The Difference Between Data Mining and Statistics

The Difference Between Data Mining and Statistics
Author

Priyadharshini

Last updated June 21, 2018


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Jean-Paul Benzeeri says, “Data Analysis is a tool for extracting the jewel of truth from the slurry of data. “And data mining and statistics are fields that work towards this goal. While they may overlap, they are two very different techniques that require different skills. 

Statistics form the core portion of data mining, which covers the entire process of data analysis. Statistics help in identifying patterns that further help identify differences between random noise and significant findings—providing a theory for estimating probabilities of predictions and more. Thereby, both data mining and statistics, as techniques of data-analysis, help in better decision-making.

Let’s take a look in a little more detail.

What is Data Mining?

Data scientist Usama Fayyad describes data mining as “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.”

Today’s technologies have enabled the automated extraction of hidden predictive information from databases, along with a confluence of various other frontiers or fields like statistics, artificial intelligence, machine learning, database management, pattern recognition, and data visualization. 

With data mining, an individual applies various methods of statistics, data analysis, and machine learning to explore and analyze large data sets, with the aim of extracting new and useful information that will benefit the owner of these data.

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By using data mining, an organization may discover actionable insights from their existing data. For example, by analyzing social media posts, a snack foods company may be surprised to learn that their largest market is single dads.

What is Statistics?

Statistics is a component of data mining that provides the tools and analytics techniques for dealing with large amounts of data. It is the science of learning from data and includes everything from collecting and organizing to analyzing and presenting data. Statistics focuses on probabilistic models, specifically inference, using data.

While the aims of statistics and data mining are similar, it is estimated that there are very few statisticians to deal with the demands of data analysts. The two types of statistics prevalent are descriptive and inferential. Descriptive statistics organize and summarize the data for the sample. The methodology of using these summaries to draw conclusions from entire data sets, is called inferential statistics.

Popular statistical methods

How Similar or Different are Data Mining and Statistics?

A research paper by Jerome H. Friedman of Stanford University explains the connection between Statistics and Data Mining.

Both data mining and statistics are related to learning from data. They are all about discovering and identifying structures in data, with the aim of turning data to information. And although the aims of both these techniques overlap, they have different approaches.

Statistics is only about quantifying data. While it uses tools to find relevant properties of data, it is a lot like math. It provides the tools necessary for data mining. Data mining, on the other hand, builds models to detect patterns and relationships in data, particularly from large data bases.

To demystify this further, here are some popular methods of data mining and types of statistics in data analysis.

Data Mining Applications

Data mining is essentially available as number of commercial systems. Today, data mining is widely used in nearly every industry. For example, financial data analysis is usually systematic as the data is highly reliable. Typical cases of financial data analysis include: loan payment prediction, customer credit policy analysis, classification and clustering of customers for targeted marketing, detection of money laundering, and other financial crimes.

Data mining has a bigger role to play in the retail industry, since it collects data from various sources like sales, customer purchasing history, goods transportation, consumption, and services. In the retail industry it helps in identifying customer behaviors; designing and constructing data warehouses based on the benefits of data mining; multidimensional analysis of sales, customers, products, time and region; effectiveness of sales campaigns; customer retention; product recommendation, and cross-referencing of items.

In the telecommunication industry, data mining helps identify telecommunication patterns, detect fraudulent activities, improve the quality of services and also make better use of resources.

Data mining has also made significant contributions to biological data analysis like genomics, proteomics, functional genomics, and biomedical research. It helps in analysis by semantic integration of heterogeneous, distributed genomic and proteomic databases; association and path analysis, visualization tools in genetic data analysis, and more.

It also helps in the analysis of large amounts of data from domains such as geosciences, astronomy, and more. Other scientific applications such as climate and ecosystem modeling, chemical engineering, and fluid dynamics all benefit from data mining.

Data mining has also found enormous application in detecting intrusion and threats that attack network resources and plays a major role in network administration. Areas in which data mining may be applied in intrusion detection are development of data mining algorithms for intrusion detection, association and correlation analysis, aggregation to help select and build discriminating attributes, analysis of stream data, distributed data mining, and visualization and query tools.

Trends in Data Mining

Depending on the type of data and the kind of information that you are trying to decipher, you might choose from any of these different techniques of data mining.

Some trends in the evolving concept of data mining are:

Popular methods of data mining

Some trends in the evolving concept of data mining are:

  • Application exploration
  • Scalable and interactive data mining methods
  • Visual data mining
  • New methods of mining complex types of data
  • Biological data mining
  • Data mining and software engineering
  • Web mining, real-time data mining
  • Distributed data mining
  • Real time data mining
  • Multi database data mining
  • Privacy protection and information security in data mining

Conclusion

This article is merely an overview of data mining and statistics—they are both huge subjects rich in information. Want to learn more about data mining and statistics and how they work together? Check out some of our Big Data and Analytics courses including our Data Science Masters Program, and Business Analytics Expert Training.

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About the Author

Priyadharshini is a knowledge analyst at Simplilearn, specializing in Project Management, IT, Six Sigma, and e-Learning. With a penchant for writing and a passion for professional education & development, she is adept at penning educative articles. She was previously associated with Oxford University Press and Pearson Education, India.


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