Data Mining And Statistics: A Guide For 2022

 

What Is The Connection Between Data Mining And Statistics?

Data and analytics are tools with the potential to speed up organizational growth. A recent Mckinsey Global Institute survey found that businesses that make fair use of data are 23 times more likely to attract clients than those that stick to the status quo.

The report further reveals that 71% of consumers expect personalized interactions from a business- something that can only be facilitated through data and analytics-backed decision-making.

 

As the significance of data analytics increases, businesses are discovering more and better uses for data mining and statistics.

Companies utilize data mining to transform unstructured data into actionable information. On the other hand,  statistics is a data mining component that offers the resources and analytical methods that help us make sense of large volumes of data.

The primary focus of statistics is on drawing inferences from probabilistic models whereas data mining depends on statistics, visualization, analysis, and pattern recognition to discover useful insights. 

 

Data mining and statistics are closely related but certainly not interchangeable. They do have connections as well as differences and understanding how these two differ is the key to making the most of them.

 

What Is The Connection Between Data Mining And Statistics?

Data mining was once a manual process; however, with the development of affordable computing power, it has evolved into a semi-automatic process.

Now, various methods have simplified the data mining process, one of which is statistical analysis.

The statistical method used in current data mining practices is mainly derived from the broad toolset that was created to address challenges occurring in other sectors. 

 

Data Mining And Statistics_ A Guide

 

In Data mining, statistical analysis is used particularly in two ways:

How Is Statistical Analysis Used In Data Mining?

Data mining is a computer-assisted information extraction of big and complicated data sets. Statistics is used to analyze that data and represent it for easier comprehension.

In the analysis of huge data sets and the discovery of correlations or patterns across different fields in big relational databases, both practices make extensive use of statistical techniques.

 

Following are the statistical techniques and software used to perform data mining accurately.

 

Statistical Techniques Used In Data Mining

Most analytics used in the data mining process is based on statistical methodologies. The many analytic models rely on statistical ideas that provide numerical results that are relevant to particular business goals.

 

Statistical Techniques Used In Data Mining

 

  1. Linear Regression: The target variable is predicted using the best linear connection between the independent and dependent variables.
  2. Classification: One strategy for enhancing the effectiveness of the analytical process is classification. This data mining technique involves categorizing a set of data so that predictions and analyses may be made with higher accuracy. Classifying the data allows for efficient analysis of very big datasets.
  3. Correlation Analysis: The link between the variables in a pair is captured statistically through correlation analysis. Such variables’ values typically indicate an element’s property and are kept in a column or rows of a database table.
  4. Regression Analysis: Regression is a statistical data mining technique that forecasts a variety of numerical values based on established numerical data. Regression models are employed in a wide range of sectors for trend analysis, environmental modeling, and financial data forecasting.

 

Prominent Statistical Analysis Software For Data Mining

A significant number of statistical analysis tools are available for data mining.

These tools are used in different phases of data mining, from data collection and analysis to the graphical representation of data for better understanding.

 

But two statistical tools: R and SAS, are widely used in the process.

 

1.) R is a comprehensive set of tools for calculating, manipulating data, and displaying graphics. R, the programming language of choice for data scientists, may be used to address any data science issue.

The program offers a number of statistical and graphical approaches, including linear and non-linear modeling, time-series analysis, classification, and clustering, as well as the rapid and easy implementation of machine learning algorithms.

 

2.) SAS is the statistical software that is highly used if the company wants to carry out multivariate analysis, data management, advanced analytics, or predictive analytics.

Furthermore, the software enables users to engage with their data through interactive graphs and charts that make things simpler to understand. 

 

Such software helps in representing the data visually so that the team can understand the result of the data. After getting a clear picture of the data, the team can draw implications for business growth. 

 

What Is The Difference Between Data Mining And Statistical Analysis?

Although data mining uses statistical methods in its process, they do have many differences.

Data mining involves sorting through enormous amounts of data to find hidden patterns, connections, and other features that may significantly impact the company.

Statistics use tried-and-true mathematical models, formulas, and other techniques to identify trends in data. Other differences are given below: 

 

Data Mining Statistical Analysis
Make use of numeric or non-numeric data Make use of only numeric data
Can be used for a huge data set Used only for a small data set
It’s easy to automate It is difficult to automate
Require less user interaction  Requires more user interaction
It’s an inductive process It is a deductive process
Less focus is on data collection More focus is on data collection
Has exploratory approach  Conclusions are drawn based on probability distributions
Heuristics are important Heuristics are rejected

 

1.) Deriving Insights And Interpreting Data

Statistics and data mining are two very distinct ideas. The practice of extracting practical insights from data is known as data mining. The study of data collection, analysis, and interpretation is known as statistics.

 

2.) Ease Of Automation

Data mining is easy to automate since it just needs small user input to validate the model. However, statistics are difficult to automate since it needs human input to confirm the model.

 

3.) Differences In Input

Much of the data in the contemporary world is fetched from social media like LinkedIn, Instagram, Facebook, etc. LinkedIn is particularly a valuable source for business.

LinkedIn data mining has many benefits for your business, as you can collect various business network information; this is why many companies outsource data mining for LinkedIn to use the information for their business growth. 

 

On the other hand, statistics exclusively deals with quantitative data. Therefore, the initial step in using statistics for data is frequently to derive numerical measures from it.

 

4.) Tools And Techniques

A data mining specialist has to be familiar with the methods and tools used for data analysis, retention, and visualization.

Data miners must thus be knowledgeable about processing frameworks like Spark and SQL and visualization software such as Tableau, PowerBI, etc., that can be used to display the findings. 

 

A statistician uses free source or paid software to calculate descriptive statistics and draw conclusions. This comprises both proprietary and open-source applications, including SAS, R, and Minitab.

For statisticians, even a spreadsheet program like Open Office or Microsoft Excel is an effective tool.

 

5.) Exploring Data And Formalizing Thoughts

In contrast to statistics, where this is mainly about drawing conclusions based on probability distributions, data mining frequently results in a prediction approach as its ultimate product.

Data mining frequently has an exploratory focus. The goal of statistics is to verify hypotheses.

 

6.) Focus On Data Collection

An essential component of statistics is the gathering and cleansing of data. Data mining does not place a lot of focus on data collecting and is designed to function with almost any type of data.

Working with the data already in hand is more important than developing data collection tactics.

 

7.) Importance Of Domain Knowledge

Heuristics are informal guidelines developed based on subject expertise. In data mining, heuristics are crucial and frequently serve as the foundation of investigation.

However, all heuristics are rejected in statistics, and data interpretation is limited to mathematical proof and probability.

 

Conclusion

After realizing the importance of data in this century, more money is being put into data mining and analysis by both multinational and small businesses.

Companies are using the data retrieved from various sources like social media to get an idea of what their customers actually need and dedicating a separate department to data mining and research so as to make the most out of the information they have in hand. 

 

On that journey, I hope this blog helps you make more sense of how data mining and statistical analysis differ and how you can put them to good use.

 

Gracie Ben Author
Gracie Ben

Gracie Ben is a data analyst currently involved with DataEntryIndia.in. For 10+ years, she has actively contributed to the growth of many enterprises that have to outsource data mining services to the organization. By innovating and implementing data mining she has contributed to the growth of small to big size companies. She also likes sharing her interest in data science with other enthusiasts through informative blogs. 

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