Data Exploration - A Complete Introduction

What is Data Exploration?

Data exploration definition:
Data exploration refers to the initial step in data analysis in which data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, in order to better understand the nature of the data.

Data exploration techniques include both manual analysis and automated data exploration software solutions that visually explore and identify relationships between different data variables, the structure of the dataset, the presence of outliers, and the distribution of data values in order to reveal patterns and points of interest, enabling data analysts to gain greater insight into the raw data.

Data is often gathered in large, unstructured volumes from various sources and data analysts must first understand and develop a comprehensive view of the data before extracting relevant data for further analysis, such as univariate, bivariate, multivariate, and principal components analysis.

Data Exploration Tools

Manual data exploration methods entail either writing scripts to analyze raw data or manually filtering data into spreadsheets. Automated data exploration tools, such as data visualization software, help data scientists easily monitor data sources and perform big data exploration on otherwise overwhelmingly large datasets. Graphical displays of data, such as bar charts and scatter plots, are valuable tools in visual data exploration.

A popular tool for manual data exploration is Microsoft Excel spreadsheets, which can be used to create basic charts for data exploration, to view raw data, and to identify the correlation between variables. To identify the correlation between two continuous variables in Excel, use the function CORREL() to return the correlation. To identify the correlation between two categorical variables in Excel, the two-way table method, the stacked column chart method, and the chi-square test are effective.

There is a wide variety of proprietary automated data exploration solutions, including business intelligence tools, data visualization software, data preparation software vendors, and data exploration platforms. There are also open source data exploration tools that include regression capabilities and visualization features, which can help businesses integrate diverse data sources to enable faster data exploration. Most data analytics software includes data visualization tools.

Image shows OmniSci's data exploration platform performing visualizations of galactic positions.

Why is Data Exploration Important?

Humans process visual data better than numerical data, therefore it is extremely challenging for data scientists and data analysts to assign meaning to thousands of rows and columns of data points and communicate that meaning without any visual components.

Data visualization in data exploration leverages familiar visual cues such as shapes, dimensions, colors, lines, points, and angles so that data analysts can effectively visualize and define the metadata, and then perform data cleansing. Performing the initial step of data exploration enables data analysts to better understand and visually identify anomalies and relationships that might otherwise go undetected.

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What is Exploratory Data Analysis?

Exploratory Data Analysis (EDA), similar to data exploration, is a statistical technique to analyze data sets for their broad characteristics. Visualization tools for exploratory data analysis such as HEAVY.AI's Immerse platform enable interactivity with raw data sets, giving analysts increased visibility into the patterns and relationships within the data.

Data Exploration in GIS

GIS (Geographic Information Systems) is a framework for gathering and analyzing data connected to geographic locations and their relation to human or natural activity on Earth. With so much of the world's data now being location-enriched, geospatial analysts are faced with a rapidly increasing volume of geospatial data.

Advanced GIS software solutions and tools can facilitate the incorporation of spatio-temporal analysis into existing big data analytics workflows, enabling data analysts to easily create and share intuitive data visualizations that will aid in spatial data exploration. The ability to characterize and narrow down raw data is an essential step for spatial data analysts who may be faced with millions of polygons and billions of mapped points. For example, learn about the ways GIS technologies are improving disaster response operations.

Data exploration in GIS example

Data Exploration in Machine Learning

A Machine Learning project is as good as the foundation of data on which it is built. In order to perform well, machine learning data exploration models must ingest large quantities of data, and model accuracy will suffer if that data is not thoroughly explored first. Data exploration steps to follow before building a machine learning model include: 

  • Variable identification: define each variable and its role in the dataset 
  • Univariate analysis: for continuous variables, build box plots or histograms for each variable independently; for categorical variables, build bar charts to show the frequencies
  • Bi-variable analysis - determine the interaction between variables by building visualization tools
  • ~Continuous and Continuous: scatter plots
  • ~Categorical and Categorical: stacked column chart
  • ~Categorical and Continuous: boxplots combined with swarmplots
  • Detect and treat missing values
  • Detect and treat outliers

The ultimate goal of data exploration machine learning is to provide data insights that will inspire subsequent feature engineering and the model-building process. Feature engineering facilitates the machine learning process and increases the predictive power of machine learning algorithms by creating features from raw data.

Interactive Data Exploration

Advanced visualization techniques are employed throughout a variety of disciplines to empower users to visualize patterns and gain insight from complex data flows, and make subsequent data-driven decisions. Industries from engineering to medicine to education are learning how to do data exploration.

