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What Is Exploratory Data Analysis in Data Science? Tools, Process & Types

Introduction to Exploratory Data Analysis (EDA)

Exploratory Data Analysis refers to the process of cleaning and transforming data for analysis and creation of models. The ultimate goal of data analysis is to extract informative insight from data models. Exploratory data analysis is critical for impactful decision-making in businesses. 

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Read on to learn more about the tools, types, and processes of EDA in data science.

Why Is EDA Important in Data Science?

Exploratory Data Analysis is a set of techniques for extracting crucial trends and patterns from big data using deep learning and machine learning. EDA helps make critical business decisions by analysing vast volumes of data. The significance of EDA lies in the data analysis objectives as listed below:

  • Identification and removal of data outliers
  • Identification of patterns about the target
  • Identification of trends in space and time
  • Discovery of new data sources
  • Creation of hypotheses and examination of the same through rigorous experimentation 

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Steps in EDA

The Exploratory Data Analysis steps are described below:

1. Collection of data

Every industrial sector generates tremendous volumes of data. Business organisations can use the data only after collection and analysis. EDA in data science begins with collecting data through surveys, customer reviews, client feedback, polls on social media, and other modes. Collecting relevant data is the first step of data analysis.

2. Identification and understanding of variables in data

The process of analysis begins with the extraction of information from the data. The information reveals dynamic values related to various characteristics helping obtain insights from the data. It is pertinent to identify the key variables influencing the impact of data analysis to extract invaluable insights.

3. Cleansing datasets

Cleaning the datasets involves eliminating irrelevant information, anomalies, outliers, and null values from the data. Cleaned datasets enhance productivity and make the highest quality information available for effective decision-making. Moreover, data cleaning also helps save time and computational power.

4. Identification of correlated variables

A correlation among variables reveals the relationships among the significant data variables. The data analyst prepares a correlation matrix to represent the correlation among variables.

5. Selecting the correct statistical method

A data analyst selects statistical methods and tools based on the categorical or numerical form of data, the purpose of analysis, and the data types of the different variables. The statistical report provides unbiased information and represents the data through graphical charts and bars.

6. Visualization and analysis of results

The data analyst interprets the statistical report to disclose trends and patterns in datasets. The trends and patterns are combined with variable correlation information to obtain valuable insights from the data. Business organisations of different industrial sectors use data analysis results to improve and expedite decision-making.

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Types of EDA

Exploratory Data Analysis is of three types, as described below:

Univariate data analysis

In univariate data analysis, the entire dataset is collected for the output, which is a single variable. The data simply discloses the products produced every month in a year. Univariate data analysis does not concern itself with cause-and-effect relationships.

Univariate data analysis can be both graphical and non-graphical.

Graphical univariate analysis is performed on Auto MPG datasets. Univariate graphics include histograms and stem-and-leaf plots. Non-graphical univariate analysis is for identifying the distribution of population data based on specific statistical parameters. The parameters include central tendency, range, and standard deviation. 

Bivariate data analysis

In bivariate data analysis, the outcome of the analysis is dependent on two data variables. There also exists a cause-and-effect relationship between the analysis outcome and the variables.

Multivariate data analysis

In multivariate data analysis, there are more than two types of outcomes. The data analyst performs multivariate data analysis on both categorical and numerical variables. The data analyst represents the data analysis report in graphical, visual, or numerical forms.

Non-graphical multivariate data analysis is performed to show the relationship among variables by using statistics and cross-tabulation techniques. On the other hand, graphical multivariate analysis involves using graphs to represent the connections among variables. Multivariate data analysis graphics include scatter plots, multivariate charts, bubble charts, run charts, and heat maps.

EDA Tools and Techniques

The tools and techniques employed to perform EDA in data science are given below:

Python:

Data analysts conduct Exploratory Data Analysis (Python) to identify missing values in data collection, formulate the data description, handle outliers, and extract insights from graphs.

