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M-SC in Computer Science Big Data Analytics at Central University of Rajasthan
Ajmer, Rajasthan
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About the Specialization
What is Computer Science (Big Data Analytics) at Central University of Rajasthan Ajmer?
This Computer Science (Big Data Analytics) program at Central University of Rajasthan focuses on equipping students with advanced skills in handling, processing, and analyzing massive datasets. It addresses the growing demand for data professionals in India''''s rapidly expanding digital economy, integrating core CS principles with specialized knowledge in data analytics, machine learning, and big data technologies.
Who Should Apply?
This program is ideal for fresh graduates with a background in Computer Science, IT, Mathematics, or related fields, seeking entry into data-driven roles. It also suits working professionals aiming to upskill in analytics or career changers transitioning into the booming Indian data industry, provided they meet the academic prerequisites.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Big Data Engineers, Machine Learning Engineers, and Business Intelligence Analysts in India. Entry-level salaries typically range from INR 5-8 lakhs annually, with significant growth potential up to INR 15-20 lakhs for experienced professionals in top Indian companies and MNCs.
Student Success Practices
Foundation Stage
Strengthen Core Programming and Data Structures- (Semester 1-2)
Dedicate time to master advanced data structures and object-oriented programming in C++. Regularly practice coding problems on platforms to solidify algorithmic thinking, crucial for efficient data processing.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef
Career Connection
A strong foundation in these areas is non-negotiable for technical interviews and is the bedrock for building scalable data solutions.
Build a Robust DBMS Foundation- (Semester 1-2)
Thoroughly understand relational database concepts and master SQL. Practice complex queries and database design, as data is primarily stored and retrieved from such systems before analytics.
Tools & Resources
MySQL Workbench, PostgreSQL, LeetCode SQL
Career Connection
Proficiency in databases is essential for any data-related role, enabling efficient data extraction and management for analysis.
Engage in Minor Projects and Peer Learning- (Semester 1-2)
Actively participate in minor projects (like CS-206) to apply theoretical knowledge. Collaborate with peers on coding challenges and group assignments to enhance problem-solving and teamwork skills.
Tools & Resources
GitHub, Jira (for project management), University computing labs
Career Connection
Practical project experience and collaborative skills are highly valued by recruiters, demonstrating applied knowledge and interpersonal effectiveness.
Intermediate Stage
Specialize in Data Analytics & Big Data Technologies- (Semester 3)
Choose electives like Data Analytics, Big Data Technologies, and Machine Learning. Deep dive into practical applications using Python and relevant libraries (Numpy, Pandas, Scikit-learn).
Tools & Resources
Jupyter Notebook, Anaconda, Apache Hadoop, Apache Spark
Career Connection
This specialization directly prepares you for roles in Data Science and Big Data Engineering, making you a competitive candidate in the Indian job market.
Contribute to Open-Source Projects & Kaggle Competitions- (Semester 3)
Seek opportunities to contribute to open-source data science projects on GitHub or participate in data challenges on Kaggle. This provides real-world experience with diverse datasets and problems.
Tools & Resources
Kaggle, GitHub, Medium (for learning and sharing)
Career Connection
Showcasing contributions and competition successes on your resume significantly boosts your profile, demonstrating initiative and practical expertise.
Network with Industry Professionals- (Semester 3)
Attend industry workshops, webinars, and conferences related to Big Data and AI. Connect with professionals on platforms like LinkedIn to gain insights into industry trends and potential opportunities.
Tools & Resources
LinkedIn, Meetup events (local tech communities), Industry conferences
Career Connection
Networking can open doors to internships, mentorship, and full-time positions, providing invaluable career guidance and visibility.
Advanced Stage
Execute an Industry-Relevant Major Project- (Semester 4)
For your Major Project (CS-401), choose a topic with direct industry applicability in Big Data Analytics. Aim to solve a real-world problem using learned technologies and present a robust solution.
Tools & Resources
Cloud platforms (AWS, Azure, GCP), Big Data frameworks (Hadoop, Spark), Visualization tools (Tableau, PowerBI)
Career Connection
A strong, well-documented major project is often a key talking point in interviews, demonstrating your ability to deliver a complete solution.
Intensive Placement Preparation- (Semester 4)
Focus heavily on technical interview preparation, including coding, data structures, algorithms, and core concepts of Big Data, Machine Learning, and SQL. Practice aptitude tests and soft skills for group discussions.
Tools & Resources
InterviewBit, GeeksforGeeks placement section, Mock interview platforms
Career Connection
Thorough preparation is critical to convert placement opportunities into successful job offers with leading Indian tech companies and startups.
Develop a Professional Portfolio- (Semester 4)
Curate all your projects, coding exercises, Kaggle solutions, and open-source contributions into a professional online portfolio or GitHub repository. Include clear descriptions and demonstrate your thought process.
Tools & Resources
GitHub Pages, Personal website builders (e.g., WordPress), LinkedIn profile
Career Connection
A well-maintained portfolio acts as a live resume, allowing potential employers to see your capabilities and technical skills firsthand, greatly enhancing your hireability.
Program Structure and Curriculum
Eligibility:
- B.Sc. in Computer Science/IT/Mathematics/Statistics/Electronics/Physics or BCA/B.Tech./BE in Computer Science/IT or equivalent with at least 55% marks (or equivalent grade) from a recognized university.
