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M-TECH in Big Data Analytics at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Thiruvallur, Tamil Nadu
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About the Specialization
What is Big Data Analytics at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvallur?
This Big Data Analytics program at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology focuses on equipping students with advanced skills to manage, process, and derive insights from massive datasets. The curriculum is meticulously designed to meet the escalating demands of the Indian industry for professionals capable of tackling complex data challenges. It emphasizes a blend of theoretical foundations and practical applications across various big data technologies and analytical methodologies.
Who Should Apply?
This program is ideal for engineering graduates, particularly from Computer Science, IT, or related disciplines, seeking to specialize in a high-demand field. It caters to fresh graduates aspiring to kickstart careers as Data Scientists, Big Data Engineers, or Analytics Consultants. Working professionals looking to upskill or transition into data-centric roles within the Indian IT and allied sectors will also find immense value in its comprehensive and industry-relevant curriculum.
Why Choose This Course?
Graduates of this program can expect to secure roles in leading Indian and multinational companies with operations in India, such as TCS, Infosys, Wipro, Amazon, and various startups. Entry-level salaries typically range from INR 6-10 lakhs per annum, with significant growth potential as experience accrues. Career paths include Data Architect, Machine Learning Engineer, Business Intelligence Developer, and Big Data Consultant, contributing to India''''s burgeoning digital economy and data-driven initiatives.
Student Success Practices
Foundation Stage
Master Core Big Data Technologies- (Semester 1-2)
Dedicate significant time in Semesters 1-2 to thoroughly understand the Hadoop ecosystem, Spark, and core programming languages like Python/Java for big data. This foundation is crucial for all subsequent advanced topics and practical applications.
Tools & Resources
Hadoop documentation, Apache Spark guides, Databricks Community Edition, Udemy/Coursera courses on Big Data Fundamentals
Career Connection
A strong grasp of fundamental tools is non-negotiable for entry-level Big Data Engineer and Data Analyst roles, making you interview-ready for companies like TCS, Infosys, and startups in India.
Build a Data-driven Project Portfolio- (Semester 1-2)
Start working on small, independent data projects using public datasets from platforms like Kaggle. Focus on implementing data warehousing, data mining, and visualization techniques learned in class. Document your process and results clearly.
Tools & Resources
Kaggle, UCI Machine Learning Repository, GitHub, Tableau Public/Power BI Desktop
Career Connection
A tangible project portfolio demonstrates practical skills to Indian recruiters, showcasing your ability to apply theoretical knowledge and solve real-world problems, which is highly valued for internship and job opportunities.
Engage in Peer Learning and Technical Clubs- (Semester 1-2)
Actively participate in departmental technical clubs, workshops, and study groups. Collaborate with peers on problem-solving, discuss emerging technologies, and present your findings. This enhances understanding and communication skills.
Tools & Resources
College tech clubs (e.g., Data Science Club), Online forums (Stack Overflow), Meetup groups for data professionals in Chennai
Career Connection
Networking and collaborative skills gained are vital for teamwork in industry. Participation often leads to leadership roles, improving your resume for both academic achievements and soft skills required by companies.
Intermediate Stage
Specialize in Advanced ML/AI for Big Data- (Semester 3)
As you delve into Machine Learning and Reinforcement Learning, identify an area of interest (e.g., Deep Learning, NLP, Stream Analytics) and pursue advanced certifications or self-study. Apply these in practical mini-projects.
Tools & Resources
TensorFlow/Keras tutorials, PyTorch documentation, Google Cloud/AWS Machine Learning services, NPTEL courses for advanced topics
Career Connection
Specialized skills differentiate you in the competitive Indian job market for roles like Machine Learning Engineer or AI Specialist, fetching higher packages and opening doors to cutting-edge research and development positions.
Seek Industry Internships and Live Projects- (Semester 3)
Actively pursue internships at tech companies, analytics firms, or startups during summer breaks. Engage in live projects that tackle real business challenges, applying your big data and machine learning skills in an industry setting.