MATLAB:

MATLAB is used in pre-processing datasets for identifying trends in data. Data analysts also use MATLAB to create customised models, visualisations, and algorithms.

Power BI:

Power BI is a data visualisation and business intelligence tool enabling big data exploration and summarisation.

R:

The programming language R is used to analyse big data and make statistical observations. R provides powerful libraries, such as Data Explorer and SmartEDA, to perform automated EDA in data science.

Tableau:

Tableau is a tool for data visualisation that allows the creation of interactive dashboards and visualisations.

Handling the tools and techniques of EDA in machine learning requires a great degree of expertise. 

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Common Visualisation Techniques Used in EDA

Data visualisation helps in identifying trends and patterns in datasets. The most common techniques of data visualisation in EDA are listed below:

  • Histogram: A histogram is used to represent both grouped and ungrouped data. 
  • Scatter plot: Scatter plots are used in bivariate data analysis to graphically represent the relationship between two quantitative variables in a dataset.
  • Stem-and-leaf plot: Stem-and-leaf plots display quantitative data in a short format.
  • Multivariate chart: Multivariate charts help visualise the relationships among all numerical variables of the entire dataset at once.
  • Run chart: A run chart represents the data values or process performance during a period.
  • Bubble chart: Bubble charts are used in assessing the relationships among multiple variables for data analysis.
  • Heat map: A heat map is a colourful graph of multivariate data in the form of rows and columns. Heat maps help in developing accurate models of EDA machine learning.

Best Practices for Effective EDA

Adhering to the following best practices can help data analysts employ EDA effectively:

  • Setting down a clear objective of the EDA
  • Ensuring that the purpose of the EDA aligns with the desired outcome of the analysis
  • Ensuring that the right questions are asked during the data collection stage
  • Maintaining data privacy and preserving the confidentiality of sensitive data during EDA
  • Being aware of domain knowledge and existing problems in the domain for which the EDA is required

Real-world Examples of EDA in Action

Given below are some practical applications of EDA (data science):

  • Retail

Let’s take an example of a retail store selling different types of clothing, such as dresses, shirts, shorts, blouses, skirts, and tees. EDA helps identify sale trends and enables the retail store owner to visualise data on buyer preferences, customer spending patterns, and the best-selling product in each clothing category. Such an analysis is essential for drawing in more customers to boost sales.

  • Clinical trials

In clinical trials, medical researchers use EDA to recognise outliers in the patient population to verify population homogeneity.

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Challenges in EDA

The execution of EDA can be tedious for data analysts. They must conduct repetitive tasks in a limited period, resulting in erroneous data analysis reports. Moreover, data analysts often lack the domain knowledge crucial for efficient data analysis. Another challenge that data analysts face is the need to maintain compliance with stakeholders’ interests, which results in neglecting essential variables.

The challenges can be overcome to a great extent by the use of advanced EDA tools and techniques.

Conclusion

EDA plays a crucial role in data science. Through EDA, data analysts can detect patterns, relationships, and trends in data to extract invaluable insights. With advanced tools and techniques, EDA can be performed for market analysis, customer feedback analysis, financial planning, making successful predictions in the stock market, and more. If you seek to build your career as a data analyst, take upGrad’s Executive PG programme in Data Science from IIITB.

Frequently Asked Questions

Are data mining and EDA the same?

Data mining and Exploratory Data Analysis (EDA) are not the same, although they are related concepts within the field of data science. Data mining refers to various data extraction processes to discover valuable insights from vast datasets. However, EDA refers to a specific method of data analysis and summarisation.

What happens during the data cleaning stage of data analysis?

Data cleaning occurs by eliminating missing values, redundant rows and columns, and other anomalies, followed by the reformatting and re-indexing of data.

What are the types of histograms used for data visualisation in EDA?

Data analysts visually represent data using different types of histograms, including box plots, percentage bar charts, grouped bar charts, and simple bar charts.

 

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