Computer Science & Engineering

Data Science integrates scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It is a multidisciplinary field that uses various techniques from statistics, machine learning, and computer science to analyze and interpret complex data.

The B.Tech program in Computer Science and Engineering (Data Science) aims to build a strong foundation in data analysis, data visualization, and data-centric computing. This course is ideal for undergraduates seeking to develop high-level skills in data science and emerging technologies that help them stand out and grow their careers in the modern data-driven era.

The program will enhance the aspirant’s skill set, making them eligible for current and future job opportunities. Students empowered with data science knowledge can not only seek jobs but also innovate by exploring new data-driven domains.

1. Engineering Knowledge: Apply the knowledge of mathematics, science, and engineering fundamentals to solve complex data science problems.
2. Problem Analysis: Identify, formulate, and analyze data-related problems to derive meaningful insights.
3. Design/Development of Solutions: Design and develop data-driven solutions that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
4. Conduct Investigations of Complex Problems: Use research-based knowledge and methods to conduct experiments, analyze data, and interpret results.
5. Modern Tool Usage: Apply appropriate techniques, resources, and modern data science tools to complex activities with an understanding of their limitations.
6. The Engineer and Society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal, and cultural issues relevant to professional data science practice.
7. Environment and Sustainability: Understand the impact of data science solutions in societal and environmental contexts, demonstrating knowledge of sustainable development.
8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities in data science practice.
9. Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
10. Communication: Communicate effectively on complex data science activities with the engineering community and society at large.
11. Project Management and Finance: Demonstrate knowledge and understanding of data science and management principles and apply these to one’s own work as a member and leader in a team to manage projects.
12. Lifelong Learning: Recognize the need for and have the preparation and ability to engage in independent and lifelong learning in the broadest context of technological change.

1. Data Analysis and Visualization: Ability to apply statistical and computational methods for data analysis and visualization, deriving actionable insights from complex datasets.
2. Machine Learning and AI: Proficiency in developing machine learning models and AI applications to solve real-world problems.
3. Big Data Technologies: Competence in using big data technologies to manage and process large-scale datasets efficiently.

• Core Knowledge and Technical Proficiency: Equip students with a strong foundation in data science principles, including data analysis, data visualization, machine learning, statistical methods, and data management.
• Hands-On Experience: Offer practical experience through labs, projects, and internships to apply theoretical knowledge to real-world problems and datasets.
• Industry-Relevant Skills: Develop industry-relevant skills, preparing students for roles such as data analyst, data scientist, business analyst, and machine learning engineer.
• Critical Thinking and Problem Solving: Enhance critical thinking and problem-solving abilities by tackling complex data challenges and deriving meaningful insights.
• Interdisciplinary Approach: Encourage an interdisciplinary approach by integrating knowledge from computer science, statistics, and domain-specific applications, enabling students to work effectively in diverse fields.
• Research and Innovation: Foster a culture of research and innovation, encouraging students to explore new data science methodologies and contribute to advancements in the field.
• Networking Opportunities: Provide opportunities for networking with industry professionals, alumni, and peers through seminars, workshops, and conferences.
• Career Readiness: Equip students with the skills and knowledge necessary to thrive in the rapidly growing and evolving data science industry, ensuring they are career-ready upon graduation.

Graduates of this program will be well-prepared for a variety of roles in the data science field, including data analyst, data scientist, business analyst, machine learning engineer, and more. With a strong foundation in data science principles and practices, students can navigate and excel in the rapidly growing and evolving data-driven industry.

Graduates of the B.Tech in Computer Science and Engineering (Data Science) program are highly sought after by top companies across various industries. Some of the leading companies that hire data science professionals include:

These companies offer exciting career opportunities, competitive salaries, and the chance to work on cutting-edge projects that drive innovation and impact in the field of data science.