The event was compiled by RGIT’s alumni Association under the guidance of the faculty of the department of the computer science. The occasion was graced by the Alumnus of the computer department, Mr Ranjeet Walunj, who is from pass out batch of 2000. Speaking on the occasion, Dr Satish Ket and faculty of computer engineering department welcomed alumni with his compassionate words and mentioned the objective of holding the seminar on data science for the advancement of our knowledge and giving a brief idea about future scope and demands.
In this seminar, Mr Ranjeet Walunj, who is currently working as Chief Technology Officer at B2X Care Solutions GMBH as an Investment Scout & Startup Advisor, discussed how Data science is the core that drives new research in many areas from environmental to social sciences.
Data Science is an emerging field that has grown out of the tremendous growth in statistics and computer science in the past few decades. Data can help one seek to understand patterns in the world in light of inherent uncertainty. In order to study and address many of the challenges in our society, one must have the tools to process data, perform computations, summarize, investigate, and communicate important findings from the information. Data science can play a critical role in many efforts to enhance the conditions of the human person and the world.
There are many associated scientific challenges, ranging from data capture, creation, polishing, storage, search, sharing, modelling, analysis and visualization. The Seminar explored both theoretical and practical works on Data Science and Advanced Analytics with visionary speakers to outline different insights and views about today and future trends. The Seminar was also featured by a panel session to discuss challenges on selected topics and helped to solve real-time problems By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data collection, cleaning and processing, analysis methods, and visualization to produce actionable insights from big data.