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M-SC in General at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Tiruvallur, Tamil Nadu
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About the Specialization
What is General at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tiruvallur?
This M.Sc. Data Science program at Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology focuses on equipping students with advanced analytical skills and knowledge to navigate the complex world of data. The curriculum is meticulously designed to meet the growing demand for skilled data scientists in the Indian industry, emphasizing practical application and theoretical foundations across various data-intensive domains. It covers core areas like machine learning, big data, deep learning, and advanced statistical methods, fostering expertise in data-driven decision making.
Who Should Apply?
This program is ideal for science or engineering graduates (B.Sc., BCA, B.E., B.Tech) with a minimum of 50% marks, seeking entry into the thriving data science field. It specifically caters to fresh graduates aspiring to become data analysts, machine learning engineers, or data scientists in Indian IT, analytics, and R&D sectors. Additionally, it targets working professionals looking to upskill or transition into data-centric roles within various Indian and global companies, leveraging their existing technical backgrounds.
Why Choose This Course?
Graduates of this program can expect to secure lucrative roles such as Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, or Big Data Engineer in India''''s booming IT, finance, healthcare, and e-commerce sectors. Entry-level salaries typically range from INR 5-8 LPA, with experienced professionals commanding INR 12-25+ LPA, reflecting the high demand for specialized skills. The program also aligns with globally recognized professional certifications, fostering strong growth trajectories in leading Indian companies and startups.
Student Success Practices
Foundation Stage
Strengthen Core Programming and Math Skills- (Semester 1-2)
Dedicate early semesters to mastering Python and R programming, alongside fundamental concepts in probability, statistics, data structures, and algorithms. Regularly practice coding problems on platforms to build a strong analytical foundation required for advanced topics.
Tools & Resources
HackerRank, LeetCode, Kaggle (basic datasets), GeeksforGeeks, NPTEL courses on Probability and Statistics
Career Connection
A solid foundation is critical for clearing technical rounds in placements and for effectively understanding and implementing advanced machine learning and deep learning concepts later on.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups to discuss complex theoretical concepts, share coding insights, and collaborate on small academic projects. Active participation in group work enhances understanding, develops communication, and builds teamwork skills essential for professional environments.
Tools & Resources
GitHub for collaboration, Discord/WhatsApp study groups, University library resources and academic forums
Career Connection
Improves problem-solving, interpersonal communication, and collaboration skills, which are highly valued by employers in team-oriented data science roles.
Explore Data Science Beyond the Curriculum- (Semester 1-2)
Supplement classroom learning by exploring introductory online courses or tutorials on platforms like Coursera or edX related to data visualization, SQL, or specific machine learning algorithms. Participate in introductory data challenges to gain practical exposure.
Tools & Resources
Coursera (Data Science Specializations), edX, Udemy (beginner courses), Kaggle ''''Getting Started'''' competitions, freeCodeCamp
Career Connection
Demonstrates proactive learning, curiosity, and passion for the field, making your profile stand out during internship and job applications by showcasing self-driven initiative.
Intermediate Stage
Build a Portfolio with Practical Projects- (Semester 3)
Apply learned concepts from Machine Learning, Big Data, and Deep Learning to build practical, end-to-end data science projects. Focus on solving real-world problems, thoroughly documenting your process, and showcasing your work on platforms like GitHub and a personal website.
Tools & Resources
GitHub, Jupyter Notebooks, Google Colab, Tableau Public, Streamlit for app deployment
Career Connection
A strong project portfolio is crucial for technical interviews, demonstrating your ability to apply theoretical knowledge and work independently on data science challenges.
Seek Internships and Industry Exposure- (Semester 3)
Actively look for internships in data science, machine learning engineering, or data analytics roles during summer breaks or dedicated internship periods. Gain hands-on experience, understand industry workflows, and build a valuable professional network. Participate in company workshops and hackathons.
Tools & Resources
LinkedIn Jobs, Internshala, Naukri.com, College Placement Cell
Career Connection
Internships provide invaluable practical experience, often leading to pre-placement offers and significantly boosting employability upon graduation by providing real-world context.
Specialize and Certify in Niche Areas- (Semester 3)
Based on the professional electives chosen (e.g., NLP, Reinforcement Learning, Generative AI), pursue relevant industry certifications from platforms like AWS, Google Cloud, or independent bodies to validate specialized skills and deepen expertise.
Tools & Resources
AWS Certified Machine Learning – Specialty, Google Cloud Professional Data Engineer, Databricks certifications, Microsoft Certified: Azure Data Scientist Associate
Career Connection
Niche specializations and industry-recognized certifications open doors to advanced roles and higher salary packages in specific, high-demand domains within the data science industry.
Advanced Stage
Master Advanced Data Science Tools and Platforms- (Semester 4)
Develop proficiency in advanced tools for MLOps, model deployment, and specific cloud services relevant to production-grade data science (e.g., Docker, Kubernetes, AWS SageMaker, Google Cloud Vertex AI). Focus on building scalable solutions and deploying machine learning models effectively.
Tools & Resources
Docker, Kubernetes, AWS SageMaker, Google Cloud Vertex AI, MLflow for experiment tracking
Career Connection
Proficiency in these tools makes you highly job-ready for roles involving deployment, maintenance, and scaling of data science solutions in large enterprises and tech companies.
Intensive Placement Preparation and Mock Interviews- (Semester 4)
Engage in rigorous placement preparation, including frequent mock interviews (technical, HR, case studies), aptitude tests, and resume building workshops. Practice presenting your final project and explaining complex technical concepts clearly and concisely.
Tools & Resources
InterviewBit, GeeksforGeeks Interview Corner, Pramp (peer mock interviews), University Career Services and Alumni Network
Career Connection
Maximizes chances of securing top placements with leading companies by ensuring you are confident, articulate, and well-prepared for all stages of the interview process.
