Companies interest in data analysis is increasing, as it helps to proliferate their growth, expand their business, lessen the costs and can establish themselves firmly in the global market. Because of these reasons data analysis has been roped with the companies in such a way that it has become an indispensable way for companies to survive the competitive market.
How is Data Analysis done?
Generally, data analysis can be done by using two methods, data mining and statistics. Both these techniques are used by companies in analysing data that aid to better decisions and uplift their business. It is true that both data mining and statistics are related to learning from data and they usually applied in discovering and identifying the structured data from the data sets. Though the aims of both techniques are similar they have different approaches.
How data mining and statistics are different from each other?
Data mining is a process which helps in extracting the meaningful, comprehensible and actionable information from the large and unstructured data sets. By using data mining, the companies can take all the imperative and crucial business decision for the betterment of their businesses.
The core part of data mining is always concerned with the analysis of data and by using various types of software techniques it helps in finding patterns and regularities in different data sets. With the help of the computers, data mining usually finds the patterns by identifying the underlying rules and features in the data of the data sets. Basically, data mining(DM) sits at the interface between statistics, machine learning, artificial intelligence, computer science, database management and data visualization. So, it can be concluded that data mining is the application of various methods of statistics, data analysis, and machine learning which is typically used to explore and analysis of large and intricate data sets.
Data mining helps in extracting new, potential, useful and coherent pattern and information that will benefit the companies which own a chunk of unstructured and uncouth data. Data are always dirty, raw and unpolished and have no use until they have fine-tuned by data mining. Data mining turns unpolished data into structured, polished and usable things. So, data mining is not only modelling and prediction rather it is an entire problem-solving process that a company can only be achieved through team effort and dedication.
Then, What is Statistics?
Statistics: Statistics is basically a component or an element of data mining which provides the tools and analytics techniques that a person needs to deal with large amounts of data. It is true that the statistics is also the science of learning from data. Statistics comprises of everything like from planning for the data collection to data management to various end-of-the-line activities like drawing inferences and interpretations of numerical facts called data and finally, the presentation of results.
Basically, statistics deals with quantifying data. Statistics is more or less like math and thus it uses various tools to find relevant properties of data. It provides with the required tools that are necessary for data mining.
While on the other hand, data mining constructs various models to detect patterns and relationships in data from large data sets. And on the basis of these models, a company can take its decisions. So, data mining is a broader term and statistics is just only a part of it.
In statistics, data is often used to answer specific questions and if the data sets are small, then the company usually prefers to use statistics over the data analysis. While statistics deals with a few hundred to thousand data sets, on the other hand, data mining is applied in analysing millions or billions of data sets. Usually, large data sets have many problems which statisticians are unable to handle, only data mining can solve the issues.
Data Mining vs. Statistics
Both data mining and statistics are interrelated to learning of Big data but they are different from each other, few of the differences are tabulated below:
|Implication||Data mining builds new models to find out patterns and relationships in collected large amount of data from different resources.||Statistics is all about quantifying data. It finds out appropriate properties of data and offers some essential tools for data mining.|
|Working||Data mining is a process of digging deep in the previously available unknown but actionable information from large databases for using it to make some crucial decisions. A set of methods are used to find patterns and relationships within the available data. It is a confluence of various processes including statistics, machine learning, database management, artificial intelligence (AI) and data pattern recognition etc||Statistics is an important component of data mining that offers effective analytics techniques and tools for dealing with large amount of data for benefiting businesses. It is science of data learning that covers everything from collecting to using data efficiently.|
|Methods||Some popular methods of data mining include classification, clustering, neural networks, associations, estimation, visualization and sequence based analysis. You may choose a DM method according to the type of data and the kind of information that you are trying to decode.||, statistical Analysis uses descriptive and inferential methods to make use of big data.|
|Applications||DM is essentially applied commercial applications like financial data analysis, retail industry, telecommunication, biology and other scientific detection. It is also applied in many other related domains of these areas and finds enormous applications in the detection of threats that attack network resources and plays a major role in network administration.||Statistics is used in every data sample to draw out a set of new information. It describes about the character of the data to be analyzed and explore the relation of the data. It uses predictive analytics to run scenarios that help to decide about the future actions. On the other hand statistics gives breathing into a lifeless data.|
|Evolving Trends||some evolving trends are application exploration, visual data mining, biological data mining, web mining, software mining, distributed data mining, real data mining and lots more.||statistics help to identify new patterns in the available unstructured data.|
To conclude, both data mining and statistics are essential and integral part of a company that wants to establish its ubiquitous presence in today’s world. Both data mining and statistics will inevitably grow with each other in the near future. It is true that data mining will not flourish without statistical thinking and, statistics will not be able to thrive on massive and intricate data sets without data mining methods and approaches.