B.Tech CST (Big Data) - Financial Data Engineering & Analytics

Duration
4 Years Full-Time Program
Eligibility

Eligibility Norms for Admission to B. Tech Programs: 

Candidates seeking admission to B.Tech. Program should have passed the Pre-University / Higher Secondary /or any equivalent 10+2 examination from an approved State / Central govt. board or from an international board like University of Cambridge "A" level certificate or certificate from International Baccalaureate (IB), Geneva, etc., with a minimum of 45% marks in Physics and Mathematics as compulsory subjects along with either Chemistry / Biotechnology / Biology / Electronics /Computer as an additional subject, and with 55% aggregate for SOCSE & 50% aggregate for SOE of total marks in the Qualifying Examination. Candidates should have appeared in any national / state level / Presidency University entrance examination viz. CET, Comed – K, JEE and such others.   

Eligibility Norms for Admission to B. Tech Programs (Lateral)  

A candidate who has passed any diploma examination or equivalent examination and obtained a minimum of 60% aggregate is eligible for admission to B. Tech Programs (Lateral Entry). Candidate should have appeared in any state level entrance examination. 

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B.Tech CST (Big Data) - Financial Data Engineering & Analytics

Programme Overview

B.Tech CST (Big Data) – Financial Data Engineering & Analytics is a future-ready engineering programme that combines the foundations of Computer Science and Technology, Big Data, Data Engineering, Analytics, and Financial Intelligence to prepare students for the rapidly evolving data-driven financial ecosystem. The programme integrates programming, databases, statistics, data management, distributed computing, cloud technologies, Big Data analytics, and financial data processing to develop scalable and intelligent data solutions.

The curriculum progresses from core concepts such as Python, C, Data Structures, Object-Oriented Programming, Database Management Systems, Computer Networks, Operating Systems, and Algorithms to specialized areas including Big Data Technologies, Distributed Data Processing, Data Warehousing, Data Engineering, Cloud Computing, Machine Learning, Data Visualization, and Advanced Analytics.

The programme focuses on applying data engineering and analytics techniques to banking, FinTech, investment, insurance, digital payments, risk management, and financial decision-making. Students develop the ability to collect, process, integrate, analyse, and visualize large and complex financial datasets to generate meaningful insights and support data-driven business decisions.

The programme emphasizes hands-on and industry-oriented learning through practical laboratories, real-world datasets, internships, data projects, analytics challenges, and an integrated capstone project. Students gain practical experience in building data pipelines, processing large-scale datasets, developing analytical models, implementing scalable data architectures, and creating financial analytics solutions.

With dedicated components for industry readiness, aptitude, employability, project-based learning, and practical exposure, the programme prepares graduates for careers in Data Engineering, Big Data Analytics, Financial Data Analytics, FinTech, Business Intelligence, Data Science, Risk Analytics, Data Architecture, and Financial Technology. The programme also provides a strong foundation for higher studies, research, professional certifications, and entrepreneurship in Big Data and financial analytics.

