Data Mining vs. Statistics - How Are They Different?

Data Mining vs. Statistics - How Are They Different?
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Priyadharshini GS

Published on December 31, 2015


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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.

Statistics form the core portion of Data Mining. The activities of data mining cover the entire process of data analysis, and statistics help in identifying patterns that further help identify differences between random noise and significant findings. It provides 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.

Now, did that make sense?

Here are a few definitions to help you understand better.

What is Data Mining?

“Data Mining is the process of extracting previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions”, says Zekulin.

Another definition by Ferruzza says, “Data Mining is a set of methods used in the knowledge discovery process to distinguish previously unknown relationships and patterns within data.”

Data mining is thus a confluence of various other frontiers or fields like statistics, artificial intelligence, machine learning, database management, pattern recognition, and data visualization.

Data mining is the “automated extraction of hidden predictive information from databases”.

Stephane Tuffrey in his book, Data Mining and Statistics for Decision Making, says data mining is the application of the methods of statistics, data analysis, and machine learning to the exploration and analysis of large data sets, with the aim of extracting new and useful information that will benefit the owner of these data.

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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.  It is concerned with 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.

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

How similar to data mining is statistics – and how different?

Both data mining and statistics are related to learning from data. They are all about discovering and identifying structures in them, thus aimed at 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.

Popular Methods of Data Mining

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

Popular methods of data mining


Some Methods of Statistical Analysis

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

The same research paper by Zhihua Xiao of National University of Singapore explains these methods of statistical analysis in detail.

Applications of Data Mining

Data mining is essentially available as number of commercial systems. It is widely used in:
  • Financial Data Analysis
  • Retail Industry
  • Telecommunication Industry
  • Biological Data Analysis
  • Certain Scientific Applications
  • Intrusion Detection
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.

It has a bigger role to play in the retail industry as 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 behaviours; 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 contribution 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, etc. Other scientific applications such as climate and ecosystem modeling, chemical engineering, and fluid dynamics – which constantly generate large amounts of data – are also domains that benefit quite a lot from data mining.
Data mining has also found its enormous application in detecting intrusion and threats that attack network resources. It thus plays a major role in network administration. Areas in which data mining may be applied in intrusion detection are: development of data mining algorithm 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. [Source: tutorialspoint.com]

Trends in 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.
 
If you are interested in data mining and statistics, you might want to check some of our Big Data and Analytics courses.

Found the article interesting? Have more inputs? Do let us know in the comments section below. 

About the Author

Priyadharshini writes on Project Management, IT, Six Sigma, & 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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