Data Mining and Data Visualization are key technologies used in Big Data Analysis to help investigate and understand information for making informed decisions. Today, these techniques are widely popular among organizations of every size, right from small to large companies. These techniques assist in uncovering underlying connections and visualizing the collected data. Various organizations worldwide employ both methods in conjunction to enhance their Data Processing and Data Comprehension capabilities.
In this guide, we share in-depth details about Data Visualization vs Data Mining: the key differences between the two to help you understand what they mean and how can they be used to visualize/uncover hidden patterns in your data sets.
One of the most important challenges in today’s time is to make sense of your data, and more so from heterogeneous sources. Both Data Visualization and Data Mining are wonderful tools to simplify exploration of data and provide sufficient understanding so you can always make the right decisions.
Table of Contents
To get the most out of this Data Visualization vs Data Mining tutorial, we recommend that you be familiar with Big Data and Big Data Analytics.
What is Data Visualization?
Data Visualization is the way of displaying data in a pictorial form. It helps transform massive and small datasets into graphics that people can easily comprehend and interpret. For example, you can convert sales numbers into a visual format like a line or bar chart to showcase sales growth.
Today, Data Visualization tools like Tableau and Power BI help build dashboards and infographics stories that assist in gaining insights into your overall business processes. Such Business Intelligence Applications make it easier to examine and learn patterns, detect anomalies, and find correlations in datasets. These concepts are essential in analyzing vast amounts of information and making data-driven choices in organizations.
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Types of Data Visualizations
In this section of Data Visualization vs Data Mining guide, we discuss some of the most commonly used Data Visualizations like Heat Map, Box Plot, Line Chart, Scatter Plot to help you visualize your data and in turn shape your analytics strategy.
Heat Map Visualization visually expresses numerical information by using colors to signify the quantity of an individual set of data. The warm-to-cool color palette has been the most widely used Heatmap Visualization, with warm colors representing high-value input and cold colors indicating low-value input.
You can investigate the distribution of data using a Box Plot Visualization. Box Plot displays the given data’s lowest, average, first quartile, highest readings, and third quartile. With Box Plots, you can also obtain the mean difference or distribution of datasets.
Scatter plots show the relationship between the different data features. For example, if you have two variables – sq. ft represents the x-axis and price represents the y-axis – a scatter plot helps you find how the price changes according to sq. ft for homes.
The progression with one or more numerical parameters is depicted using the line chart. A line chart is used to find the trends and visualize time series data.
A histogram is a graph that shows numeric values across groups, with each column representing the frequency with which integers fit within each category.
Benefits of Data Visualization
Visual pictures get processed 60,000 times quicker than words by people. As a result, viewing a chart, graph, or similar graphical presentation of the information is much more convenient for the mind to grasp than reading and comprehending texts.
Therefore, Data Visualization can greatly enhance the pace of decision-making procedures by allowing you to analyze graphical data quickly. According to the Wharton School of Business, Data Visualization techniques can reduce the business discussion time by up to 24%.
Storytelling allows building narration using visuals and figures to engage others in the decision-making process. Your shareholders will be considerably more engaged and understandable if you master storytelling. Excellent infographics or graphs usually strengthen your argument while also reinforcing your originality.
One of the most compelling features of Data Visualization is drawing connections and identifying correlations. With Data Visualization, businesses can spot patterns and monitor important KPIs so that companies can make data-driven choices.
What is Data Mining?
Data Mining is a technique that involves analyzing unseen information from different perspectives and converting raw data into ready-to-use data. Also known as Knowledge Discovery in Data (KDD), Data Mining is one of the crucial techniques for building a solid foundation for Data Analysis.
Some of the key processes involved in Data Mining are cleaning data, handling missing values, removing outliers, and more. Since data comes in different forms and has quality issues, it is strenuous to leverage raw data to gain insights. The absence of uniformity in collected information slackens the Data Analysis process since Data Scientists have to spend more time on Data Aggregation and Data Cleaning before building Machine Learning Models.
To mitigate challenges associated with raw data, organizations implement Data Mining techniques so that analysts can have access to quality data for analysis.
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Data Visualization vs Data Mining: Key Differences
In this section, we discuss Data Visualization vs Data Mining in great detail.
Today, there are several Data Visualization Tools like Tableau, Power BI, Excel, and Looker to create visual dashboards with plots and graphs. Since these tools come with drag-and-drop features, even non-programmers can quickly use these tools to import and analyze data to gain insights.
However, Data Scientists and Data Analysts often prefer to use programming languages like Python and R to create a visualization, as it provides more flexibility in the entire process. You can use ggplot2 in R, matplotlib, seaborn, and plotly which are some of the most popular Python libraries for Data Visualization.
Similarly, there are many Data Mining tools like a rapid miner, IBM SPSS, knime, Orange, SAS visual Mining, etc., to help extract, preprocess, and structure data. These tools also offer drag-and-drop features to make it comfortable for users who don’t have coding knowledge. Data Mining can also be performed using Python and R programming packages like pandas, NumPy, sklearn, dplyr, etc.
Data Visualization vs Data Mining – The Need for Programming Knowledge
Data Visualization can be carried out without any coding skills since Business Intelligence Tools like Power BI and Tableau have simplified the analysis processes. However, for critical analysis, you need to have programming skills. You can use Python or R programming to perform visualizations on notebooks or use programming languages within the Business Intelligence Tools.
For example, to perform formula-based calculations in these tools, you need to access a programming medium to create new estimations and calculations. On the other hand, Data Mining is mostly performed using programming languages like Python, R, and SQL (Structured Query Language).
Although there are several tools to simplify Data Mining, programming languages are still widely used as you are often required to handle different raw data with varying degrees of complexity. Programming knowledge is needed in both concepts to discover hidden patterns, but for visualizations, you can mostly rely on drag-and-drop features of analytics tools for building effective dashboards.
Data Visualization vs Data Mining – Applications and Use Cases
Data Visualization is crucial in Marketing Analytics because it contains numerical and categorical values that can be visualized to make informed decisions. For example, using sales data, you can conceive of the differences between sales and profits with graphs and charts.
However, Data Mining is mostly implemented to transform raw data into usable data for analysis. Data Mining does not focus on generating decision-making insights that could directly impact the end result. Instead, Data Mining is for ensuring the right data is used for decision-making through Data Visualizations or by building Machine Learning Models. In other words, Data Mining discovers the hidden parameters and gives crucial in-depth information about data.
Data Visualization vs Data Mining – Challenges
One significant challenge Data Analysts witness in Data Visualization is in selecting appropriate visual elements like graphs or charts to use for categorical data. The lack of understanding of using the right graphs or charts can mislead decision-makers.
For instance, pie charts are avoided while creating visualizations since humans face difficulty in identifying the number of slices from angles. In addition, the identification of color combinations is crucial for ensuring a better understanding of the graphs. If you do not use the right contrast, users fail to extract the depth of the insights.
Data Mining has its own challenges right from the Data Preprocessing Stage to the Data Structuring Stage. As Data Mining deals with large databases or datasets, data could be inaccurate, inconsistent, incomplete, and noisy, which needs to be rectified and processed. Besides, Data Mining has security issues, like handling private information and local databases. Often you would use several algorithms to group data based on a pattern. However, if the algorithm’s accuracy is low, you might group data together that may not be similar.
This guide presented you with an elaborate discussion on Data Visualization vs Data Mining. We addressed four aspects of comparison to help you draw out their significance and use-cases in various departments.
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