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Centre of Data Modelling, Analytics and Visualization (CoDMAV)

Centre of Data Modelling, Analytics and Visualization (CoDMAV)

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Data has become ingrained in every decision, interaction, and process, emphasizing a growing reliance on data-driven insights. Edge computing environments are driving the adoption of real-time and streaming analytics, taking analytics closer to where data and decisions originate. The ascent of AI and machine learning is revolutionizing data analytics, unlocking profound insights and precise predictions. Interactive and immersive data representation and storytelling are gaining importance in effectively conveying complex insights. These trends collectively signify a dynamic evolution in the data landscape, offering new opportunities for innovation and more informed decision-making.

Data modeling, analytics and visualization face several challenges, including data quality issues and clear and comprehensible visualizations, while data security and privacy require rigorous protection measures. The scalability of handling growing datasets remains an ongoing concern and bias and fairness must be actively managed to ensure equitable analysis. Data analytics encompasses a broad range of skills and finding data
analytics talent can be exceptionally challenging due to the high demand of data scientists and engineers.

Thus, there is a need for a dedicated hub for equipping PES students and PES faculty with the skills, tools, and knowledge to navigate the complex data landscape and drive innovation to address real-world challenges. Hence, PES University has created the Center of Data Modelling, Analytics and Visualization to cultivate professionals who can ethically and responsibly unlock the limitless possibilities offered by data assets.

Goals and Objectives

  • The center aims to foster a culture of excellence in data modeling, analytics, and visualization, equipping individuals with the skills and knowledge necessary to navigate the evolving data landscape.
  • The center would strive to bridge the gap between academia and industry, ensuring that PES graduates are well-versed in theoretical concepts and possess practical, hands-on experience in applying data-driven solutions to real-world challenges.
  • The center would engage with local and global data science communities and industry for research, innovation, consultancy, partnership and knowledge exchange.



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