The goal of data analytics is to get results to make better decisions and better outcomes for business. think about descriptive, predictive, and prescriptive analytics and provide some examples with your thoughts behind your statements.

Explanation:

Data analysis is a process used to explore, refine, modify, and model the data for finding useful information, making conclusions, and making decisions. Data analysis is a process used to obtain raw data and to make it more user-friendly by decision-making. The data is collected first, and then analyzed to answer questions, test hypotheses, or reject theories.

Descriptive analysis or statistics are one of the three basic parts of statistics science. It is the statistics about compiling, collecting, summarizing and analyzing numerical data. The main difference of descriptive statistics from inferential statistics or inductive statistics with more appropriate terms is that the goal of descriptive statistics is to express and summarize a data set as quantitative number values or count or sort values, and about the character of the statistical population that is accepted to represent such data as inferential statistics. is not the goal of obtaining analytical expressions for predictive or hypothesis testing. Even though the analysis of quantitative data is a study aimed at obtaining its main results using inductive statistical analysis, descriptive statistics tools must be used to support formal analysis. For example, a study involving a formal statistical analysis with topics of human behavior typically covers the overall sample size, sample size of important subgroups, average age, male / female ratios of people treated as data subject, and various demographic, social or clinical characters. supplied with tables.

Predictive analytics is a class of data analysis methods that focuses on predicting the future behavior of objects and subjects in order to make optimal decisions. Predictive analytics uses statistical methods, data mining methods, game theory, analyzes current and historical facts to make predictions about future events. In business, predictive models use patterns found in historical and executed data to identify risks and opportunities. Models capture relationships among many factors to make it possible to assess the risks or potential associated with a particular set of conditions, guiding decisions about possible transactions. It is used in actuarial calculations, financial services, insurance, telecommunications, retail, tourism, healthcare, pharmaceuticals and other fields. One of the well-known applications is credit scoring, scoring models process credit history, loans, consumer data and other information and provide an assessment of a potential borrower in terms of prospective solvency and forecast of timely payments on loans. One of the drawbacks of predictive analytics is the weak accounting for qualitative shifts, changes after bifurcation points, since they are built on quantitative, probabilistic methods.

The prescriptive analysis is the third and final phase of the business analysis. Extended prescriptive analysis beyond predictive analysis specifying both the actions necessary to achieve the predicted results and the related effects of  decision. This phase of analysis uses the suggestions of the applications of mathematical and computational sciences to take advantage of the results of descriptive and predictive analyzes. Usually, in a first phase a descriptive analysis is made, widely used in the majority of today's business areas and it answers the question of what happened and why. Then a predictive analysis is done or should be done that answers the question of what will happen: historical data is combined with rules, algorithms and occasionally data external to the company or organization to determine a probable event. Finally, the prescriptive analysis phase which aims to recommend actions for the benefit of predictions and show their implications and why they will occur

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