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B-TECH in Computer Science And Engineering Big Data at University of Petroleum and Energy Studies
Dehradun, Uttarakhand
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About the Specialization
What is Computer Science and Engineering (Big Data) at University of Petroleum and Energy Studies Dehradun?
This B.Tech Computer Science and Engineering (Big Data) program at UPES focuses on equipping students with advanced skills to manage, process, and analyze massive datasets. It addresses the growing demand for data professionals in the Indian market, differentiating itself through a strong emphasis on practical applications of big data technologies and real-world problem-solving, crucial for modern enterprises in India.
Who Should Apply?
This program is ideal for fresh graduates with a strong analytical bent and a keen interest in data-driven technologies. It also suits working professionals seeking to upskill in areas like Hadoop, Spark, and machine learning, or career changers looking to transition into the rapidly expanding big data industry in India. Prerequisites typically include a solid foundation in mathematics and programming for successful entry.
Why Choose This Course?
Graduates of this program can expect promising career paths as Big Data Engineers, Data Scientists, Analytics Consultants, or Machine Learning Engineers in India. Entry-level salaries range from INR 4-8 LPA, growing significantly with experience. The program aligns with industry certifications in cloud platforms and big data tools, enabling rapid career growth in leading Indian tech companies and global MNCs with presence in India.
Student Success Practices
Foundation Stage
Master Programming and Data Structures- (Semester 1-2)
Dedicate significant time in Semesters 1 and 2 to build strong foundations in C, Java, and Data Structures. Solve a variety of problems daily on coding platforms and understand algorithmic complexities thoroughly.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses
Career Connection
A solid coding foundation is paramount for technical interviews, competitive programming, and efficient problem-solving in any IT role, especially in data engineering and analytics.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Focus on Calculus, Linear Algebra, Probability, and Statistics. These form the bedrock for understanding machine learning algorithms and statistical analysis critical for Big Data. Participate in problem-solving groups and apply concepts.
Tools & Resources
Khan Academy, MIT OpenCourseWare, Relevant textbooks, Online practice problems
Career Connection
Crucial for understanding algorithmic principles, model interpretation, and advanced data analysis in big data and data science roles, giving an analytical edge.
Engage in Academic & Peer Learning- (Semester 1-2)
Actively participate in classroom discussions, form study groups, and clarify doubts regularly with faculty. Leverage peer learning to grasp complex concepts, share knowledge, and collectively solve challenging assignments.
Tools & Resources
College library resources, LMS platforms (Blackboard/Moodle), Dedicated study spaces and group collaboration tools
Career Connection
Enhances conceptual understanding, fosters critical thinking, and develops collaborative skills vital for team-based projects and effective communication in the industry.
Intermediate Stage
Gain Hands-on Experience with Big Data Tools- (Semester 3-5)
Beyond coursework, actively learn and implement projects using core Big Data technologies like Hadoop, Spark, Hive, Pig, and various NoSQL databases. Participate in university hackathons and dedicated mini-projects.
Tools & Resources
Apache Hadoop Ecosystem, Apache Spark, MongoDB, Cassandra, Google Colab/Jupyter Notebooks
Career Connection
Practical proficiency with these tools is a direct and essential requirement for Big Data Engineer, Data Analyst, and Data Architect roles in Indian companies.
Build a Portfolio of Data Projects- (Semester 4-6)
Start building a personal portfolio of projects applying Big Data concepts, machine learning, and visualization techniques. Use real-world datasets from platforms like Kaggle and showcase them on GitHub.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Tableau Public, Power BI
Career Connection
A strong project portfolio demonstrates practical skills, initiative, and problem-solving abilities, significantly enhancing employability for internships and full-time placements.
Seek Industry Internships & Workshops- (Semester 4-6)
Actively apply for summer and winter internships in companies focused on data analytics, Big Data platforms, or software development. Attend industry workshops and webinars to stay updated on emerging trends and network with professionals.
