Data Analytics, Types and its Advantages

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Data Analytics, Types and its Advantages

The use of software and specialized system to examine data in order to draw conclusions about it in various aspects is called Data Analytics. Data Analytics is widely used by commercial companies nowadays. It helps the organizations to run the administration more efficiently. It also helps them to make a more appropriate business-related decision. It also helps the researchers, scientists to verify or approve the hypothesis and theory. Data Analysts include various applications under it. Some of the applications under them are business intelligence (BI), online analytical process (OLAP) and advanced analytics.

Advantages of Data Analytics

There are several benefits of Data analysis. It helps us to increase operational efficiency, increasing the business revenues to a very high level, developing good marketing campaigns and also providing better service efforts to the customers. It can also be used for real-time analytics. The real-time analytics include the new as well as old information in the field of Data Analytics Courses

Types of Data Analytics Application

Data Analytics chiefly includes data analytics methodologies, including exploratory data analysis. The confirmatory data analysis also falls in the same category. It is widely used to find out whether the data is real or fake. It can also be distinguished as quantitative and qualitative data analysis. Quantitative data analysis involves the analysis of the mathematical data, whereas qualitative one involves understanding the nonnumerical data. Nonnumerical data includes text, pictures and also audio, video.

Advanced types of Data Analytics

Data mining, Predictive analysis, machine learning, and text mining are some examples of advanced data analysis. Data mining is identifying patterns and trends by sorting large data. Machine learning is another advanced method in which artificial intelligence technology is used by data scientists to go through the data sets. Text mining is also a data analytical process of analyzing the emails, documents, and other contents.

The inside process under Data Analytics

The data analysis is much more than analyzing data. Mainly, in the advanced analytics projects, much of the required work takes a plane in front of us. Preparing and collection of data, then preparing and integrating the data, later testing and revising the analytic process to ensure maximum accuracy of the result. The process starts with the collection of data only. Data Scientist identifies the information which they need for a definite analytic process. Then the IT sector and data engineers take over it. They prepare the desired contents. Once the desired data is made, the next task is to fix the quality of the data. The main work now is to fix the data quality. Now, data cleansing job comes into action. After this, additional data is also prepared to manipulate the previous one if needed. This is the main turning point. The data analysts build a model of it using programming languages such as Python, R language and SQL. Then it is again rechecked before further steps.

Communication through Data Analytics

Data Analytics machines are often set to automatically do all business actions. The last step of the Data Analytics is Data Visualization. It is the process by which the desired results are communicated to the business heads. They are mainly incorporated into the Business Intelligence dashboard that displays all data on a single screen. It can also be updated in real time when needed. This is how the information becomes visible.

Reference Link- Logistic Regression using R

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