Data Science is a new domain with many myths surrounding the Data Science learning pathway. However, it is simply an interdisciplinary domain that uses various scientific methods, statistical techniques, mathematical knowledge, AI, and Machine Learning algorithms to extract insights from noisy, raw data and apply the knowledge for strategic planning and decision-making.
As a student or working professional in Computer Science, Econometrics, Statistics, or Mathematics, you may consider learning Data Science for a lucrative career with vast career options and job roles. You may want to register for a Data Science Bootcamp, to begin with. However, an idea of the Statistical approach to analyzing data, namely Exploratory Data Analysis (EDA), will help fast-track your learning path.
What is Exploratory Data Analysis(EDA)
As the term suggests, Exploratory Data Analysis (EDA) is the process of visually exploring the given data for analysis. It is an approach to data analysis that uses summary statistics and graphical techniques to maximize insights into the data set; mine variables, detect outliers, discover the underlying structure; and build models.
The methodology involves examining the data sets and summarizing the characteristics to manipulate data sources and get answers. Exploratory Data Analysis helps Data Scientists discover patterns and trends, test hypotheses, and visualize the results. It goes beyond simple data crunching and analysis to better understand the underlying data structure, and fetch the variables and relationships, for detailed analysis and testing.
Most EDA techniques use statistical and graphical methods to analyze and display the outcomes.
The main objective of EDA is to explore the data without any predetermined hypotheses to gain new and unexpected insights. Graphical and natural pattern-recognition techniques help uncover the anatomic secrets of the data sets and the problem statement.
What is EDA in Data Science
As EDA looks at data without prior assumptions, its approach is free of bias. It helps identify errors that otherwise may go unnoticed and helps to understand the patterns within the data.
Data Scientists can ensure the outcomes they produce are valid and applicable to a business goal or challenge. EDA asks the right questions, this instilling confidence in the team members and stakeholders that the results handle standard deviations, confidence intervals, and categorical variables. Upon completion of EDA, its features are deployed for more sophisticated data analysis or modeling. With EDA forming the mainstay of Data Science methods, it has emerged as a key step in the scientific approach of data analysis and modeling.
Types of EDA in Data Science
EDA techniques can be graphical and non-graphical and univariate or multivariate, depending upon the relationships between variables in the data.
There are four primary types of EDA:
The data analyzed has a single variable with no causes or relationships, making it a simple type of data analysis. The representation is tabular and non-graphical, allowing you to find patterns and outliers.
As non-graphical summaries do not reveal the picture, graphical tools like bars, stem-and-leaf plots, box plots, and histograms are used to show how the data values shape the distribution. It effectively displays the relationships between variables.
Multivariate data analysis exists when there exists more than one variable. M non-graphical methods depict the relationship between the variables through cross-tabulation or statistical techniques such as correlation coefficient.
The EDA technique used for analyzing multiple variables is the Multivariate graphical type. Graphical methods represent the relationships between more than two sets of data. It displays relationships between many variables through methods such as the grouped bar plot or bar chart.
Commonly used types of multivariate graphics are:
Scatter plot, where data points are plotted on a horizontal and a vertical axis to show the interdependency of variables.
The Multivariate chart depicts the relationships between factors and response.
Run chart is a simple line graph of data plotted over a given time.
Bubble chart is a data visualization with displays of multiple circles (bubbles) in a two-dimensional plot.
EDA Tools in Data Science
The most commonly used software tools to perform EDA are Python and R.
Both enjoy massive community support and frequent updates on packages that can be used for EDA. Let’s look at the various graphical instruments that can be used to execute an EDA.
Some common EDA tools are:
Box plots are a graphical method of displaying variation in a set of data. They are a way to show the measures of spreads and centers of a data set, where the spread includes the interquartile range and the mean of the data set, while the center is the mean/average and median (the middle of a data set).
The histogram is the graphical representation of numerical data with bars of different heights to display splits in the data. Histograms are frequency distributions to show the frequency of occurrence of values in a data set. The tall bars depict more frequently occurring data points in the range, and the short bars indicate lesser data points. Very short bars indicate outliers in the data.
A histogram used when the data is numerical, and you want to see the shape of the distribution. It helps see how processes change between two points of time and determine whether the outputs vary.
Heatmaps depict the correlation between two variables with color variation. They are two-dimensional graphical representations of data that use color-coding to show the magnitude of a phenomenon and instant visual summaries.
Heatmaps are used to experiment and visualize complex data at a glance by observing changes in cell colors across each axis, and patterns, if any.
Data Scientists perform various statistical functions and techniques with EDA tools, such as
- Univariate visualization of each field in the raw dataset.
- Bivariate visualization and summary statistics to visualize the relationship between each variable in the dataset and the target variable.
- Multivariate visualizations for mapping the interactions between different fields in data.
- Clustering and dimension reduction for high-dimensional data with many variables.
- K-means Clustering with data points assigned into K groups based on the distance from the centroid of each data cluster.
- Predictive modeling, such as in linear regression
EDA is an integral part of the analysis of massive data sets as long as you match the data to the statistical model. It comes before any data mining, analysis, or data modeling tasks.
If you are a wannabe Data Scientist planning to upskill in Data Science, a fundamental knowledge of Exploratory Data Analysis and its implementations in analysis can be helpful. So what are you waiting for? Register for a Data Science Bootcamp and kick-start your career in Data Science!