• Achieved an 85% placement record, reflecting strong industry readiness among students.
• Six student startups have been established, promoting entrepreneurship and innovation.
• Successfully completed six innovation projects, contributing to research and practical solutions.
• 13 patents have been published, demonstrating the department’s commitment to innovation and intellectual property development.
• The department has 9 faculty members, including 2 doctorates, ensuring strong academic and research expertise.
• Well-equipped with 6 specialized laboratories to support hands-on learning and research.
• Faculty members have contributed 6 journal publications and 98 conference publications, strengthening the department’s research output.
• Organized 40 technical events, including workshops, seminars, and competitions to enhance student skills.
• Established 8 industry collaborations, enabling practical exposure, internships, and knowledge exchange.

B.Tech in Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

They must also be able to utilize key technical tools and skills, including:
R, Python, Apache Hadoop, MapReduce, Apache, Spark, NoSQL databases, Cloud computing, D3,, Apache Pig, Tableau, iPython notebooks, GitHub.