Why Data Science?
Data Science encompasses multidimensional area that uses statistics, predictive modeling, machine learning techniques to determine useful information from data to solve real-life problems. The conglomerations of high-end computer with huge storage capacity, powerful software algorithms and sophisticated mathematical modeling has helped us to explore every form of data around us (may be structured, semi-structured and unstructured data) and produce fast insights to make fact-based decisions. In the era of Big Data Analytics, the industry is constantly examining large and varied dataset to discover underlying patterns and trend that can help organizations make more precise, apt data-sabby decisions.
The U.S. Bureau of Labor Statistics, predicted data scientist jobs to increase by 22 percent by 2020. While the present vacancies amount to 97,000, India is the second-biggest analytics jobs hub after the US, a recent study by skilling platform Great Learning and Analytics India Magazine revealed. The figure is poised to more than double to two lakhs by 2020. “By 2015, however, there were already over 2,350,000 job listings for core Data Science and Analytics (DSA) jobs in theUnited States, and by 2020 the number of DSA job listings is projected to grow by nearly 364,000 listings, to about 2,720,000openings. If McKinsey’s predicted supply of 2.8 million analytically savvy workers is accurate, then nearly every one of theseworkers must change jobs annually to fill open DSA positions.”1
The evolution of Data Science Analytics profession has evolved in recent times has generated several roles in industry varying from Data Scientists, Data Analysts, Data Systems Developer to Data Driven Decision Makers.
There is no doubt that Analytics & Big Data have revolutionised the strategy of business around the globe. All companies, irrespective of size, depend on data and related analytics and scientific mechanisms to make critical business decisions based on the explored information from the data. From comprehending consumer behaviour to predicting market trends, even right down to product features and product scaling, the steps aremotivated bydata science in major companies across the world. Digital ecosystems are generating digital business that gain value through enhanced interactions between business, people and things.
The objective of the Executive MTechin Data Science program is to enable industry professionals to apply learn Data Analytics and Machine learning techniques to solve real-life problems. As a data scientist, they can help the organization not only in collecting and treating data but also in areas of knowledge management, data management, data security, and interaction design. Even the practitioners can use their knowledge in business functions such as strategy formation, operational processes,and decision making.
The course content of the program has designed with valuable inputs from data practitioners from industry and academic experts in the domain of data science. Please click the link. Apart from theory and hands-on implementation, every year, a student will be assessed on term projects assigned to them. The classes will be taken by in-house faculty followed by lectures from industry practitioners who can add extra flavors to enhance the vision of the students in the concerned domain.
At the end of the Data Science program,
- The students will learn fundamentals of statistics, mathematical modelling for data analytics and will be able to apply them on real-time data
- The students will gain knowledge of Machine Learning and Artificial Intelligence and concerned tools and techniques to exploreBig Data problem and analyse them to deduce useful information and decision
- The several electives will help the students to be specialized in domains like Social Media Analytics, Cybersecurity, NLP, Image Analytics, Healthcare analytics which have he demands in industry
- As a whole, the students will be able to grasp theunderstanding of engineering and management principles in the domain of data science to handle/manage projects in a multidisciplinary scenario.
B.Tech. / B.E.(with minimum 60% marks or equivalent CGPA) in any engineering discipline.
M.Sc. (with minimum 50% marks or equivalent CGPA) in Computer Science/ Statistics / Electronics Science/Physics/Mathematics.
MCA (with minimum 60% marks or equivalent CGPA).