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An Introduction to Data Analytics: Its Types and Its Applicationsdoi:10.4018/978-1-5225-2031-3.CH001A. S. AbdullahS. SelvakumarA. Abirami
Nearly every aspect of business is affected by data analytics. There are many powerful tools that can quickly process large amounts of data. For businesses to capitalize on data analytics, they need leaders who understand the data analytic process. Even more valuable are leaders who know how to...
The goal of data analytics is to gain understanding and intelligence from data which can be used to make improvements or changes in business. Analytics often involves studying past historical data for hidden valuable trends, to analyze the effects of certain decisions or events, or to evaluate the...
Predictive Modeling:Some advanced data analytics techniques that the data scientists and organizations are using are: Random Forests, Matrix Factorization, TensorFlow, Simulated Annealing, etc. Visualization:Many organizations are adopting more open-source technologies for their business. A few examples are...
Analytics encompasses emerging industry practices, such as data mining, big data analytics, machine learning, AI, and predictive analytics. It is a term that is used to describe the technical aspects of analytics that have predictive capabilities and can be used to solve business problems....
Finally, one of the largest advantages of tight integration is the ability to build applications that seamlessly combine different processing models. For example, in Spark you can write one application that uses machine learning to classify data in real time as it is ingested from streaming sources...
Figure 1.1: Buy a beautifully printed in-color version of “A Business Analyst’s Guide to Business Analytics(2e)” on Amazon:http://www.amazon.com/dp/B0CFZMKRGX. There are three languages at the core of any data-driven business analysis. I label the three languages narrative, math, and...
Data Analytics Introduction Training InformationBack at work, attendees will be able to:Define what Data Analytics is and how it helps with business-focused decision-making Understand the fundamentals of pattern recognition Differentiate between data roles such as Data Analyst, Data Scientist, Data ...
This book is all about how to build a data lake that brings raw (as well as processed) data to a large user community of business analysts rather than just using it for IT-driven projects.The reason to make raw data available to analysts is so they can perform self-service analytics. ...