For a Data scientists and Data analysts, understanding your data before you go ahead to give it a meaning is important. Understanding the data involves a number of activities. These activities are known as Exploratory Data Analysis. This article will look at the steps involved in the EDA process.
EXPLORATORY DATA ANALYSIS
The different techniques that are involved in understanding the data requires different techniques and processes to understand. EDA involves different techniques to come up with show the distribution of data, find the statistical measures involved, give visual representation of the data and find the unwanted data within the section. The process generally makes the data meaningful to the person who is about to use it for the next step that is either for creating dashboards for data analysts, or using it for training models for data scientists and machine learning engineers.
What does EDA Involve?
Understanding the processes involved in EDA is key for any data enthusiast because it acts as a guide for the steps that should be involved. The below are some of the key techniques that are involved in the exploratory data analysis process.
- Data Distribution
This is one of the main steps involved in EDA. Understanding the distribution of Data involves a number of mathematical and statistical processes. Using statistical measures such as mean and median is important to get understanding of the columns that are numerical in nature. The statistical measures such as the standard deviation, percentiles and quartile ranges are also important in the understanding the distribution of data. Python has a wide range of libraries that are helpful in the data distribution. Machine learning and Artificial intelligence mainly relies on numeric values for the model training process. This shows the important role that understanding data distribution in EDA plays. - Graphical Representation of Data
Matplotlib and Seaborn are the most commonly used Data visualization libraries that python provides. To get a clear and detailed understanding of the data, plotting them using different types of graphs is important. This Graphical representation comes with a huge number of benefits. It gives the relationship of different variables, helps identify outliers in our data, that is the values that are out of range compare to other values within the dataset. The Heatmaps, boxplots, histograms, barcharts, scatter plots are some of the mostly used data visualization techniques in the development process. - Handling missing values
For huge datasets, the probability of having missing or null values is always high. For a perfectly analyzed data, handling such situations is important. Looking at the effect that the missing values have on the overall dataset, it is important to take the perfect steps in analyzing the data. In some of the cases where the missing values result to a small percent of the data, the rows with the missing values are always dropped. However this is not advised due to different reasons. Handling the values involves techniques such as using mean and median and mode. Through the python libraries, this process in EDA is promoted and its success ensured. - Apart from the above three techniques, the other involved techniques are the outlier detection, analysis of how the data is correlated and much more. Understanding these techniques is important before the start of any EDA process.
Importance of Exploratory Data Analysis
The below list shows some of the most important reasons why EDA is important:
1.EDA gives the individual working with the data a more detailed understanding of the data being used. It gives information about the number of feature involved, information about each feature(categorial or numerical feature), mathematical information, and their distribution. This step is important for Data scientist because it helps them come up with the perfect model that should be used in the prediction process.
- Feature selection and Engineering. EDA gives more knowledge about the data and this is important in the process of coming up with new features, based on the relationships that are found. Feature engineering is an important concept in Data science as new features makes it effective to come up with new analysis techniques. In addition, the new feature can also be used to create new dashboards in the data analysis step.
- EDA makes it possible to select the best features from a list of available features. After exploring a dataset, there are some features that might come out as not meaningful due to the nature of the project. EDA gives information about each feature through statistics and visualization and this makes it easy to come up with the right decision.
- In the data analysis process, the outliers and data outside the needed range always have a negative effect to the final outcome. Through the boxplots, the outliers are easily detected and removed to make the subsequent process in data more easy and meaningful.
Conclusion
In conclusion, Exploratory Data Analysis is one of the most important areas in the data science and data analysis process. The different areas involved is important in ensuring that those using the data gets a perfect understanding of the data and they come up with the perfect decisions based on their area of specialization.