What do you understand by EDA?
Exploratory data analysis is an approach that involves systematically exploring data to understand its features and underlying structure better. Exploratory data analysis aims to understand the dataset better rather than produce definitive results.
Exploratory data analysis typically begins with visualizing the data using histograms, scatter plots, and bar charts. These visualizations can help to identify trends and patterns in the data. Once trends and practices have been identified, more formal analysis methods can be employed to investigate the relationships between variables further.
EDA vs. CDA
There are two camps: the "exploratory" and "confirmatory". Exploratory data analysis (EDA) is making sense of data when you don't have a clear hypothesis, and Confirmatory data analysis (CDA) tests specific ideas. Each approach has advantages and disadvantages.
EDA is often used when a problem is first encountered, as it can help to identify patterns and relationships that may not be immediately obvious. EDA is also useful for dealing with multimodal data, which can be challenging to analyze using traditional statistical methods. However, EDA can be time-consuming, making it easy to draw false conclusions from complex data sets.
CDA, however, is more efficient and allows for more definitive conclusions. However, CDA requires a robust research question or hypothesis to be in place before any analysis can take place. Without a straightforward question guiding the analysis, it can easily get lost in the data.
Ultimately, both EDA and CDA have their place in data analysis.
Advantages:
One of the advantages of exploratory data analysis is that it can be used on any type of dataset, regardless of size or complexity. This makes it an ideal approach for data analysts working with a new dataset or who want to get a better understanding of a dataset before applying more formal analysis methods.
Another advantage of exploratory data analysis is that it can help to identify outliers and unusual observations in the data. These observations can then be investigated further to see if they are true anomalies or if other factors can explain them.
Exploratory data analysis is an essential step in any data analysis project, as it helps to familiarize oneself with the dataset and to identify potential areas of interest. However, it is necessary to remember that exploratory data analysis is only the first step in a data analysis project; more formal methods of analysis will need to be employed to draw definitive conclusions from the data.
Disadvantages
Some might say that data is the lifeblood of business. Without it, how can you make informed decisions about where to allocate resources or assess the effectiveness of a new marketing campaign?
Businesses drowning in a sea of information may turn to exploratory data analysis (EDA) to make sense of it all. While EDA can be a helpful tool, it also has its drawbacks. One downside is that EDA relies heavily on statistical techniques that can be complex and difficult to interpret.
This can cause us to form wrong conclusions from the data. In addition, EDA often produces large amounts of visual data that can be difficult to assess and summarize. As a result, businesses should weigh the pros and cons of using EDA before diving into their data head-first.
Some standard methods of exploratory data analysis include:
-Visualizing the data using histograms, scatter plots, and bar charts.
-Identifying trends and patterns in the data.
-Investigating relationships between variables.
-Identifying outliers and unusual observations in the data.
-Familiarize oneself with the dataset.
-Identifying potential areas of interest.
Exploratory data analysis is an essential step in any data analysis project, as it helps to familiarize oneself with the dataset and to identify potential areas of interest. However, it is necessary to remember that exploratory data analysis is only the first step in a data analysis project; more formal methods of analysis will need to be employed to draw definitive conclusions from the data.