Tools & Resources
College placement cell, LinkedIn Jobs, Internshala, Company career pages
Career Connection
Internships are often a direct pathway to pre-placement offers (PPOs) in India. They provide invaluable industry experience, build a professional network, and make your profile attractive to top recruiters for permanent roles.
Contribute to Open Source and Kaggle Competitions- (Semester 3)
Participate in Kaggle data science competitions or contribute to open-source projects related to big data or machine learning. This hones your problem-solving abilities, exposes you to diverse data challenges, and enhances your technical visibility.
Tools & Resources
Kaggle platform, GitHub (for open source contributions), Community discussions on data science forums
Career Connection
Success in competitions and open-source contributions are highly regarded by hiring managers, demonstrating your proactive learning, coding proficiency, and ability to work on complex problems, crucial for top tech firms.
Advanced Stage
Focus on a Capstone Project with Business Impact- (Semester 4)
Your Project Work (Phase-II) should be a comprehensive, industry-relevant solution demonstrating significant business impact. Choose a problem that leverages multiple aspects of big data analytics and showcases your specialized skills.
Tools & Resources
Industry mentors (if available), Advanced analytics tools (e.g., Apache Flink, AWS SageMaker), Research papers and technical journals
Career Connection
A strong capstone project is your biggest asset for placements, acting as a powerful talking point in interviews. It proves your ability to deliver end-to-end data solutions, making you highly desirable for Lead Data Scientist or Architect roles.
Ace Placement Preparation and Interview Skills- (Semester 4)
Intensively prepare for campus placements, focusing on data structures, algorithms, SQL, advanced Python for data science, and machine learning concepts. Practice aptitude tests, group discussions, and mock interviews to refine your communication and problem-solving under pressure.
Tools & Resources
GeeksforGeeks, LeetCode, HackerRank, PrepInsta (for Indian aptitude tests), Vel Tech Placement Cell resources
Career Connection
Systematic preparation directly translates to successful placement in top companies. Mastering interview skills ensures you can articulate your technical knowledge and project experience effectively, securing your desired role and compensation.
Explore Entrepreneurship and Advanced Research- (Semester 4)
For those inclined towards innovation, use the insights gained to develop a startup idea utilizing big data, or pursue research leading to publications. Vel Tech''''s innovation cell can provide mentorship and support.
Tools & Resources
Vel Tech Incubation Center, Startup India resources, Research publication platforms (arXiv, IEEE Xplore)
Career Connection
This path is for aspiring entrepreneurs or academicians. It fosters innovative thinking, problem-solving beyond conventional boundaries, and can lead to impactful startups or a career in R&D, contributing to India''''s tech ecosystem.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science and Engineering / Information Technology / Computer and Communication Engineering / Software Engineering / Electronics and Communication Engineering / Electrical and Electronics Engineering / Electronics and Instrumentation Engineering / Instrumentation and Control Engineering or equivalent B.E./B.Tech. degree with a minimum of 50% aggregate marks (45% for reserved category) or an equivalent grade. Candidates with AMIE / B.Sc. (3 years) + M.Sc. (2 years) in relevant fields (Mathematics / Physics / Computer Science) are also eligible.
