivariate analysis means the analysis of bivariate data. It used to find out if there is a relationship between two sets of values. It usually involves the variables X and Y.

Examples of Bi-Variate Analysis

  • Scatter Plots
  • Regression Analysis
  • Correlation Coefficients

data exploration and data preparation

Missing values treatment

It is necessary to understand the concept of missing values for the researcher because if it not handled properly then inaccurate interference occurs in the data. It can lead to wrong prediction and classification.

data exploration and data preparation

Outlier treatment

Outlier is a data point that is distant from other different point. These outliers should remove from datasets. This can be identified directly looking at the data table or work sheet.

data exploration and data preparation

Variable transformation

Data does not always come in a form that is immediately suitable for analysis. We often have to change variables before applying them to analysis. A transformation is a recursion of data using a function or some mathematical operation on each observation.

Conclusion

It is clear from the above discussion that by using the right online BI tools, an organization can easily detect and present data effectively. However, as with anything, having a plan and focus yields the best results.
You can use this detail information on data discovery and data preparation before you start analyzing data.

Abhishek Sharma

Abhishek Sharma

Software Developer

Abhishek is working as a Web Graphics Designer at EzDataMunch. He is involved in Maintaining and enhancing websites by adding and improving the design and interactive features, optimizing the web architectures for navigability & accessibility and ensuring the website and databases are being backed up. Also involved in marketing activities for brand promotion.

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