Course Curriculum

  • 01Calculus and Differential Equations
  • 02English for Technical Communication
  • 03Optoelectronics and Quantum Physics
  • 04Optoelectronics and Quantum Physics Lab
  • 05Engineering Graphics
  • 06Digital Design
  • 07Digital Design Lab
  • 08Introduction to Design Thinking
  • 09Computational Thinking using Python
  • 10Industry Readiness Program – I
  • 01Calculus and Differential Equations
  • 02English for Technical Communication
  • 03Chemistry of Smart Materials
  • 04Chemistry of Smart Materials Lab
  • 05Foundations of Integrated Engineering
  • 06Basics of Electrical and Electronics Engineering
  • 07Basics of Electrical and Electronics Engineering Lab
  • 08Indian Constitution
  • 09Computational Thinking using Python
  • 10Industry Readiness Program – I
  • 01Probability and Statistics
  • 02Advanced English
  • 03Chemistry of Smart Materials
  • 04Chemistry of Smart Materials Lab
  • 05Foundations of Integrated Engineering
  • 06Basics of Electrical and Electronics Engineering
  • 07Basics of Electrical and Electronics Engineering Lab
  • 08Indian Constitution
  • 09Problem Solving using C
  • 10Problem Solving using C Lab
  • 11Industry Readiness Program – II
  • 12Design Workshop
  • 13Environmental Studies
  • 01Probability and Statistics
  • 02Advanced English
  • 03Optoelectronics and Quantum Physics
  • 04Optoelectronics and Quantum Physics Lab
  • 05Engineering Graphics
  • 06Digital Design
  • 07Digital Design Lab
  • 08Introduction to Design Thinking
  • 09Problem Solving using C
  • 10Problem Solving using C Lab
  • 11Industry Readiness Program – II
  • 12Design Workshop
  • 13Environmental Studies
  • 01Linear Algebra and Vector Calculus
  • 02Data Communication and Computer Networks
  • 03Data Communication and Computer Networks Lab
  • 04Data Structures
  • 05Data Structures Lab
  • 06Object Oriented Programming Using Java
  • 07Object Oriented Programming Using Java Lab
  • 08Computer Organization and Architecture
  • 09Introduction to Big Data
  • 10Software Design and Development
  • 11Universal Human Values
  • 12Introduction to Aptitude
  • 01Discrete Mathematics
  • 04Database Management Systems
  • 05Database Management Systems Lab
  • 06Data Visualization and Reporting
  • 07Data Visualization and Reporting Lab
  • 08Analysis of Algorithms
  • 09Analysis of Algorithms Lab
  • 10Essentials of AI
  • 11Essentials of AI Lab
  • 12Essentials of Finance
  • 13Aptitude Training-Intermediate
  • 01Theory of Computation
  • 02Web Technologies
  • 03Web Technologies Lab
  • 04Operating Systems
  • 05Operating Systems Lab
  • 06Data Mining and Predictive Analytics
  • 07Data Mining and Predictive Analytics Lab
  • 08Big Data Technologies
  • 09Big Data Technologies Laboratory
  • 10Data Infrastructure for Financial Systems
  • 11Internship
  • 12Logical and Critical Thinking
  • 01No SQL Databases
  • 02No SQL Databases Lab
  • 03Web Intelligence and Analytics
  • 04Web Intelligence and Analytics Lab
  • 05Competitive Programming and Problem Solving
  • 06Big Data Applications in Decentralized and Digital Finance
  • 07Stream Analytics and Fraud Detection
  • 08Open Elective – I
  • 09Aptitude for Employability
  • 01Quantitative Risk Assessment and Credit Scoring Systems
  • 02Secure Data Collaboration and Privacy-Aware Analytics
  • 03Distributed Storage Systems and Scalable Data Processing
  • 04Open Elective – II
  • 05Mini Project/Hackathon - FinTech Data Sprint
  • 01Capstone Project - End-to-End FinTech Analytics Platform

Programme Educational Objectives

After four years of successful completion of the program, the graduates shall be:

PEO 1: Graduates will demonstrate their knowledge in science and engineering as problem solvers and researchers.

PEO 2: Graduates will exhibit skills in cutting-edge technologies to solve societal needs in multidisciplinary areas.

PEO 3: Graduates will develop an attitude towards lifelong learning and ethics to emerge as socially committed entrepreneurs.

PEO 4: Graduates will develop as a freelancing consultant to the computer science and technology & Big Data Industry.

Programme Outcomes (POs)

On successful completion of the Program, the students shall be able to:

PO1. Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

PO2. Problem Analysis: Identify, formulate, review research literature, and analyse complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

PO3. Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.

PO4. Conduct Investigations of Complex Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.

PO5. Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.

PO6. The Engineer and Society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.

PO7. Environment and Sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

PO8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

PO9. Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.

PO11. Project Management and Finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.

PO12. Life-Long Learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

Programme Specific Outcomes

On successful completion of the Program, the students shall be able to:

PSO 01: [Problem Analysis]: Identify, formulate, research literature, and analyse complex engineering problems related to Software Engineering principles & practice, Programming, Big Data computing & analytics, reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

PSO 02: [Design/development of Solutions]: Design solutions for complex engineering problems related to Software Engineering principles & practice, Programming, Big Data Computing & analytics, and design system components or processes that meet the specified needs with appropriate consideration for public health and safety, as well as cultural, societal, and environmental considerations.

PSO 03: [Modern Tools Usage]: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modelling, to complex engineering activities related to Software Engineering principles & practice, Programming, Big Data Computing & analytics, with an understanding of the limitations.

Student handbook

Program Regulations

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USPs

Industry-Integrated Curriculum with 6 courses covering emerging technologies and real-world applications.

  • 34-Credit Industry Learning through courses, internship, mini projects/hackathons, and capstone project.
  • Paytm Virtual Internship providing practical industry exposure and experience.
  • Hands-On Learning through mini projects, hackathons, and industry-oriented capstone projects.
  • Industry Mentorship & Joint Certification to strengthen technical skills and employability.
  • Up to 3 Interview Opportunities to support career readiness and industry placement.
  • LMS & Digital Learning Resources providing flexible access to structured learning materials.

Career Opportunities

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