Exploratory data analysis: Impact on Data Science

American mathematician John Tukey Originally developed exploratory Data Analysis (EDA) in the 1970s. Still today, EDA techniques continue to be a widely used method in the data discovery process. Beyond formal modeling or hypothesis testing, EDA opens a wide door for a better understanding of Data set variables and their relationships. It also helps to determine if Statistical Technique that has been considered for the data analysis is appropriate or not.

What is exploratory data analysis?

Exploratory Data Analysis (EDA) is widely used by Data Scientists while analyzing and investigating Data sets, summarizing the main characteristics of data to the visualizing method. It helps the Data Scientist to discover Data Patterns, Spot anomalies, hypothesis testing, and or assumption.

So in a simple way, it can be defined as a method that helps the Data Scientist determine the best ways to manipulate the given data source to get the answer that is needed as a goal.

How important Exploratory Data Analysis is Data Science

The primary purpose of EDA is to help deep look at the data set before making any assumptions, identifying obvious errors, gain a better understanding of the patterns within the dataset, figure out outliers and/or anomalous events, and last but not least, to find out the exciting relationships among the variables.

Exploratory Data Analysis is extremely important to Data Analysis in the Data Science arena. First, EDA is used to ensure the results the Data scientists are producing are valid and applicable to any desired goals. Second, EDA helps the stakeholders to ensure that they are always asking the right questions. It also helps answer the questions about standard deviations, categorical variables, and confidence intervals. Finally, once EDA is complete and insights are drawn, its features can then be used for more sophisticated data analysis or modeling, including machine learning.

Exploratory data analysis Types

Well there are primarily four types of EDA:

  • Univariate non-graphical:

Univariate Non Graphical is the most simplest form of data analysis. here it consists of just one variable. Being a single variable, it doesn’t deal with causes or relationships. Instead, the primary purpose of the univariate thematic analysis is to describe the data and find patterns within it.

  • Univariate graphical

Non-graphical methods cannot provide a complete picture of the data. Graphical methods are therefore required here. The Common types of univariate graphics are:

  1. Stem-and-leaf plots: These shows all data values and the shape of the distribution.
  2. Histograms a bar plot: in which each bar represents the frequency (count) or proportion (count/total count) of cases for a range of values.
  3. Box plots: graphically depict the five-number summary of minimum, first quartile, median, third quartile, and maximum.
  • Multivariate non-graphical

Multivariate data arises from more than one variable. Generally, Multivariate non-graphical EDA techniques show the relationship between two or more data variables through cross-tabulation or statistics.

  • Multivariate graphical

Multivariate data uses graphics while displaying relationships between two or more Dataset. The Most used graphic is a grouped bar plot or bar chart with each group representing one level of one of the variables and each bar within a group representing the levels of the other variable.

The Other common types of multivariate graphics include:

  • Scatter plot: Is used to plot data points on a horizontal and a vertical axis to show how much one variable is affected by another.
  • Multivariate chart: Is a graphical representation of the relationships between factors and a response.
  • Run chart: Is a line graph of data plotted over time.
  • Bubble chart: Is a data visualization that displays multiple circles (bubbles) in a two-dimensional plot.
  • Heat map: Is a graphical representation of data where values are depicted by color.

Exploratory data analysis Tools

There are many tools available for exploratory data analysis. Some of the most popular ones are R, Python, and SAS. However, each has its strengths and weaknesses, so choosing the right tool for the job is essential.

R is an excellent tool for visualizing data. It has a wide variety of plots and charts that can be used to explore data. It also has a lot of statistical functions that can be used to perform more advanced analyses.

Python is another great tool for EDA. It has many of the same features as R, but it’s also more user-friendly. As a result, Python is an excellent choice for beginners who want to get started with data analysis.

SAS is a powerful statistical software package that can be used for EDA. SAS is more expensive than R and Python, but it’s worth the investment if you need to perform more complex calculations.

QuestionPro and exploratory data analysis

You can always have your data from a different data source, and QuestionPro can definitely help you gather the survey data from multiple channels. But what happens when you want to go beyond the data that’s already been collected? That’s where exploratory data analysis comes in.

QuestionPro’s built-in analysis tools make it easy to get started with EDA. You can quickly see summary statistics for your data, create interactive visualizations, and more. And because QuestionPro integrates with R, you can use all the powerful statistical tools R offers.

So if you’re ready to take your data analysis to the next level, QuestionPro is one of the perfect tools.

Conclusion

Finally, we can say that exploratory data analysis is a proven methodology that can help Data Scientists to make sense of complex datasets. By using visualizations and other methods, you can uncover patterns and relationships that you might not have found otherwise.

Therefore, EDA is an essential part of any data analysis, and we hope that this article has given you a great introduction to the topic.

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Authors: Md Assalatuzzaman & Mizanul Islam