There are different mechanisms that professionals dealing with data use to visualize and analyze data. Exploratory data analysis, also known as EDA, is one of the top ways data enthusiasts analyse and evaluate different types of data sets.
Data professionals use this mechanism to comprehend data sets before modelling them into the desired formats to be used in different aspects. It is crucial to understand how exploratory data analysis operates if you are operating in a career dealing with data mining and analysis.
What is Exploratory Data Analysis?
It is evident that many people are not familiar with exploratory data analysis. What does this mean? It is a mechanism that data professionals use to analyze and dig deep through different data sets before they begin using it in different aspects.
The main agenda of conducting this kind of data analysis is to identify the major characteristics of the data sets. By conducting EDA, data analysts get a chance to analyze and make predictions and assumptions depending on the context of the data.
The exploratory data analysis process involves different aspects such as data visualization, which incorporates things such as creating graphs, box plots, charts and many more. The process of EDA involves a series of terminologies that you need to understand.
This involves terminologies such as outlier referring to the data value that is mainly different, including the lower and the higher dataset. Distribution refers to how the dataset is spread out. The distribution can be identified by observing the shape of the graph.
Data model refers to how data is organized and the general relationship between the values across the data sets. Variable is another popular in this area which refers to the characteristics that you can measure things such as income or weight.
Types of EDA
Exploratory data analysis come in different types that data analysts use in their daily operations. They are classified either as graphical or quantitative. The graphical methods involve the process of summarizing the data in a diagrammatic manner.
On the flip side, the quantitative method involves the process of calculating summary statistics. The results are then used to generate better insights for use. The two methods mentioned above are then divided further into multivariate and univariate methods.
The univariate method works in consideration of a single variable that is also known as a data column. The multivariate works consider two or even more variables simultaneously to explore the relationship between datasets.
The most exciting thing is that the above exploratory data analysis types are then divided further into different categories that data professionals use. You have to keep in mind that every type of EDA is used in a different instance.
Benefits of Conducting Exploratory Data Analysis
Exploratory data analysis offers value to small business owners by helping data professionals understand whether the data results that have been collected have been interpreted correctly or not. Also, they get it easier to align the data within the business context.
Apart from generating excellent data technical results, EDA also helps company stakeholders confirm if the questions they are asking are beneficial to business success. On most occasions, EDA often turns up with unpredictable results.
The data professionals never mind investigating the data further since the results generated are usually highly informative and do not require further interpretation. You can easily integrate exploratory data analysis into your business intelligence software to get more insights.
EDA plays a crucial role in ensuring that the business management team knows everything regarding the data before making any assumption that is likely to impact the normal running of the company.
Performing exploratory data analysis gives companies the confidence to believe in their data and make valuable decisions to contribute to the organization’s success. –It also contributes to deploying powerful machine learning algorithms into the business operation.
How to Conduct Exploratory Data Analysis
Most business operators get it challenging to carry out exploratory data analysis. However, the process is much simpler when you opt to break down the process into different steps. Check out the steps discussed below when conducting an exploratory data analysis!
Check for Missing Data
Exploratory data analysis is all about dealing with datasets. The first step is to begin by looking at the data itself. Evaluate the data you have at hand and identify any missing values that you need to use to complete the entire work.
To ensure that you have analyzed the data perfectly, check and analyze every feature one at a time. This is a fantastic method, especially if you are dealing with large data sets. Once you identify specific missing values, you need to ask yourself why the value is missing.
Even though this process might sound simple, it is a complex procedure that requires a keen eye for details to answer the many available questions. Note that sometimes, the answer to why some of the values are missing may never be found.
By evaluating data considering aspects such as seasonality, you can get the trend used in the data and fix the gaps. If the time series you get does not have a definite trend, you can opt to utilize linear interpolation to fill in the missing values.
Present the Basic Description of Your Sample and Features
This is the second step after identifying the missing values in your data. However, after identifying any missing aspects in the datasets, you still do have much information about the data. When filling in the spaces, there are various features that you need to consider.
According to Decofacts, Continuous is one of the features that can help you assume an infinite value within the given range. Discrete helps you assume a countable number of values that are always numeric in nature.
The categorical feature also aids in assuming the missing number depending on the category of the given values. If the values are in discrete form, you also need to fill in a discrete figure to complete the values.
Identify the Shape of Your Dataset
Once you have filled in the missing values, the next important step is to identify the shape of your dataset. Researchers have revealed that this is the most critical step to never miss the point during analysis.
You need to gather the relevant information about the entire dataset at this stage. By doing a detailed analysis and evaluating all the points deeply, you will understand the data details that will help you determine the actual shape.
Remember that the shape of your dataset is mainly meant to showcase the distribution of your data depending on the kind of business you are running. At this point, you can also note other data features such as the skewness and gaps.
These features open a room that enables you to learn more about the data at hand. You can identify the available trends across the dataset, which is vital in understanding the context in which the data was generated.
Identify the Relationship within the Datasets
As you get a chance to understand your dataset deeply, you also learn the relationship between the points. Remember that the relationship between different values is picked randomly depending on the kind of data you have.
Try and identify the correlations between different values by analyzing every data aspect independently. You need to analyze every data point as an independent entity and relate it with other values across the entire set.
As you continue doing analysis, have a notebook that will help you take notes about the data. This will make the entire work easier since you will easily remember to identify the correlations between the data values.
After identifying the correlations, go ahead and think about why specific values are related to each other. The reasons you get will help you identify how the relationship came to life, and you will easily find more data points that are related to each other.
Locate the Outliers in the Dataset
Identifying the outliers in the dataset is another crucial step that requires a keen eye for details to locate the perfect values. Remember that the outliers are the values in the dataset that are mainly different from the rest of the data points.
In addition, the outliers can be much higher or lower compared to the other values across the dataset. It is very critical to identify the outliers within the values because they can skew the mean mode, or the median of the values.
Once the outlier has skewed the dataset, it affects the visual presentation of the entire set of data. To note the outliers easily, observe the curves on your graph. Also, you can opt to sort the data in a numerical format during the process of exploratory data analysis.
Exploratory data analysis has a major impact on the smooth running of any business or organization. The challenging part is how to execute the procedure to generate better results from the process. Below are the major steps when carrying out exploratory data analysis within your business setting.