The changing technology prospect is described by the phrase Big Data that resulted in a large amount of data, a greater variety of data sources, a continuous flow of data and multiple data formats. As such data is growing rapidly, there is a need for advanced analytic techniques that operates on such data and extracts effective information, unknown patterns, and relationships that help in making decisions. Big Data Analytics provides such valuable insight. In this paper, we start with a definition of Big Data. Then we provide the systematic structure that divides the Big Data system into five sections namely data generation, acquisition and storage, data processing, data querying, and data analytics. This paper is also providing a brief overview of different techniques and technology used in Big Data Analytics.
Most of the developing countries are facing the problem of ever-rising low-quality population. To convert this low-quality population into a high-quality one, efforts are required to be laid down. These efforts include investment in Research and development and in the education sector. If the people living in an area will be educated then they will be productive for the nation and eventually contribute towards its GDP. The advancement of technology helps educational institutions to turn raw data into actionable insights to achieve desirable results. This study has worked towards prediction models so that student's performance be evaluated timely so that necessary steps be taken in due time to improve their performance. In this study, the record of 265 Computer Science and Engineering students at UIET, MDU Rohtak are used for prediction task by using five algorithms namely Simple Linear Regression, Random Forest, Decision Table, SMOreg, LWL. Algorithms are compared by using WEKA tool. The results showed that out of these five algorithms, Simple Linear Regression gave the best prediction accuracy with the highest correlation coefficient of 0.78, lowest Mean Absolute Error of 4.97 and lowest Root Mean Squared Error of 6.75. This study has laid the foundation in selecting efficient algorithms for predicting the results of students.
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