B. Tech Computer Science & Technology (Big Data)

Duration
4 Years
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 Computer Science & Technology (Big Data)

Programme Overview

The program is designed to meet the increasing need for highly skilled data analysts who can analyze the growing amount of data confronting them in a variety of disciplines and transform it into usable information for use in decision-making. The program delivers rigorous training in computational techniques and provides mastery of data analysis. Developing skills in solution development, database design (both SQL and NoSQL), data processing, data warehousing, and data visualization helps build a solid foundation to support the role of a business analyst. 

Course Curriculum

  • 01Calculus and Linear Algebra - MAT1001
  • 02Optoelectronics and Device Physics - PHY1002
  • 03Engineering Graphics - MEC1006
  • 04Technical English - ENG1002
  • 05Introduction to Soft Skills - PPS1001
  • 06Problem Solving Using C - CSE1004
  • 07Digital Design - ECE2007
  • 08Introduction to Design Thinking - DES1146
  • 01Applied Statistics - MAT1003
  • 02Environmental Science - CHE1018
  • 03Basic Engineering Sciences - CIV1008
  • 04Problem Solving Using JAVA - CSE1006
  • 05Advanced English / Foreign Language Courses - ENG2001/FRLXXXX
  • 06Enhancing Personality Through Soft Skills - PPS1012
  • 07Basics of Electrical and Electronics Engineering - EEE1007
  • 08Indian Constitution and Professional Ethics for Engineers - LAW1007
  • 09Innovative Projects Using Arduino - ECE2010
  • 01Applied Statistics - MAT1003
  • 02Basics of Electrical and Electronics Engineering - EEE1007
  • 03Technical English - ENG1002
  • 04Introduction to Soft Skills - PPS1001
  • 05Problem Solving Using C - CSE1004
  • 06Basic Engineering Sciences - CIV1008
  • 07Environmental Science - CHE1018
  • 08Indian Constitution and Professional Ethics for Engineers - LAW1007
  • 01Calculus and Linear Algebra - MAT1001
  • 02Engineering Graphics - MEC1006
  • 03Problem Solving Using JAVA - CSE1006
  • 04Advanced English / Foreign Language Courses - ENG2001/FRLXXXX
  • 05Enhancing Personality Through Soft Skills - PPS1012
  • 06Introduction to Design Thinking - DES1146
  • 07Optoelectronics and Device Physics - PHY1002
  • 08Digital Design - ECE2007
  • 09Innovative Projects Using Arduino – ECE2010
  • 01Mathematical Methods for CSE - MAT3005
  • 02Data Structures and Algorithms - CSE2001
  • 03Data Communications and Computer Networks - CSE3155
  • 04Computer Organization and Architecture - CSE2009
  • 05Discrete Mathematical Structures - MAT2004
  • 06Innovative Projects Using Raspberry Pi - ECE2011
  • 07Programming in Python - CSE1005
  • 08Introduction to Aptitude - PPS4002
  • 09Web Technologies - CSE2067
  • 01Numerical Methods for Engineers - MAT2003
  • 02Design and Analysis of Algorithms - CSE2007
  • 03Database Management Systems - CSE3156
  • 04Operating System with Linux Internals - CSE3120
  • 05Fundamentals of Data Analytics - CSE3190
  • 06Software Engineering - CSE2014
  • 07Data Handling and Visualization - CSE2026
  • 08Open Elective - I - XXXXXXX
  • 09Aptitude Training Intermediate - PPS4004
  • 10Mastering Object-Oriented Concepts in Python – CSE3216
  • 01Professional Elective-I - CSEXXXX
  • 02Artificial Intelligence and Machine Learning - CSE3157
  • 03Big Data Technologies - CSE3002
  • 04Professional Elective-II - CSEXXXX
  • 05No SQL Databases - CSE2024
  • 06Professional Elective-III - CSEXXXX
  • 07Logical and Critical Thinking - PPS4006
  • 08Data Structure and Web Development with Python - CSE3217
  • 09Open Elective - II - CSEXXXX
  • 01Big Data Security and Privacy - CSE3034
  • 02Web Intelligence and Analytics - CSE3031
  • 03Professional Elective-IV - CSEXXXX
  • 04Professional Elective-V - CSEXXXX
  • 05Open Elective – III (Management Basket) - XXXXXXX
  • 06Aptitude for Employability - PPS4005
  • 07Python Full-Stack Development - CSE3218
  • 08Professional Elective-VII - CSEXXXX
  • 09Cloud Computing - CSE3343
  • 01Professional Elective-VII - CSEXXXX
  • 02Professional Elective-VIII - CSEXXXX
  • 03Professional Elective-IX - CSEXXXX
  • 04Professional Elective-X - CSEXXXX
  • 05Streaming Data Analytics - CSE3032
  • 06Preparedness for Interview - PPS3018
  • 07Capstone Project - PIP2004
  • 01Internship - PIP4008

