In every field where data plays a crucial role, whether it is data analysis, engineering, or modelling, data analysis and investigation, we can say data exploration becomes one of the major tasks to perform before going forward with the data. Therefore, to start your journey in the field of data science, it is always suggested to start by knowing exploratory data analysis(EDA). In this article, we will discuss the exploratory data analysis (EDA) using the following points:
Table of content
- What is Exploratory Data Analysis(EDA)?
- Why is EDA important?
- Types of EDA
- Steps involved in EDA
- Exploratory Data Analysis tools
What is Exploratory Data Analysis(EDA)?
Before working with data, we must understand that data’s characteristics. The exploratory data analysis can explain features/characteristics of the data and understanding of the data. EDA is a process that helps people to understand data, discover patterns of data, and perform hypothesis testing from the data.
EDA plays a crucial role in making a business decision based on old data records. EDA can represent the data using different charts that can be interpreted as a summarisation of the tabular data. EDA also involve mathematics expressions such as standard deviations, categorical variables, and confidence intervals.
Why is EDA important?
EDA becomes essential for any business and allows data analysts, engineers and scientists to analyse and understand data just before consuming it in any process. Mainly EDA ensures that the results after the processing are applicable and valid to business outcomes and goals.
Some other points that make EDA important include:
- It helps in identifying errors in data
- It helps in detecting outliers or anomalous data points and events.
- It helps in drawing and understanding the relationship between variables.
- Visualisation makes decision-making easy.
- It helps in understanding background processes.
Types of EDA
There is mainly four types of EDA we find:
- Univariate non-graphical: One of the simplest forms of EDA or data analysis used to understand the data and patterns of the data. This type of analysis includes only one variable of the data, which is the reason it is not capable of telling the cause and relationship.
2. Univariate graphical: This can be considered an advanced form of non-graphical EDA which is capable of giving more insight from the data than the non-graphical EDA. common Univariate graphical EDA includes:
- Stem-and-leaf plots, help in representing the distribution of the data.
- Histogram, helps in representing the frequency or proportion of classes for a range of values.
- Box plots, help in representing the summary of data in terms of minimum, first quartile, median, third quartile, and maximum value of data.
3. Multivariate nongraphical: When more than one variable is available, this kind of EDA helps in representing the relationship between those variables utilising cross-tabulation or statistics.
4. Multivariate graphical: This kind of EDA uses visual graphs from the data with more than one variable to represent the relationship between variables. Grouped bar charts with each group representing one level of one of the variables can be a typical example of Multivariate graphical EDA.
Some other common examples of this type of EDA include:
- Scatter plot, Helps in representing the relationship between two variables by drawing data points in two-dimensional or three-dimensional space.
- Multivariate chart, Helps in representing the relationship between factors and response.
- Heatmap, Helps in representing the correlations between variables using the density of colours.
Steps involved in EDA
Some of the basic steps of an EDA process include:
- Data collection: the first step start the EDA is to have data from the data source and analyse it at a higher level. It just includes the following things:
Determining the size
Data points observation
Description of dataset
2. Missing value optimisation: this step requires finding the missing value under the data set. After finding it, we are required to recognise the source of the missing value and how we can handle such values by just deleting or filling them.
3. Data categorisation: This step includes the categorisation of the data values so that it can become helpful in performing statistical analysis and visualisation of the data. Following main categories, a dataset can include:
Categorical
Continuous
Discrete
4. Relationship Identification: this step helps in finding the relationship between data variables. For example, the category of weather can be optimised using values of humidity, pressure and wind speed. This example tells us about how weather can change because of continuous values. Finding correlations helps in deriving independent and dependent variables.
5. Outlier detection: after finding the correlation, one step that makes the data processing more accurate is outlier detection. There is always a possibility of the existence of such values in the data, which is different from the other data points. These values become very harmful in data modelling. So it is always suggested to perform this step to avoid wrong data modelling safely.
Exploratory Data Analysis tools
Python and R are the most common tools for EDA:
- Python — It is an object-oriented programing language which includes a lot of libraries like Pandas, Numpy, Matplotlib, Seaborn and Scipy, which makes it one of the best tools to use for data analysis. Pandas library is beneficial for data analysis. Matplotli, Seaborn and Plotly are some libraries that help visualise data. Being easier in terms of coding also makes this language a widely used language for data analysis and data modelling.
- R — it is an open source programming language that gives various facilities to perform statistical computations and analysis, including multiple aspects of data visualisation. This is also a widely used programming language amongst data scientists, analysts and engineers.
- SPSS: SPSS is one of the main tools/software to utilise for statistical analysis. This software has been developed to perform statistical analysis in social science. Although this can be utilised for performing EDA for every kind of data and supports the interactive user interface, it differs from Python and R programming languages.
Conclusion
In this article, we have discussed the EDA, which helps data analysts, engineers and scientists to understand the data by finding essential patterns and information hidden inside the data. Along with this, we have also talked about its importance and steps that can be included while performing EDA. Python and R tools are primarily used tools for performing EDA and can be a good point for beginners to start with EDA.
About DSW
Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.
DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.
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