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Differences between data mining and statistics

Jean-Paul Benzeri says, “Data analysis is a tool for extracting true gems from a slurry of data.” And data mining and statistics are areas that work towards this goal. They may overlap, but 2 Two very different techniques that require different skills.

Statistics form the core of data mining that covers the entire process of data analysis. Statistics help identify patterns that are even more helpful in identifying differences between random noise and important findings, and provide theories for estimating the probability of prediction. This helps both data mining and statistics to make better decisions as a method of data analysis.

Let’s take a closer look.

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What is data mining?

Data Scientist Usama Fired Describes data mining as “an important process for identifying useful, new, potentially useful, and ultimately understandable patterns of data.”

Today’s technology enables the automatic extraction of hidden predictive information from databases, along with the confluence of various other frontiers or fields such as statistics, artificial intelligence, machine learning, database management, pattern recognition, and data visualization.

In data mining, individuals apply different methods of statistics, data analysis, and machine learning to explore and analyze large datasets and extract new and useful information that benefits the owners of these data. To do.

Data mining allows organizations to discover actionable insights from existing data. For example, a snack food company may analyze social media posts and be surprised that the largest market is a single father.

What is statistics?

Statistics are a component of data mining that provides tools and analytical methods for processing large amounts of data. It’s a science that learns from data and includes everything from collecting and organizing data to analyzing and presenting it. Statistics focus on probabilistic models, especially inference using data.

Although statistics and data mining have similar purposes, it is estimated that few statisticians can meet the demands of data analysts. There are two types of widely used statistics: Descriptive and inferenceDescriptive statistics organize and summarize sample data. The method of drawing conclusions from the entire dataset using these summaries is called inference statistics.

General statistical methods

How similar are data mining and statistics?

Ah Research paper by Jerome H. Friedman A professor at Stanford University explains the relationship between statistics and data mining.

Both data mining and statistics are related to learning from data. They are all about discovering and identifying the structures in the data, with the goal of transforming the data into information. The goals of these techniques overlap, but the approaches are different.

Statistics are only intended to quantify the data. Use the tools to find the relevant properties of the data, much like math. Provides the tools you need for data mining. Data mining, on the other hand, builds models that detect patterns and relationships in data, especially from large databases.

To better explain this, here are some common methods of data mining and the types of statistics in data analysis.

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Data mining application

Data mining is basically available as some commercial system. Today, data mining is widely used in almost every industry. For example, financial data analysis is usually systematic because of the reliability of the data. Typical cases of financial data analysis include loan payment forecasting, customer credit policy analysis, customer classification and clustering for targeted marketing, money laundering detection, and other financial crimes. ..

Data mining plays a more important role in the retail industry because it collects data from a variety of sources such as sales, customer purchase history, goods transportation, consumption, and services. In the retail industry, it helps identify customer behavior. Design and build a data warehouse based on the benefits of data mining. Multidimensional analysis of sales, customers, products, time and region. Effect of sales campaigns; Customer retention; Product recommendations, and item cross-references.

In the telecommunications industry, data mining helps identify communication patterns, detect fraud, improve service quality, and make better use of resources.

Data mining also contributes significantly to the analysis of biological data such as genomics, proteomics, functional genomics, and biomedical research. This is useful for semantic integration of heterogeneous distributed genome and proteomics databases, association and path analysis, visualization tools for genetic data analysis, and more.

It is also useful for analyzing large amounts of data from fields such as earth science and astronomy. All other scientific applications such as climate and ecosystem modeling, chemical engineering, and fluid dynamics benefit from data mining.

Data mining has also found tremendous applications in detecting intrusions and threats that attack network resources and play an important role in network management. Areas where data mining may be applied to intrusion detection are for intrusion detection, association and correlation analysis, aggregation to help select and build identifying attributes, stream data analysis, ANOVA, visualization and query tools. Data mining algorithm development.

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Data mining trends

You can choose one of these different data mining methods, depending on the type of data and the type of information you are trying to decrypt.

Here are some trends in the evolving concept of data mining:

Common methods of data mining

Here are some trends in the evolving concept of data mining:

  • Exploring applications
  • Scalable and interactive data mining method
  • Visual data mining
  • A new way to mine 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 just an overview of data mining and statistics, both of which are vast and informative themes.Want to know more about data mining and statistics and how they work together? Check out some of us Big data and analysis course, Including us Master of Data Science Program, And Business analyst training..

https://www.simplilearn.com/data-mining-vs-statistics-article Differences between data mining and statistics

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