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A Brief Summary of the Science of Data Analytics

Data Science and Data Analytics are two most common terminologies in today’s data driven world. How a business uses big data to make key business decisions is critical to the future success of the firm. Data is collected into raw form and processed according to the requirement of a company and then this data is utilized for the decision making purpose. This process helps the businesses to grow & expand their operations in the market. Data or information is typically in raw format. The increase in size of the data has given rise to the need for carrying out inspection, data cleaning, data transformation as well as data modeling to gain insights from the data in order to derive conclusions for better decision making. Data Mining is a popular type of data analysis methodology to perform data modeling as well as intelligence gathering that is geared towards predictive purposes. Business Intelligence operations provide various data analysis capabilities that rely on data aggregation. In Statistical applications, business analytics is divided into Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA). EDA focuses on discovering new elements in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive Analytics does forecasting or classification by focusing on statistical or structural models. There are four types of techniques used for Data Analysis - Descriptive, Predictive, Diagnostic and Prescriptive Analysis. In descriptive analysis, we analyze and describe the features of a data. It deals with the summarization of information. In descriptive analysis, we deal with the past data to draw conclusions and present our data in the form of dashboards. In businesses, descriptive analysis is used for determining the Key Performance Indicator (KPI) to evaluate the performance of the business. With the help of predictive analysis, we determine the potential future outcome. Based on the analysis of the historical data, we develop effective models to forecast the future. It makes use of descriptive analysis to generate predictions about the future. It is important to think critically about the nature of data and understand the descriptive analysis in depth. In order to find issues in the data, we need to find anomalous patterns that might contribute towards the poor performance of our model. With diagnostic analysis, we are able to diagnose various problems that are exhibited through the big data. Businesses use this technique to reduce losses and optimize performance. Prescriptive analysis combines insights from descriptive, predictive and diagnostic analytical techniques. It is the final frontier of data analytics. Prescriptive analytics makes heavy usage of Artificial Intelligence in order to facilitate companies into making careful business decisions.

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