Exploratory data analysis

Exploratory data analysis.

The term exploratory data analysis was coined by John Tukey in 1961. He championed its use in data analysis, and that is why you will rarely find a data analysis process without data exploration.  As an aspiring data analyst, this is one of the areas that you should be conversant with. Institutions offering statistics will often teach their students the various forms of data exploration and give assignments. If you have an assignment which you perceive as challenging, you should contact us. We provide exploratory data analysis assignment help to students.


Since we are talking about exploratory data analysis, it’s imperative to have a general idea about the software that you will often be using. SAS is an acronym for the statistical analysis system.  It’s a software that was released 44 years ago. Ever since then, it has risen to become one of the most preferred statistical analysis software. The software is not entirely free but offers a free version known as SAS analytics U available for learning purposes. SAS is an indispensable tool for all sorts of data analysis.

What is exploratory data analysis?

In simple terms, exploratory data analysis is about identifying the trends in the data. It involves making sense of the data and can help a data scientist in formulating the hypothesis question. The goal here is to obtain theories which will be tested by the model.

Where does it fall in the data analysis process?

The process of data analysis begins with the data collection where data is obtained in its raw form. Nothing here is done to improve the quality of the data. Rarely, is the data in its raw form ready for data analysis. It always contains some errors such as missing values which have to be corrected. The second step (data preparation) involves correcting these errors. This is the most cumbersome part of data analysis. If done right, the eventual results will be more accurate. Then the third step is exploratory data analysis. It’s done before the process of data modelling. Then we go on to data modelling, having a general clue about the data. Finally, we make conclusions.

Exploratory data analysis methods.

SAS is well equipped with a plethora of tools which you can use for exploratory data analysis. Some of these tools are:

  1. Data visualization.

This is the most commonly used in exploratory data analysis. Hardly will you find a data analysis without visualization or a statistical data analysis software without visualization. Visualizations help to identify the general trends and anomalies in the data. In fact, most managers prefer a chart as compared to a description using words. Data visualizations can further be broken down to univariate data visualizations, bivariate, and multivariate.

Univariate visualizations are usually probability distribution of each field in the data set. They’re the easiest to plot and the ones we are more familiar with. They include pie charts, bar charts, histograms, or boxplots.

Bivariate visualizations show a relationship between two variables. However, the appropriate graph is dependent on the data that you have. For instance, if you have a continuous dataset, a scatter plot will be more appropriate.

Multivariate visualizations are more advanced and help to show the interaction between different variables in the dataset.

  1. Dimensionality reduction.

Dimensionality reduction is used to minimize the number of random variables in the analysis. There are different types of dimensionality reduction which include principal component analysis, low variance filter, high variance filter, and, factor analysis.

  1. Cluster analysis

A common form of cluster analysis is the k-means clustering. Ideally, cluster analysis forms groups of datasets with a similar observation. If the point does not form a cluster, then it’s an outlier. It’s perfectly suited for detecting anomalies

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