Duration: 2 years (4 semesters)
Credits: 68 Credits
Assessment: Internal: 50% (for theory), 75% (for lab), 50% (for project), External: 50% (for theory), 25% (for lab), 50% (for project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MACR1101 | Advanced Mathematics for Data Science | Core | 3 | Linear Algebra, Probability and Statistics, Optimization Techniques, Transform Methods, Numerical Methods |
| BDAR1101 | Data Warehousing and Data Mining | Core | 3 | Data Warehouse Architecture, OLAP, Data Preprocessing, Association Rule Mining, Classification and Prediction, Clustering Techniques |
| BDAR1102 | Big Data Technologies | Core | 3 | Hadoop Ecosystem, HDFS, MapReduce, YARN, Spark, Hive, Pig |
| BDAR1103 | Data Visualization Techniques | Core | 3 | Data Visualization Principles, Graphical Representation, Interactive Visualization, Data Storytelling, Visualization Tools (Tableau/Power BI) |
| BDAR1104 | Big Data Technologies Lab | Lab | 2 | Hadoop Installation, HDFS Operations, MapReduce Programming, Spark Implementation, Hive Queries, Pig Scripting |
| BDAR1105 | Data Visualization Lab | Lab | 2 | Tableau/Power BI Basics, Chart Types, Dashboard Creation, Data Cleaning for Visualization, Interactive Reporting |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDAR1201 | Advanced Database Technologies | Core | 3 | NoSQL Databases (MongoDB, Cassandra), Graph Databases, Columnar Databases, Distributed Databases, Data Models |
| BDAR1202 | Machine Learning for Big Data | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Ensemble Methods, Deep Learning Basics, Model Evaluation |
| BDAR1203 | Cloud Computing for Big Data | Core | 3 | Cloud Models (IaaS, PaaS, SaaS), Virtualization, Cloud Storage, Big Data on Cloud (AWS, Azure, GCP), Serverless Computing |
| BDAR1204 | Natural Language Processing | Core | 3 | Text Preprocessing, N-grams, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, Text Classification |
| BDAR1205 | Machine Learning Lab | Lab | 2 | Python for ML, Scikit-learn, TensorFlow/Keras, Regression, Classification, Clustering Algorithms |
| BDAR1206 | Advanced Database Technologies Lab | Lab | 2 | NoSQL Database Operations, MongoDB Queries, Cassandra Operations, Graph Database Implementation |
| Elective-I | Professional Elective I | Elective | 3 | Students choose from the pool of Professional Electives |
| Elective-II | Professional Elective II | Elective | 3 | Students choose from the pool of Professional Electives |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDAR2101 | Advanced Analytics and Optimization | Core | 3 | Prescriptive Analytics, Optimization Models, Linear Programming, Simulation, Heuristic Algorithms, Multi-objective Optimization |
| BDAR2102 | Reinforcement Learning | Core | 3 | Markov Decision Process, Bellman Equations, Q-learning, SARSA, Policy Gradient Methods, Deep Reinforcement Learning |
| BDAR2103 | Big Data Security and Privacy | Core | 3 | Data Privacy Issues, Security Challenges in Big Data, Anonymization Techniques, Data Governance, GDPR Compliance |
| Elective-III | Professional Elective III | Elective | 3 | Students choose from the pool of Professional Electives |
| Elective-IV | Professional Elective IV | Elective | 3 | Students choose from the pool of Professional Electives |
| BDAR2104 | Minor Project | Project | 6 | Problem Definition, Literature Survey, System Design, Implementation, Testing and Evaluation, Report Writing |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDAR2201 | Project Work (Phase-II) | Project | 18 | Advanced Problem Solving, Prototype Development, Comprehensive Testing, Performance Evaluation, Technical Report, Oral Defense |
Semester electives
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BDAR1901 | Social Media Analytics | Elective | 3 | Social Network Analysis, Data Collection from Social Media, Sentiment Analysis, Community Detection, Influence Maximization |
| BDAR1902 | Deep Learning | Elective | 3 | Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders, Generative Adversarial Networks (GANs) |
