CSE Events -Power Seminar On Data Analytics

Power Seminar On Data Analytics

As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.

The data analytics process has some key components that are needed for any initiative. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

  • Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.
  • The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. This information provides new insight from data. Advanced analytics addresses “what if?” questions.
  • The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. The collection of big data sets is instrumental in enabling these techniques. Big data analytics enables businesses to draw meaningful conclusions from complex and varied data sources, which has made possible by advances in parallel processing and cheap computational power.

 

Follow us on

RAVINDAR MOGILI

Associate Professor

M.Tech (CSE), (Phd)

14 Years of Teaching experience

Area of interest: Data Mining, Machine learning, Image processing

ravindermogili@gmail.com

Mobile: 9493142141

 

Most people associate a personal computer (PC) with the phrase computer. A PC is a small and relatively inexpensive computer designed for an individual use. PCs are based on the microprocessor technology that enables manufacturers to put an entire CPU on one chip. Personal computers at home can be used for a number of different applications including games, word processing, accounting and other tasks. Computers are generally classified by size and power as follows, although there is considerable overlap. The differences between computer classifications generally get smaller as technology advances, creating smaller and more powerful and cost-friendly components. Personal computer: a small, single-user computer based on a microprocessor. In addition to the microprocessor, a personal computer has a keyboard for entering data, a monitor for displaying information, and a storage device for saving data. Workstation: a powerful, single-user computer. A workstation is like a personal computer, but it has a more powerful microprocessor and a higher-quality monitor. Minicomputer: a multi-user computer capable of supporting from 10 to hundreds of users simultaneously. Mainframe: a powerful multi-user computer capable of supporting many hundreds or thousands of users simultaneously. Supercomputer: an extremely fast computer that can perform hundreds of millions of instructions per second.