The amount of data generated in today’s world is significantly high. Traditional data analysis tools such as Excel cannot analyze present data and produce meaningful insights due to its variety, volume, and velocity. As a result, advanced data analytics tools and techniques have emerged to help generate insights from large datasets. One of those techniques used is exploratory data analysis (EDA).
But what is EDA, and why is it important? What are some EDA techniques used to extract insights from data? And what tools are used in exploratory data analysis? Let’s find out.
What is Exploratory Data Analysis?
EDA is a data analytics approach used to analyze large amounts of data to extract meaningful insights. Initially developed by John Tukey in the 1970s, EDA is used in various advanced technologies, including predictive analytics and machine learning. Data scientists implement EDA techniques and tools to investigate, summarize, and analyze the main features of datasets, often using data visualization methods.
EDA techniques facilitate effective manipulation of information sources, allowing data scientists to discover the answers they need by identifying anomalies, data patterns, testing hypotheses, or assessing assumptions. Data engineers and scientists use EDA to determine what datasets can uncover beyond formal data modeling and hypothesis testing.
Why is EDA Important?
Exploratory data analysis is heavily used in the data science field. It helps data scientists assess data before making assumptions. EDA helps you detect obvious errors and better understand data patterns. Also, it enables you to find interesting relationships between variables.
Using EDA ensures the outcomes of the analysis are applicable and valid. Therefore, it helps businesses make data-informed decisions, driving efficiency and enabling them to achieve their goals.
Also, EDA helps stakeholders confirm they’re asking the correct questions. This data analytics services approach answers questions about categorical variables, confidence intervals, and standard deviation. With that information, stakeholders can determine the correctness of their questions or assumptions.
Upon completing and extracting insights from EDA, data scientists can use its features to perform more complex data analysis. For instance, they can use it to build advanced models, such as machine learning algorithms.
4 Techniques Used in Exploratory Data Analysis
Here are some exploratory data analysis techniques used to extract insights from data:
- Univariate Non-Graphical
Univariate non-graphical is the simplest technique used for exploratory data analysis. In this case, data has only one variable. Therefore, data scientists do not have to deal with relationships that often make EDA complex and challenging. The primary goal of this EDA technique is to describe the data and discover patterns within it.
Data specialists use univariate non-graphical techniques to assess several parameters, including the following:
- Range. This is the difference between the minimum and maximum values in the data. It indicates how much the data deviates from the central value on both the lower and higher sides.
- Standard deviation and variance. While the variance is the measure of dispersion that shows the spread of all data points in your dataset, the standard deviation is its square root. A higher standard deviation indicates that your data is farther spread from the mean.
- Central tendency. It refers to the values located in your data’s middle zone. Generally, central tendency is measured based on three parameters; median, mode, and mean.
- Univariate Graphical
Although its simple, univariate non-graphical technique does not present a comprehensive view of data. And that’s where the univariate graphical technique comes into play. Data scientists implement graphical approaches to gain a more detailed picture of the data. Several types of univariate graphics are used, including the following:
- Box plots. These plots graphically represent the five-number summary of maximum, minimum, first quartile, median, and third quartile.
- Histograms. A histogram is a bar plot whereby each bar depicts the proportion (total count) or frequency (count) of cases in a range of values.
- Stem-and-leaf plots. These graphical representations show all data values and the shape of the distribution.
- Multivariate Non-Graphical
Unlike univariate non-graphical, multivariate non-graphical techniques involve data with more than one variable. Generally, these techniques represent the relationship between multiple data variables through statistics or cross-tabulation.
- Multivariate Graphical
Multivariate graphical techniques use graphics to show relationships between several datasets. Some widely used multivariate graphics include the following:
- Bar plot/charts. Each group represents a single level of one variable. On the other hand, each bar within a group shows the level of the other data variable.
- Heat maps. This technique graphically represents data where variables are portrayed by color.
- Multivariate charts, which graphically represent the relationships between a factor and response.
- Bubble charts. These data visualization tools display multiple bubbles (circles) in a 2-D plot.
- Scatter plots. As the name suggests, this multivariate graphical technique involves plotting data points on a horizontal and vertical axis to represent how much a variable is affected by another.
- Run charts. Run charts represent a line graph of data mapped over time.
Tools Used in Exploratory Data Analysis
Several data science tools are used in creating exploratory data analysis models. They include:
Python
Python is an interpreted, object-oriented programming (OOP) language featuring dynamic semantics. Data scientists use this language due to its high-level, pre-built data structures, facilitating rapid application development.
Python and exploratory data analysis can be used together to detect missing values in large data sets. This enables you to determine how to manage missing values for AI and machine learning.
R
R is an open-source programming language that provides a free software environment for graphics and statistical computing, all facilitated by the R Foundation of Statistical Computing. It helps in data analysis and building statistical observations.
In addition to Python and R, data professionals use various business intelligence (BI) tools, such as Tableau, IBM Cognos, and Qlik Sense. These tools incorporate interactive dashboards and visualization features, providing a comprehensive data view.
Final Thoughts
Exploratory data analysis is an efficient approach that helps extract valuable insights from data. Various techniques, including univariate non-graphical/graphical and multivariate graphical/non-graphical, are used in EDA. These techniques help data scientists gain a comprehensive view of data. This enables them to identify patterns and relationships in large data sets. Tools, like Python, R, and BI, are used in exploratory data analysis. EDA has significant potential to improve performance. Therefore, organizations should invest in EDA tools and techniques, as they help achieve business goals by providing meaningful insights that inform their decisions.
