Exploratory data analysis (EDA) is an important step in any research analysis). EDA’s main goal is to look at the distribution, outliers, and anomalies in the data to guide specific testing of your hypothesis. In order to make the data easier to understand, it uses graphical representation.
In today’s blog, we will discuss exploratory data analysis, its purpose and its tools.
What is Exploratory data analysis?
According to John Tukey, the person who first coined the term exploratory data analysis in the 1970s), it’s the procedures and technique used for analysing data and interpreting the results.
Researchers and data analysts use EDA to analyse and summarise the content of a dataset, typically with a specific question in mind or to prepare for more advanced statistical modelling in future stages of data analysis. The use of EDA involves planning, tools, and statistics you can use to extract insights from raw data. It relies on visualisations that enable researchers to identify and define patterns and characteristics in the dataset that they otherwise would not have known to look for.
What is the purpose of using exploratory data analysis in a research report?
While in the process of doing exploratory data analysis, researchers start to understand the data they have access to, this helps them figure out what questions to ask, how to frame these questions and also how to approach and with what questions to survey respondents so that they can uncover insight they feel are missing. In order to highlight the narrative the data is presenting, EDA involves examining patterns, trends, outliers, and unexpected results in the existing survey data.
The main purpose of Exploratory Data Analysis data is to examine a dataset without making any assumptions about what it might contain. By doing this, researchers and data analysts can recognize the pattern and potential causes for the observed behaviour. This ultimately helps identify which statistical model would be best to use in later stages of data analysis.
Researchers that conduct exploratory data analysis are able to:
- Identify mistakes that have been made during data collection, and areas where data might be missing.
- Map out the underlying structure of the data.
- Identify the most influential variables in the dataset.
- List and highlight anomalies and outliers.
- Test previously proposed hypotheses.
- Establish a parsimonious model.
- Estimate parameters, determine confidence intervals, and define margins of error.
Technical and Business Assumptions
The assumptions that researchers and data analysts make about raw data can be described as:
- Technical Assumptions
- Business Assumptions
Technical assumptions include the assumption that no data is missing or corrupted in any way. Business assumptions often go unrecognised and can influence the problem at hand without the researcher being aware.
To validate and confirm how accurate both technical and business assumptions are, the researcher must drill systematically into each data field, and do a thorough examination of its interaction with other variables.
By creating data visualisations, researchers are able to leverage the human mind’s natural skill of pattern recognition for the analysis of large amounts of data. Data analysts use pattern recognition to identify potential causes of a particular behaviour, highlight problematic data points, and form hypotheses that they can test.
R for Exploratory Data Analysis
R is a statistical programming package which can be used for exploratory data analysis. It is a versatile, powerful software that is also open-source, which means it is free to use!
Once the data is thoroughly executed by exploratory data analysis, R allows researchers and analysts to perform several statistical functions, which include:
- Cluster Analysis
- Summary statistics and univariate visualisations for each field in the original dataset
- Bivariate visualisation and summary statistics enable researchers to examine and assess the relationship between each of the variables in the dataset and a specific variable of interest.
- Researchers can uncover insight into the interactions between different fields in the dataset with the help of Multivariate visualisations
- L-mean clustering
- Predictive models, such as linear regression
By carrying out these statistical functions allows researchers and data analysts to validate assumptions that were established previously and it highlights patterns that can help the researcher understand the problem better and select a predictive model accordingly.
By doing this, they make sure that the data analysis was of high quality and that the collected data was organised in the way that was expected.
Research Report Structure
Without knowing how to write a properly formatted research report, learning to do exploratory data analysis holds no meaning. Hence, here we will briefly describe the structure of a report.
A well-written report on research processes, data, and findings is known as a research report. It is a significant document that provides a first-hand account of the research process and is typically regarded as a reliable source of information.
A report is usually broken down into multiple sections, which allows for a concise presentation of information.
| Broad Division | Section | Contents |
| Preliminary Page |
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| Body of Report |
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| Supplementary Pages |
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We hope this helps clear up everything about exploratory data analysis and its purpose in research. If you are still confused about EDA or just about how to write a report then hire one of our experts to make the whole process for you easier.