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:

PO 1: Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

PO 2: Problem analysis: Identify, formulate, research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

PO 3: 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.

PO 4: 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.

PO 5: Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.

PO 6: The engineer and society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues, and the consequent responsibilities relevant to professional engineering practice.

PO 7: Environment and sustainability: Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

PO 8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of engineering practice.

PO 9: Individual and teamwork: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO 10: 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.

PO 11: Project management and finance: Demonstrate knowledge 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.

PO 12: 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.

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Career Opportunities

  • Core Big Data Roles

    Big Data Engineer: Design, build, and maintain scalable data pipelines and infrastructure for large datasets using tools like Hadoop, Apache Spark, Kafka, and Hive.

    Data Analyst: Perform exploratory data analysis and generate actionable insights using tools like SQL, Tableau, Power BI, and Python.

    Data Scientist: Develop predictive models and apply machine learning techniques to extract insights from structured and unstructured data using tools like TensorFlow, R, and Scikit-learn.

    Database Administrator: Manage and optimize databases for storing and querying large datasets using tools like MySQL, PostgreSQL, and MongoDB.

  • Industry-Specific Roles

    Business Intelligence (BI) Analyst: Develop dashboards and reports to support business decision-making using tools like Tableau, Looker, and QlikView.

    Healthcare Data Analyst: Analyze patient data for improved diagnosis, treatment planning, and resource optimization.

    Retail Data Specialist: Work on customer segmentation, inventory optimization, and pricing strategies for e-commerce and retail companies.

    Financial Data Analyst: Focus on fraud detection, risk assessment, and financial forecasting using big data.

    Telecom Data Engineer: Manage and analyze massive data generated in telecommunications for optimizing networks and customer services.

  • Emerging and Specialized Roles

    IoT Data Analyst: Analyze data from IoT devices in smart homes, cities, and industrial applications using tools like MQTT and Apache Flink.

    Cloud Data Engineer: Manage cloud-based big data solutions for storage, processing, and analysis using platforms like AWS, Azure, and Google Cloud.

    AI/ML Specialist in Big Data: Apply AI and machine learning techniques to big data for predictive and prescriptive analytics, focusing on recommendation engines and customer sentiment analysis.

    Cybersecurity Analyst (Big Data Focus): Use big data tools to detect, prevent, and analyze cybersecurity threats with tools like Splunk and Elastic Stack.

    Geospatial Data Analyst: Analyze spatial data for urban planning, environmental monitoring, and logistics using tools like GIS and Google Earth Engine.

  • Academic and Research Opportunities

    Research Scientist in Big Data: Focus on innovative big data algorithms, optimization techniques, and distributed computing.

    Lecturer/Professor: Teach big data technologies and mentor students in academic institutions.

  • Entrepreneurship

    Big Data Startup Founder: Create solutions for industries like healthcare, finance, or e-commerce using big data technologies.

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