Tools & Resources
Internshala, LinkedIn Jobs, UPES Career Services Portal, Industry meetups
Career Connection
Provides invaluable real-world experience, industry contacts, and often leads to pre-placement offers, accelerating career entry and professional growth in your chosen domain.
Advanced Stage
Specialize in a Big Data Niche & Research- (Semester 6-8)
Identify a specific area within Big Data (e.g., Stream Processing, Cloud Big Data, NLP, or specific ML algorithms) and deep dive through advanced electives, certifications, and research papers. Work on your capstone project with a clear specialization focus.
Tools & Resources
Coursera/edX advanced courses, Research papers (IEEE, ACM), Specialized certifications (AWS, Azure, Google Cloud Data Engineer)
Career Connection
Distinguishes you as an expert, opening doors to specialized roles, higher salaries, and potential for R&D positions in leading tech firms and startups.
Refine Interview Skills & Placement Preparation- (Semester 7-8)
Intensify preparation for technical interviews, focusing on data structures, algorithms, SQL, Python, and Big Data concepts. Practice aptitude, reasoning, and communication skills. Participate in mock interviews with faculty or alumni.
Tools & Resources
InterviewBit, Glassdoor, UPES Placement Cell workshops, Mock interview platforms
Career Connection
Directly prepares you for the rigorous placement process in Indian companies, maximizing your chances of securing desirable job offers from top recruiters.
Network Professionally & Attend Conferences- (Semester 7-8)
Build a strong professional network through LinkedIn, alumni connections, and by attending industry conferences, seminars, and meetups (online or offline). Engage with thought leaders and recruiters to build your brand.
Tools & Resources
LinkedIn, Professional networking events, Industry conferences (e.g., Data Science Summit India, PyData)
Career Connection
Creates opportunities for referrals, mentorship, and staying informed about industry demands, crucial for long-term career advancement and leadership roles in the data ecosystem.
Program Structure and Curriculum
Eligibility:
- Minimum 50% marks in Class 10th and 12th. Minimum 50% in Physics, Chemistry, and Mathematics (PCM) in Class 12th.
Duration: 4 years / 8 semesters
Credits: 194 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCN1001 | Calculus and Linear Algebra | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors |
| DSCN1002 | Programming for Problem Solving | Core | 4 | C Programming Fundamentals, Variables and Data Types, Control Structures, Functions and Arrays, Pointers and Strings |
| DSCN1003 | Physics for Engineers | Core | 4 | Wave Optics, Quantum Mechanics, Semiconductor Physics, Electromagnetism, Laser Physics |
| DSCN1004 | Communication Skills | Skill | 3 | Presentation Skills, Technical Writing, Interpersonal Communication, Group Discussions, Resume Building |
| DSCN1005 | Engineering Graphics & Design | Core | 3 | Orthographic Projections, Isometric Views, Sectional Views, Computer Aided Design (CAD), Design Principles |
| DSCN1006 | Programming for Problem Solving Lab | Lab | 2 | C Programming Exercises, Control Flow Implementation, Array and Function Problems, Pointers and Structures Practice, Debugging Techniques |
| DSCN1007 | Physics for Engineers Lab | Lab | 1 | Optical Experiments, Semiconductor Device Characteristics, Magnetic Field Measurements, Wave Interference Experiments, Basic Electronics Circuits |
| UCSE1001 | Environmental Studies and Sustainability | Ability Enhancement | 2 | Ecosystems and Biodiversity, Environmental Pollution, Renewable Energy, Sustainable Development Goals, Environmental Policies in India |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCN1008 | Differential Equations and Complex Analysis | Core | 4 | First Order Differential Equations, Higher Order Linear ODEs, Partial Differential Equations, Complex Numbers and Functions, Complex Integration |
