Data Mining

Analytics

Data Mining

Analytics is the methodical scientific, systematic analysis of quantitative data or statistics gathered by different means. It is primarily used for the communication, diagnosis, and analysis of meaningful trends in collected data. It also involves using various data patterns towards efficient decision making.

Analytics is an analytical method of collecting, organizing, and analyzing data to answer specific questions about specific areas of interest or application. It is often referred to as “data mining” in the industry. It combines data gathering and analysis with a systematic method of communication of the results. It is usually done by using computers and software.

Analytics is the combination of mathematical algorithms with the data collected. Data mining is a method by which data is extracted and analyzed from a variety of sources. These may be from a variety of different fields, such as business, science, engineering, social sciences, public health and medicine, medical research and education, telecommunications and banking etc. The algorithm to solve problems is usually developed through the use of mathematical principles.

Data mining is a systematic process in which data is gathered in different ways, so as to analyze them. Analysis is a step-by-step process in which mathematical algorithm is applied in order to generate relevant results or conclusions. Some of the methods to generate relevant results are linear regression, bivariate, logistic and Kriging. These techniques and algorithms are then applied to gather useful information from the data. There are different ways of gathering information in analytics.

Some of the data that can be collected through analytics include web browsing behavior, search engine optimization (SEO), e-commerce sites, e-mail marketing campaigns, web content creation, and the website traffic. Analytics has helped many companies and organizations in gaining new insights in their product and services. It helps them in finding out new ways of selling and providing services. These data gathered through analytics help to improve the products and services that are being offered by the company.

Data mining can be broadly classified into two categories: those used for data extraction (e.g., cell counting) and those used for statistical analysis (e.g., logistic regression). Data extraction can be categorized into two groups: qualitative and quantitative. In a qualitative analysis, information that can be collected through analytics includes the quality or quantity of information; while quantitative data is information, that comes from statistical patterns or cycles. {i.e., frequencies. frequencies and standard deviations of measurements. The quantitative data will come from statistics and mathematical formulas and will come from empirical methods.

Data mining can be considered as the art of extracting the best data from the large and intricate databases without violating the confidentiality of the data, while preserving the integrity and reliability of the data and the quality of the results. It is essential that the collected data is analyzed in an organized manner in order to ensure reliability. Statistics are the best tools available for collecting and analyzing the large data sets. Statistics are a set of mathematical concepts and formulas that give you statistical information about any given variable (or set of variables) based on its frequency, mean, standard deviation, mean value, standard error, and correlation coefficient among other related properties. The use of statistics requires a systematic approach and a thorough understanding of the concepts involved in statistical methods.

The business needs to make use of various statistical methods to achieve its goals, objectives, or strategies. For example, for marketing research, it is necessary to have access to a wide range of market information. Data mining can also help in this regard. For instance, the internet has enormous amounts of data regarding online consumer behavior, online shopping behavior, and the purchase tendencies of customers, so that the research can be conducted to understand the customer buying behavior and their buying preferences.