In big data exploration tools, interactivity is an important component in the perception of data exploration visual technologies and the dissemination of insights. The manner in which users perceive and interact with visualizations can heavily influence their understanding of the data as well as the value they place on the visualization system in general.

Interactive data exploration emphasizes the importance of collaborative work and facilitates human interaction with the integration of advanced interaction and visualization technologies. Accelerated multimodal interaction platforms equipped with graphical user interfaces that prioritize human-to-human properties facilitate big data exploration through visual analytics, accelerate the sharing of opinions, remove the data bottleneck of individual analysis, and reduce discovery time.

Image gives insight to OmniSci's interactive data visualizaiton platform, analyzing USA mobile coverage quality.

What is the Best Language for Data Exploration?

The most popular programming tools for data science are currently R and Python, both highly flexible, open source data analytics languages. R is generally best suited for statistical learning as it was built as a statistical language. Python is generally considered the best choice for machine learning with its flexibility for production. The best language for data exploration depends entirely on the application at hand and available tools and technologies.

Data Exploration in Python

Data exploration with python has the advantage in ease of learning, production readiness, integration with common tools, an abundant library, and support from a huge community. Nearly every tool kit and functionality is packaged and can be executed by simply calling the name of a method.

Python data exploration is made easier with Pandas, the open source Python data analysis library that can single-handedly profile any dataframe and generate a complete HTML report on the dataset. Once Pandas is imported, it allows users to import files in a variety of formats, the most popular format being CSV. The pandas data exploration library provides:

  • Efficient dataframe object for data manipulation with integrated indexing
  • Tools for reading and writing data between disparate formats
  • Integrated handling of missing data and intelligent data alignment 
  • Flexible pivoting and reshaping of datasets
  • Time series-functionality
  • Intelligent label-based slicing, fancy indexing, and subsetting of large datasets
  • Columns can be inserted and deleted from data structures for size mutability
  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on datasets
  • High performance merging and joining of datasets
  • Hierarchical axis indexing

Techniques for how to improve data exploration using Pandas are discussed at length in expansive Python community forums.

Data Exploration in R

The data exploration and visualization with R process looks like:

  • Loading the data: Due to the availability of predefined libraries and simple syntax, loading data from a variety of formats, such as .XLS, TXT, CSV, and JSON, is very straightforward
  • Converting variables: The process of converting a variable into a different data type in R entails adding a character string to a numeric vector, converting all the elements in the vector to the character
  • Transpose a dataset: R provides code to transpose a dataset from a wide structure to a much narrower structure
  • Sorting of dataframe: accomplished by using order as an index
  • Create plots or histograms
  • Generate frequency tables to best understand the distribution across categories
  • Generate a sample set with just a few random indices
  • Remove duplicate values of a variable
  • Find class-level count average and sum: R data exploration techniques include apply functions to accomplish this
  • Recognize and treat missing values and outliers by inputting with the mean of other numbers
  • Merge and join datasets: R includes an appending datasets function and a bind function

What is the Relationship Between Data Exploration and Data Mining?

There are two primary methods for retrieving relevant data from large, unorganized pools: data exploration, which is the manual method, and data mining, which is the automatic method. Data mining, a field of study within machine learning, refers to the process of extracting patterns from data with the application of algorithms. Data exploration and visualization provide guidance in applying the most effective further statistical and data mining treatment to the data.

Once the relationships between the different variables have been revealed, analysts can proceed with the data mining process by building and deploying data models equipped with the new insights gained. Data exploration and data mining are sometimes used interchangeably.

Four stages of Data Mining for Data Exploration
Image from TechTarget

Data Discovery vs Data Exploration

Once data exploration has refined the data, data discovery can begin. Data discovery is the business-user-oriented process for exploring data and answering highly specific business questions. This iterative process seeks out patterns and looks at clusters, sequences of events, specific trends, and time-series analysis, and plays an integral part in business intelligence systems, providing visual navigation of data and facilitating the consolidation of all business information.

Most popular data discovery tools provide data exploration and preparation and modeling capabilities, support visual and digestible data representations, allow interactive navigation and sharing options, support access to data sources, and offer seamless integration of data preparation, analysis, and analytics. Learn how HEAVY.AI's converged analytics platform integrates these capabilities to derive insights from your largest datasets at the speed of curiosity.

Data Examination vs Data Exploration

Data examination and data exploration are effectively the same process. Data examination assesses the internal consistency of the data as a whole for the purpose of confirming the quality of the data for subsequent analysis. Internal consistency reliability is an assessment based on the correlations between different items on the same test. This assessment gauges the reliability of a test or survey that is designed to measure the same construct for different items.

Data Exploration Resources

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