Duration: 4 semesters / 2 years
Credits: 80 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-101 | Advanced Data Structures | Core | 4 | Data Structures Fundamentals, Trees and Heaps, Graphs and Algorithms, Sorting and Searching Techniques, Hashing and Collision Resolution |
| CS-102 | Object-Oriented Programming with C++ | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Virtual Functions, Exception Handling |
| CS-103 | Operating Systems | Core | 4 | OS Structures and Services, Process Management, Memory Management, File System Interface, Deadlocks and Protection |
| CS-104 | Computer Networks | Core | 4 | Network Models (OSI/TCP-IP), Data Link Layer, Network Layer, Transport Layer Protocols, Application Layer Services |
| CS-105 | Advanced Data Structures Lab | Lab | 2 | Implementation of Stacks and Queues, Tree and Graph Traversals, Sorting and Searching Algorithms, Hashing Techniques, Algorithm Analysis using C++ |
| CS-106 | Object-Oriented Programming Lab | Lab | 2 | C++ Program Development, Class and Object Implementation, Inheritance and Polymorphism Exercises, File I/O Operations, Exception Handling Practices |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-201 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis Techniques, Divide and Conquer, Greedy Algorithms, Dynamic Programming, NP-Completeness |
| CS-202 | Database Management Systems | Core | 4 | DBMS Architecture, ER Model, Relational Algebra, SQL Queries, Normalization and Transaction Control |
| CS-203 | Artificial Intelligence | Core | 4 | Introduction to AI, Intelligent Agents, Search Strategies, Knowledge Representation, Machine Learning Basics |
| CS-204(A) | Machine Learning | Elective I (Big Data Analytics Relevant) | 4 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Neural Networks Basics, Model Evaluation and Selection |
| CS-204(B) | Soft Computing | Elective I | 4 | Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, Swarm Intelligence, Hybrid Systems |
| CS-204(C) | Compiler Design | Elective I | 4 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| CS-205 | Database Management Systems Lab | Lab | 2 | SQL Command Implementation, Database Schema Design, PL/SQL Programming, Triggers and Stored Procedures, Report Generation from Databases |
| CS-206 | Minor Project | Project | 2 | Problem Identification, Software Design Principles, Implementation and Testing, Technical Report Writing, Project Presentation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-301 | Data Warehousing and Data Mining | Core (Big Data Analytics Relevant) | 4 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing, Association Rule Mining, Classification and Clustering Techniques |
| CS-302 | Python Programming | Core | 4 | Python Fundamentals, Data Structures in Python, Functions and Modules, Object-Oriented Programming in Python, Introduction to Libraries (Numpy, Pandas) |
| CS-303(A) | Cryptography & Network Security | Elective II | 4 | Classical Cryptography, Symmetric Key Ciphers, Asymmetric Key Cryptography, Hash Functions and Digital Signatures, Firewalls and Intrusion Detection |
| CS-303(B) | Distributed Systems | Elective II | 4 | Distributed System Architectures, Inter-process Communication, Distributed File Systems, Consistency and Replication, Fault Tolerance Mechanisms |
| CS-303(C) | Mobile Computing | Elective II | 4 | Mobile Operating Systems, Wireless Communication Technologies, Mobile Application Development, Security in Mobile Computing, Location-Based Services |
| CS-304(A) | Data Analytics | Elective III (Big Data Analytics Core) | 4 | Data Exploration and Preprocessing, Statistical Analysis for Data Analytics, Data Visualization Techniques, Predictive Modeling, Introduction to Big Data Analytics Tools |
| CS-304(B) | Deep Learning | Elective III (Big Data Analytics Relevant) | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Deep Learning Frameworks (TensorFlow, PyTorch) |
| CS-304(C) | Cloud Computing | Elective III | 4 | Cloud Architecture and Deployment Models, Service Models (IaaS, PaaS, SaaS), Virtualization Technology, Cloud Security and Privacy, Cloud Resource Management |
| CS-304(D) | Internet of Things (IoT) | Elective III | 4 | IoT Architecture and Protocols, Sensors, Actuators, and Devices, IoT Communication Technologies, IoT Platforms and Ecosystems, Data Analytics for IoT |
| CS-305(A) | Big Data Technologies | Elective IV (Big Data Analytics Core) | 4 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Hive and Pig for Data Processing, Spark for Real-time Analytics, NoSQL Databases (HBase, Cassandra) |
| CS-305(B) | Natural Language Processing | Elective IV | 4 | Text Preprocessing, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Fundamentals |
| CS-305(C) | Block Chain Technology | Elective IV | 4 | Cryptographic Primitives, Blockchain Architecture, Consensus Mechanisms, Smart Contracts, Decentralized Applications (DApps) |
| CS-305(D) | High Performance Computing | Elective IV | 4 | Parallel Computing Concepts, Distributed Memory Systems, Shared Memory Systems, Performance Metrics, HPC Application Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS-401 | Major Project | Project | 10 | Research Problem Formulation, System Design and Architecture, Implementation and Testing, Technical Documentation, Project Defense and Presentation |
| CS-402(A) | Information Security | Elective V | 4 | Security Attacks and Services, Cryptographic Techniques, Access Control Mechanisms, Network Security Protocols, Web Security |
| CS-402(B) | Soft Computing | Elective V | 4 | Fuzzy Logic Systems, Artificial Neural Networks, Evolutionary Computation, Hybrid Intelligent Systems, Application of Soft Computing |
| CS-402(C) | Cyber Security | Elective V | 4 | Cyber Threats and Vulnerabilities, Attack Vectors and Exploits, Digital Forensics, Incident Response, Security Policies and Compliance |
| CS-402(D) | Distributed Systems | Elective V | 4 | Distributed Architectures, Middleware Technologies, Distributed Shared Memory, Fault Tolerance, Security in Distributed Systems |
