| |9 NOVEMBER 2025HIGHERReviewthat they are using data in an ethical and responsible way"Its sub domains are Data Visualization, Data Mining, Predictive and Prescriptive Modelling and Analysis, Data Analytics, Computational Science, Pattern Recognition, Machine Learning, Deep Learning, Big Data and Big Data Analytics and many more. The career opportunities in Data Science include Data Scientist, Data Analyst for Predictive and Prescriptive Modelling and Analysis for Businesses, Data Mining Specialists, Big Data Analyst and Algorithm Specialists for Machine Learning", Says Dr. Munish Sabharwal, Professor and Associate Dean - Computer Science, Chandigarh UniversityLeveraging Data to Improve Student RetentionOne of the most significant areas where data analytics can be used to improve student success is in the area of student retention. By collecting and analyzing data on student behaviour, preferences, and challenges, colleges and universities can identify at-risk students who may be considering dropping out and provide targeted support to help them stay on trackFor example, some institutions are using predictive modelling to identify at-risk students before they drop out. Predictive modelling uses to identify patterns in student behaviour that may indicate a higher risk of dropping out, such as low grades, poor attendance, or a lack of engagement. Once these students have been identified, institutions can provide targeted support, such as academic tutoring or counselling, to help them stay on track and ultimately graduate. Another way that data analytics can be used to improve student retention is through the use of early warning systems. Early warning systems use data analytics to identify students who may be at risk of dropping out based on factors such as attendance, engagement, and course grades. Once these students have been identified, institutions can provide targeted support to help them stay on track.Data analytics is transforming higher education institutions in many ways. By analyzing data on student performance, engagement, and behaviour, institutions can identify patterns and trends that help them make informed decisions. From predicting student outcomes to identifying areas where students might need additional support, data analytics is helping institutions to better serve their students and improve student success rates. While there are certainly challenges and considerations that institutions need to keep in mind when implementing data analytics, the benefits are clear. As data analytics continues to evolve, we can expect to see even more exciting innovations in higher education.
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