Baramulla, Nov 15: Division of Agriculture Economics & Statistics at SKUAST- Kashmir inaugurated a week-long Existing Management Development Programme (E-MDP) on “Applied Data Mining and Statistical Learning Using R and R Studio.”
The program, sponsored by the Union Ministry of Micro, Small and Medium Enterprises, aims to provide participants with a fundamental understanding of supervised and unsupervised learning and the underlying statistical framework.
Prof. Haroon Rashid Naik, Director Research at SKUAST-Kashmir, officially opened the training program.
Dr. Fahim Jeelani Wani, the Coordinator, stressed the importance of statistical learning theory as a practical tool for both theoretical analysis and creating algorithms for estimating multidimensional functions.
During the week long program, the participants are expected to acquire technical skills in programming languages for statistical computing to enhance their research capabilities.
Dr. Showkat Maqbool, Associate Professor (Statistics), highlighted the relevance of Statistical Learning, Data Mining, and related aspects in understanding real-world problems with respect to data.
He encouraged students to engage in data-driven investigations and gain hands-on experience in managing and visualizing diverse data types.
Prof. Raihana Habib Kanth, Dean of the Faculty of Agriculture, commended the Division for initiating the program, expressing confidence that it would broaden students’ exposure to Statistical Learning and Data Mining across various sectors, enhancing their career prospects.
The inaugural event, attended by dignitaries, division heads, professors, scientists, faculty members, and program participants, concluded with a formal vote of thanks by Dr. Sajad A. Saraf, Associate Prof. (Agri. Economics).
Dr. Saraf expressed gratitude to the dignitaries, organizing team, and non-teaching staff for their efforts in conducting the training program.
The E-MDP will run until November 22, 2023, providing participants with a comprehensive exploration of Statistical Learning and Data Mining, aligning theoretical knowledge with practical applications.
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