Data Mining vs Data Exploration
There are two main methodologies or techniques used to retrieve relevant data from large, unorganized pools. They are manual and automatic methods. The manual method is another name for data exploration, while the automatic method is also known as data mining.
Data mining generally refers to gathering relevant data from large databases. On the other hand, data exploration generally refers to a data user finding their way through large amounts of data to gather necessary information. Let's study both methods in detail and compare their differences.
What is Data Exploration?
Data exploration refers to the initial step in data analysis. Data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, to understand the nature of the data better.
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 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. 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.
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 understand better and visually identify anomalies and relationships that might otherwise go undetected.
Data Exploration Tools
Manual data exploration methods entail 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 create basic charts for data exploration, view raw data, and identify the correlation between variables. To identify the correlation between two continuous variables in Excel, use the CORREL() function 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.
What can Data Exploration Do?
In general, the goals of data Exploration come into these three categories.
Use Cases of Data Exploration
Data Exploration has been widely used in multiple industries serving different purposes. Besides monitoring prices in eCommerce, data Exploration can help in individual paper research, news aggregation, marketing, real estate, travel and tourism, consulting, finance, and many more.
What is Data Mining?
Data mining could be called a subset of Data Analysis. It explores and analyzes huge knowledge to find important patterns and rules.
Data mining could also be a systematic and successive method of identifying and discovering hidden patterns and data throughout a big dataset. Moreover, it is used to build machine learning models that are further used in artificial intelligence.
What Can Data Mining Do?
Data mining tools can sweep through the databases and identify hidden patterns efficiently by automating the mining process. For businesses, data mining is often used to discover patterns and relationships in data to help make optimal business decisions.
Use Cases of Data Mining
After data mining became widespread in the 1990s, companies in various industries - including retail, finance, healthcare, transportation, telecommunication, E-commerce, etc., started to use data mining techniques to generate insights from data. Data mining can help segment customers, detect fraud, forecast sales, etc. Specific uses of data mining include:
Difference between Data Exploration and Data Mining
There are two primary methods for extracting data from disparate sources in data science: data exploration and data mining. Data Exploration can be part of data mining, where the aim is to collect and integrate data from different sources. As a relatively complex process, data mining comes as discovering patterns to make sense of data and predict the future. Both require different skill sets and expertise, yet the increasing popularity of non-coding data Exploration tools and data mining tools greatly enhances productivity and makes people's lives much easier.