| DSCN1009 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Searching and Sorting Algorithms, Algorithm Analysis (Time & Space Complexity) |
| DSCN1010 | Digital Electronics | Core | 4 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memory Devices |
| DSCN1011 | Object Oriented Programming using Java | Core | 4 | Classes and Objects, Inheritance and Polymorphism, Encapsulation and Abstraction, Exception Handling, Collections Framework |
| DSCN1012 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms Practice, Sorting and Searching Practice |
| DSCN1013 | Digital Electronics Lab | Lab | 1 | Logic Gate Verification, Flip-Flop Implementations, Counters and Registers Design, Decoder/Encoder Circuits, Multiplexer/Demultiplexer Circuits |
| DSCN1014 | Object Oriented Programming using Java Lab | Lab | 2 | Java Class Design, Inheritance and Interface Examples, Polymorphism Practice, File I/O in Java, GUI Programming Basics |
| UCSE1002 | Indian Constitution and Human Rights | Ability Enhancement | 2 | Preamble and Basic Structure, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Government, Human Rights Conventions and Laws in India |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCN2001 | Database Management Systems | Core | 4 | ER Modeling, Relational Algebra, SQL Queries, Normalization, Transaction Management |
| DSCN2002 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Management, Concurrency and Deadlocks |
| DSCN2003 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Network Topologies, Data Link Layer Protocols, Routing Algorithms, Transport Layer Protocols |
| DSCN2004 | Probability and Statistics for Engineers | Core | 4 | Probability Distributions, Hypothesis Testing, Regression Analysis, Sampling Theory, Stochastic Processes |
| DSBD2005 | Introduction to Big Data Analytics | Specialization Core | 4 | Big Data Concepts, Hadoop Ecosystem Overview, NoSQL Databases, Data Ingestion and Processing, Big Data Challenges and Opportunities |
| DSCN2006 | Database Management Systems Lab | Lab | 2 | SQL Practice, Database Design, Normalization Implementation, PL/SQL Programming, Database Connectivity |
| DSCN2007 | Operating Systems Lab | Lab | 2 | Shell Scripting, Process Management Commands, CPU Scheduling Simulation, Memory Allocation Techniques, Synchronization Problems |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCN2008 | Design and Analysis of Algorithms | Core | 4 | Greedy Algorithms, Dynamic Programming, Divide and Conquer, Graph Algorithms, NP-Completeness |
| DSCN2009 | Software Engineering | Core | 4 | Software Development Life Cycle (SDLC), Requirement Engineering, Software Design Principles, Testing Strategies, Project Management |
| DSBD2010 | Distributed Systems | Specialization Core | 4 | Distributed Architectures, Client-Server Models, Concurrency Control, Distributed File Systems, Message Passing |
| DSBD2011 | Data Warehousing and Data Mining | Specialization Core | 4 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Clustering |
| DSBD2012 | Big Data Technologies Lab | Lab | 2 | Hadoop HDFS Operations, MapReduce Programming, Pig and HiveQL, Spark Basics, NoSQL Database Operations (e.g., MongoDB, Cassandra) |
| DSCN2013 | Software Engineering Lab | Lab | 2 | UML Diagramming Tools, Software Testing Tools, Version Control Systems (Git), Agile Methodology Practice, Documentation Tools |
| UCSE2001 | Essence of Indian Traditional Knowledge | Ability Enhancement | 2 | Vedas and Upanishads, Indian Philosophy Schools, Traditional Indian Sciences, Indian Art and Culture, Ethics in Ancient India |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSBD3001 | Machine Learning for Big Data | Specialization Core | 4 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Feature Engineering, Model Evaluation |
| DSBD3002 | Cloud Computing for Big Data | Specialization Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization, Big Data on Cloud Platforms (AWS, Azure, GCP), Cloud Security |
