Exploratory Data Analysis is the process of evaluating, analyzing, and summarizing a dataset in order to get a better understanding of its structures, patterns, point anomalies, experiment with hypotheses or examine inferences. It is a critical step in the data analysis pipeline, as it helps to identify potential issues with the data and inform subsequent data cleaning and modeling efforts. Data scientists employ exploratory data analysis to ensure that the outcomes they produce are accurate and acceptable to any desired business objectives and findings.
There are several steps that are involved in EDA and before performing the analysis, it is important that all the steps are followed to ensure that we come up with accurate results. Here are some of the steps involved in exploratory data analysis.
It is very important to understand the kind of data that you are working with before starting EDA. There are tools that can be used to understand your data. some of these tools include but are not limited to, Python programming language, R, and others. python, in particular, has in-built data structures that make it easy to perform exploratory data analysis. it has libraries that easily help achieve this.
understanding the data involves knowing the data's source, its format, and the relevant variables and features.
The first step involved in exploratory data analysis is data cleaning. this is the procedure of identifying and handling missing values, dealing with outliers and anomalies, and removing duplicate or irrelevant data. This step is very important as it ensures that you are working with clean data and it helps during the final stages of EDA that is the modeling and the visualization. Python has libraries that help in this process. the library is called pandas.
The next step is to explore each variable in the data set, this process is known as Univariate Analysis:
The univariate analysis involves exploring each variable or feature in the dataset individually. This step is typically the starting point of EDA, as it provides a basic understanding of each variable's distribution, central tendency, and variability.
Distribution: One of the key aspects of univariate analysis is visualizing the distribution of data, which can be done using a histogram, kernel density plot, or box plot. This helps to identify any skewness or outliers in the data.
b. Central tendency: Another important aspect of the univariate analysis is identifying the central tendency of the data. This includes measures like mean, median, and mode, which provide an indication of where the data is centered.
c. Variability: Finally, univariate analysis involves exploring the variability of the data. This includes measures like range, variance, and standard deviation, which provide an indication of how spread out the data is.
After exploring each variable, it is now time to explore the correlation between two variables. This now leads us to the next step which is known as Bivariate Analysis:
The bivariate analysis involves exploring the relationship between two variables. This step helps to identify any patterns or trends in the data, as well as any potential outliers.
Correlation: One of the primary techniques used in bivariate analysis is a correlation, which measures the strength and direction of the linear relationship between two variables. A scatter plot is a common visualization tool used to display the relationship between two variables.
b. Patterns and trends: Bivariate analysis can also help to identify any patterns or trends in the data. This includes identifying any nonlinear relationships between the two variables or any groups or clusters within the data.
c. Outliers: Finally, bivariate analysis can help to identify any potential outliers or anomalies in the data. This includes identifying any points that fall far outside the normal range of values for the two variables.
Afterward, we explore the relationship between more than two variables, through the process of Multivariate Analysis:
Multivariate analysis involves exploring the relationship between more than two variables. This step helps to identify complex patterns and relationships within the data.
a. Correlation matrix: One of the primary techniques used in multivariate analysis is the correlation matrix, which displays the pairwise correlation between all variables in the dataset. This can help to identify any strong correlations between variables and any potential issues with multicollinearity.
b. Visualization: Multivariate analysis also involves visualizing the relationship between multiple variables. This includes techniques like scatterplot matrices, which display the relationship between multiple variables in a single plot.
c. Outliers and Clusters: Finally, multivariate analysis can help to identify any potential outliers or clusters within the data. This includes techniques like cluster analysis, which groups similar observations together, and outlier detection, which identifies any observations that fall far outside the normal range of values for multiple variables.
after exploring the relationship between different variables and doing the necessary cleaning, we can now get into the next step which is data visualization. Data visualization is a critical aspect of EDA. It involves creating visual representations of data using graphs, charts, and other visual tools. This can help to identify patterns and trends that may not be apparent from looking at the data alone.
Exploratory data analysis, therefore, involves summarizing the results of the analysis and drawing conclusions. This includes identifying any trends or patterns in the data, identifying any potential issues or outliers, and making recommendations for further analysis or modeling.
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