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What is Data Analytics ?
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics.
Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Normally the data analytics process starts with descriptive analytics. This describes the historical tren in your data. Descriptive analytics is able to answer question about what happened. It does not make prediction or directly inform decisions. It focuses on summarizing data in meaningfull and descriptive way.
The next part is advanced analytics. This is the data science part, it takes the advantage of extracting the data and making predictions and identifying the trends. It includes classical statistics as well as machine learning. Machine learning consists of natural language processing, neural networks, sentiment analysis, image processing and many more.
Types of Data analytics
Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
How to Use Data Analytics: The analytical process
Understand the business problem.
Collect data related to the problem.
Prepare the data for data analysis.
Analyze the data to extract meaningful information.
Deploy analytics models.
Monitor and optimize the model performance.
First generating insights from the data to ensure that it is correct, organized and actionable. Many companies start by creating a standard way to access data across disparate sources using a virtual data layer. After that automate data wrangling. Industry focuses on the time and energy on acting on data outliers or inconsistencies. Data visualization provides interactive dashboards for data representation graphically. It gives companies powerful analytic capabilities in an intuitive format. Many companies use data science to build the model to provide predictive insights. Data analytics results pull real time streaming data for most of the accurate analysis with new insights, making quick decisions simple with clear understanding of both real time and historic data.
Data analytics Vs Data analysis
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modelling and evaluation, and deployment. It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data. Data analytics is used to formulate larger organization decisions.
Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics, statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data through analytics. There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, decision tree, logistic regression, linear to multiple regression analysis, classification to do predictive modeling.
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