| DSBD3003 | Big Data Security and Privacy | Specialization Core | 4 | Data Governance, Privacy-Preserving Data Mining, Data Anonymization, Big Data Encryption, Compliance Regulations (GDPR, Indian IT Act) |
| DSE BDXXX | Program Elective - I | Elective | 3 | Advanced topics in specific Big Data areas (e.g., Stream Processing, Graph Analytics, IoT Analytics), Choice from a basket of advanced subjects |
| OEC XXX | Open Elective - I | Open Elective | 3 | Interdisciplinary topics from other departments (e.g., Management, Liberal Arts, Design), Broadening general knowledge and skills |
| DSBD3004 | Machine Learning for Big Data Lab | Lab | 2 | Implementing Scikit-learn algorithms, Data Preprocessing with Pandas, Model Training and Evaluation, Spark MLlib Exercises, Deep Learning Frameworks (TensorFlow/PyTorch) basics |
| DSBD3005 | Mini Project - I | Project | 2 | Problem Identification, Literature Survey, System Design, Implementation of a small Big Data solution, Report Writing and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSBD3006 | Big Data Visualization | Specialization Core | 4 | Principles of Data Visualization, Tools for Big Data Viz (Tableau, Power BI, D3.js), Dashboards and Reports, Interactive Visualizations, Geospatial Data Visualization |
| DSBD3007 | Big Data Streaming and Real-time Analytics | Specialization Core | 4 | Stream Processing Concepts, Apache Kafka, Apache Flink, Spark Streaming, Real-time Data Dashboards |
| DSBD3008 | Natural Language Processing for Big Data | Specialization Core | 4 | Text Preprocessing, Tokenization and Stemming, Sentiment Analysis, Topic Modeling, Named Entity Recognition |
| DSE BDXXX | Program Elective - II | Elective | 3 | Further specialized topics in Big Data, Advanced analytics techniques (e.g., Predictive Analytics, Deep Learning for Big Data) |
| OEC XXX | Open Elective - II | Open Elective | 3 | Enhancing skills in non-technical areas or exploring new domains, Subjects like Economics, Psychology, Foreign Language |
| DSBD3009 | Big Data Streaming and Visualization Lab | Lab | 2 | Kafka Producer/Consumer Implementation, Spark Streaming Applications, Dashboard Creation with Tableau/Power BI, Real-time Data Monitoring, Interactive Plotting Libraries (e.g., Plotly, Bokeh) |
| DSBD3010 | Mini Project - II / Industrial Project | Project | 2 | Complex Problem Solving, System Development with Big Data tools, Data Analysis and Interpretation, Report and Presentation, Industry-relevant problem solving |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSBD4001 | Capstone Project - Phase I | Project | 6 | Project Proposal Development, Detailed System Design, Technology Stack Selection, Initial Implementation and Proof of Concept, Interim Report and Presentation |
| DSE BDXXX | Program Elective - III | Elective | 3 | Niche areas like Blockchain for Big Data, Edge Computing, Quantum Computing basics, Advanced Statistical Modeling for Big Data |
| DSE BDXXX | Program Elective - IV | Elective | 3 | Further specialization in areas of interest, e.g., Recommender Systems, Time Series Analysis, IoT Data Analytics |
| OEC XXX | Open Elective - III | Open Elective | 3 | Entrepreneurship and Innovation, Business Analytics, Digital Marketing, Foreign Languages |
| UI XXX | Internship / Industrial Training | Internship | 8 | Real-world Industry Exposure, Application of Big Data Skills, Professional Networking, Problem-solving in a corporate environment, Internship Report and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSBD4002 | Capstone Project - Phase II | Project | 12 | Full System Development and Testing, Performance Optimization, Result Analysis and Validation, Comprehensive Project Report, Final Presentation and Viva Voce |
| DSE BDXXX | Program Elective - V | Elective | 3 | Advanced research topics in Big Data, Emerging trends and technologies, Specialized deep dive into a Big Data domain |
| DSE BDXXX | Program Elective - VI | Elective | 3 | Further broadening of Big Data knowledge or related fields, e.g., Data Ethics, Legal Aspects of Data, Business Intelligence |
