# Different Types of Analytics

Analytics refers to the systematic analytical study of observed or unplanned data or statistics. It is used for the development, analysis, and communication of statistically significant patterns from real-life data. It also involves applying statistical patterns toward better decision-making. Analytic methods can be categorized into three main categories, namely, descriptive, prescriptive, and probabilistic. Each category has its own advantages and disadvantages.

Descriptive analytics aims at providing descriptive information about a given situation or event. Its most basic goal is to provide insight into the causes and consequences of an action or occurrence by making use of tools, methods, techniques, theories, etc. descriptive analytics can also be termed as descriptive statistics, since it attempts to measure the results that come from observing data that are not necessarily statistically significant. For example, it may show that a sales person purchased five new cars in one month. The statistic might indicate that this is a statistically significant result. However, if you were to observe ten sales people in this month, you might not be able to see any statistical significance.

Descriptive analytics can also include testing or modeling. In simple terms, it is the application of statistical principles to evaluate data in order to create models or predictions about the behavior of a system or phenomenon. This is sometimes referred to as the predictive or explanatory methodology.

Prescriptive analytics deals with the prediction of future events based on past events. It includes forecasting, the determination of causes and effects of a specific event, the estimation of probability of an occurrence, and the evaluation of the likely outcome of an event or series of events. It may also include predictions concerning future events using historical information.

Probabilistic analysis is a quantitative method in which a collection of data, generally those that can be easily observed, can be analyzed using statistical methods. Data sets that fall under this category can include those gathered from surveys, experiments, or clinical trials. This type of analytics is used primarily for predicting statistical patterns or distributions.

Analytical data can be further subdivided into two categories: descriptive analytics and prescriptive analytics. While descriptive analytics seeks to give information about observed situations or occurrences and what they imply, prescriptive analytics focuses on the underlying cause or factors behind the events. and is typically used in a scientific study. It also describes the statistical significance of a data set of observations. Descriptive analytics is often used in research studies as well.

The analytical method used in a given case will depend on whether it is descriptive or prescriptive in nature. The methods and approaches used to gather data will also depend on the particular study. It can also depend on the purpose of the data being collected and how it is going to be analyzed.

The Analytics Techniques that can be used to study and analyze data, including analytical methods, are diverse and there are many available today. These data sources can range from personal interviews, surveys, or surveys of a company’s employees, customers, etc., or a combination of all of these. Data can be collected from online forms, web sites, or other forms of public records.

A data analyst is a person who analyzes a set of data. The Data Analyst is a specialized type of statistician who has extensive training in data analysis and is trained to use statistical methods to study data and make predictions. Data analysts can use various tools, such as mathematical formulas, computers, databases, spreadsheets, and graphs to analyze and predict the future trends of a given data set. Data analysts use statistics to determine the effect of an event on a data set and determine whether or not it will have a positive or negative outcome.

Analytical methods are usually based on probability, mathematical algorithms, statistical probabilities, or probability distributions, but not all analytics methods are based on these methods. Many types of analytics involve a mixture of these three. They can also take advantage of the power of observation or experience. The data analyst must gather information and observe and make decisions based on what he or she has observed. An example of this is weather patterns by gathering a wide variety of weather reports, taking the time to study weather patterns, and then making statistical models based on these observations.

Data mining is another type of analytics that involves analyzing data to uncover hidden information or data. Data mining is used in the context of scientific studies and has the same methods and approach as statistical analysis. One example of data mining is using publicly available information in order to analyze and predict trends in public data to predict what the future holds for the data.

Data mining is an emerging field where one uses data to help predict or detect patterns in a given data set to see if certain conditions or events will occur, whether or not they will be favorable or unfavorable. Other examples of this would be in the context of a new product being developed.