Exploratory Data Analysis (EDA) is a critical step in the process of understanding and analyzing data. It is an iterative process that helps to uncover patterns, trends, and relationships within the data. As a business analyst, it is important to understand EDA techniques and apply them appropriately when working with various types of data sets. While you can take up a business analyst course to gain hands-on experience, it is critical that you understand the 8 main EDA techniques and how they can be applied to data sets.
#1. Univariate Analysis
Univariate analysis involves looking at one variable or feature at a time. This technique helps to identify the central tendency, dispersion, and shape of data distribution. It is useful for understanding individual variables and their relationships with other variables in the dataset.
#2. Bivariate Analysis
Bivariate analysis involves studying two features at a time by plotting them against each other on a graph. This technique helps to understand the effect that one variable has on another and can be used to identify correlations between two variables.
#3. Multivariate Analysis
Multivariate analysis takes into consideration more than two features at once. This technique can provide insights into complex trends and patterns within a dataset by allowing you to study various combinations of features simultaneously.
#4. Correlation Analysis
Correlation analysis looks at how changes in one variable affect changes in another. This technique is used to identify associations and relationships between variables, which in turn can aid in making predictions about future behavior.
#5. Regression Analysis
Regression analysis allows you to study the relationship between independent and dependent variables by fitting a line or curve that best represents the data points. It helps to understand how much of the variation in a dependent variable can be attributed to changes in an independent variable.
#6. Time Series Analysis
Time series analysis looks at how a particular variable (or feature) changes over time. This technique provides insights into trends and patterns within data sets, which can be used for forecasting future values of the same variable based on past data points.
#7. Cluster Analysis
Cluster analysis is a technique that groups data points into clusters based on their similarity. This allows you to identify similar characteristics within the dataset and can be used to make predictions about future behavior or trends.
#8. Outlier Detection
Outlier detection identifies observations in the dataset that are significantly different from the rest of the data points. This helps to uncover anomalies within the, which can provide valuable insights underlying trends and patterns.
Tips to Improve Your Exploratory Data Analysis
When performing exploratory data analysis, it is important to consider the following:
- Understand your data set before you begin any analysis
- Use graphical techniques to gain a better understanding of the data
- Make sure the results are valid and meaningful
- Utilize automated tools for faster and more efficient data analysis
- Consider different types of EDA techniques for different scenarios.
Conclusion
Exploratory data analysis is an important part of the business analytics process. It helps to uncover patterns and relationships between different features in a dataset, which can be used to make informed decisions about the data set.
As a business analyst, it is essential to understand the various EDA techniques and apply them appropriately in different scenarios. Taking up a business analyst course can also give you hands-on experience with these techniques and help you gain insights into your data sets more effectively.