| BDAR1903 | Stream Analytics | Elective | 3 | Stream Processing Concepts, Spark Streaming, Kafka, Flink, Real-time Data Ingestion, Stream Join Operations |
| BDAR1904 | Business Intelligence | Elective | 3 | BI Architecture, Data Marts, ETL Processes, Reporting, Dashboards, Data-driven Decision Making |
| BDAR1905 | Ethical Hacking and Digital Forensics | Elective | 3 | Ethical Hacking Concepts, Penetration Testing, Digital Forensics Process, Incident Response, Cyber Laws |
| BDAR1906 | Information Retrieval | Elective | 3 | Text Indexing, Boolean Models, Vector Space Models, Ranking Algorithms, Web Search, Evaluation Metrics |
| BDAR1907 | Block Chain Technology | Elective | 3 | Cryptography, Distributed Ledger, Consensus Mechanisms, Smart Contracts, Ethereum, Hyperledger |
| BDAR1908 | Internet of Things | Elective | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols, IoT Platforms, Data Analytics for IoT, Edge Computing |
| BDAR1909 | Cyber Physical Systems | Elective | 3 | CPS Architecture, Sensors, Actuators, Embedded Systems, Real-time Systems, Data Fusion, Smart Grids |
| BDAR1910 | Digital Image Processing | Elective | 3 | Image Enhancement, Image Restoration, Image Segmentation, Feature Extraction, Object Recognition, Morphological Processing |
| BDAR1911 | Human Computer Interaction | Elective | 3 | HCI Principles, Usability Engineering, User-Centered Design, Interaction Styles, Evaluation Techniques, UX Research |
| BDAR1912 | Computer Vision | Elective | 3 | Image Features, Object Detection, Image Segmentation, Motion Analysis, 3D Vision, Deep Learning for Vision |
| BDAR1913 | Robotics | Elective | 3 | Robot Kinematics, Dynamics, Path Planning, Robot Control, Sensors for Robotics, Vision-based Robotics |
| BDAR1914 | Quantum Computing | Elective | 3 | Quantum Mechanics Basics, Qubits, Quantum Gates, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography |
| BDAR1915 | Industrial Automation | Elective | 3 | Automation Principles, PLC, SCADA, DCS, Robotics in Industry, Smart Manufacturing |
| BDAR1916 | Multi-core Architectures | Elective | 3 | Parallel Computing, Multi-core Processors, Shared Memory, Message Passing, Threading, Performance Optimization |
| BDAR1917 | Cryptography and Network Security | Elective | 3 | Symmetric/Asymmetric Ciphers, Hash Functions, Digital Signatures, Firewalls, VPN, IDS/IPS |
| BDAR1918 | Wireless Adhoc Networks | Elective | 3 | Adhoc Network Routing, MAC Protocols, Quality of Service, Energy Efficiency, Security in Adhoc Networks |
| BDAR1919 | Optimization Techniques | Elective | 3 | Linear Programming, Non-linear Programming, Dynamic Programming, Metaheuristics, Evolutionary Algorithms |
| BDAR1920 | Cognitive Computing | Elective | 3 | AI Fundamentals, Machine Learning, Natural Language Processing, Computer Vision, Knowledge Representation, Problem Solving |
| BDAR1921 | High Performance Computing | Elective | 3 | Parallel Architectures, Cluster Computing, Grid Computing, GPU Computing, Message Passing Interface (MPI) |
| BDAR1922 | Mobile Computing | Elective | 3 | Wireless Technologies, Mobile OS, Mobile Application Development, Location-based Services, Mobile Security |
| BDAR1923 | Pattern Recognition | Elective | 3 | Feature Extraction, Classification Algorithms, Clustering, Dimensionality Reduction, Image Pattern Recognition |
| BDAR1924 | Web Data Mining | Elective | 3 | Web Crawling, Web Content Mining, Web Structure Mining, Web Usage Mining, Link Analysis |
| BDAR1925 | Health Care Analytics | Elective | 3 | Healthcare Data, Electronic Health Records, Predictive Analytics in Healthcare, Medical Image Analysis, Public Health Informatics |
| BDAR1926 | Financial Analytics | Elective | 3 | Financial Data Sources, Time Series Analysis, Risk Analytics, Algorithmic Trading, Predictive Modeling in Finance |
| BDAR1927 | Agri Informatics | Elective | 3 | Agricultural Data Collection, Crop Monitoring, Yield Prediction, Precision Agriculture, Remote Sensing in Agriculture |
| BDAR1928 | Geo Informatics | Elective | 3 | GIS, Remote Sensing, GPS, Spatial Data Models, Geospatial Analysis, Satellite Imagery |