Contribute to Open Source or Research Publications- (Semester 4)
Aim for high-quality output in your final project, potentially contributing to an open-source project or publishing research findings in relevant conferences or journals. This showcases deep expertise, innovation, and a commitment to advancing the field.
Tools & Resources
arXiv, IEEE Xplore (for research papers), GitHub for open-source contributions, Academic conferences and workshops
Career Connection
Distinguishes your profile for research-oriented roles, PhD opportunities, or senior data scientist positions that require thought leadership and a strong academic or technical contribution record.
Program Structure and Curriculum
Eligibility:
- A Pass in any recognized Bachelor’s Degree in Science (10+2+3 or 4 year pattern) / BCA / B.E / B.Tech from any recognized University with minimum 50% of marks.
Duration: 4 semesters / 2 years
Credits: 92 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT-DS23101 | Applied Probability and Statistics | Core | 4 | Probability Theory and Distributions, Random Variables and Expectations, Statistical Inference and Estimation, Hypothesis Testing Techniques, Correlation and Regression Analysis, Design of Experiments |
| CSC-DS23101 | Python Programming | Core | 4 | Python Basics and Data Types, Control Structures and Functions, Object-Oriented Programming in Python, File Handling and Exception Handling, NumPy for Numerical Operations, Pandas for Data Manipulation |
| CSC-DS23102 | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks, Queues, Trees and Binary Search Trees, Graphs and Graph Traversal Algorithms, Sorting and Searching Techniques, Hashing and Collision Resolution, Algorithm Analysis and Complexity |
| CSC-DS23103 | Database Management Systems | Core | 4 | Data Models and Database Architecture, Relational Algebra and SQL, Database Design and Normalization, Transaction Management and Concurrency Control, Database Security and Recovery, Introduction to NoSQL Databases |
| CSC-DS23104 | Data Science with R | Core | 3 | R Programming Fundamentals and Environment Setup, Data Import, Export, and Manipulation, Data Visualization with ggplot2, Descriptive and Inferential Statistics in R, Linear and Logistic Regression in R, Introduction to Machine Learning with R |
| HSM-DS23101 | Research Methodology and IPR | Core | 3 | Research Design and Problem Formulation, Data Collection Methods and Sampling, Statistical Analysis in Research, Scientific Report Writing and Presentation, Intellectual Property Rights Overview, Patents, Copyrights, and Trademarks |
| CSC-DS23105 | Python Programming Lab | Lab | 2 | Hands-on Python Programming Exercises, Implementation of Data Structures, Functions and Module Creation, Object-Oriented Programming Applications, Basic Data Analysis using NumPy, Data Manipulation with Pandas |
| CSC-DS23106 | Data Science with R Lab | Lab | 2 | R Programming Practice and Scripting, Data Preprocessing and Cleaning in R, Creating Advanced Visualizations, Performing Statistical Tests, Building Basic Predictive Models, Generating Data Reports |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSC-DS23201 | Advanced Data Structures and Algorithms | Core | 4 | Balanced Trees (AVL, Red-Black), B-Trees and Tries, Graph Algorithms (MST, Shortest Path), Dynamic Programming Principles, Greedy Algorithms, Network Flow Algorithms |
| CSC-DS23202 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Dimensionality Reduction), Model Evaluation and Validation, Ensemble Methods (Bagging, Boosting), Support Vector Machines and Decision Trees, Bias-Variance Trade-off |
| CSC-DS23203 | Big Data Analytics | Core | 4 | Introduction to Big Data Ecosystem, Hadoop Distributed File System (HDFS), MapReduce Programming Model, Apache Spark for Big Data Processing, NoSQL Databases (Cassandra, MongoDB), Data Stream Processing |
| CSC-DS23204 | Cloud Computing for Data Science | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization and Containerization, Introduction to AWS, Azure, GCP, Cloud Storage and Database Services, Serverless Computing Architectures, Data Security and Privacy in Cloud |
| CSC-DS23205 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Frameworks (TensorFlow, Keras, PyTorch), Image Recognition and Object Detection, Sequence Modeling for Natural Language Processing |
| CSC-DS23206 | Machine Learning Lab | Lab | 2 | Implementation of Supervised Learning Algorithms, Implementation of Unsupervised Learning Algorithms, Feature Engineering and Selection, Model Training and Performance Evaluation, Hyperparameter Tuning Techniques, Using Scikit-learn for ML tasks |
| CSC-DS23207 | Deep Learning Lab | Lab | 2 | Building Feedforward Neural Networks, Implementing CNNs for Image Processing, Implementing RNNs for Text Data, Using TensorFlow/Keras for Model Development, Data Augmentation and Transfer Learning, Model Deployment Basics |
| CSC-DS23E01 | Optimization Techniques | Professional Elective | 3 | Linear Programming and Simplex Method, Non-linear Programming Methods, Integer Programming, Heuristic and Meta-Heuristic Algorithms, Dynamic Programming Applications, Queueing Theory Fundamentals |
| CSC-DS23E02 | Data Visualization Techniques | Professional Elective | 3 | Principles of Effective Data Visualization, Interactive Visualizations with D3.js, Data Storytelling and Dashboard Design, Tools: Tableau and Power BI, Exploratory Data Analysis using Visuals, Geospatial Data Visualization |
| CSC-DS23E03 | Natural Language Processing | Professional Elective | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Recurrent Neural Networks for NLP, Transformer Models (BERT, GPT), Sentiment Analysis and Text Classification, Named Entity Recognition and Information Extraction |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSC-DS23301 | Data Mining and Data Warehousing | Core | 4 | Data Preprocessing and Data Quality, Data Warehousing Concepts (OLAP, OLTP), Association Rule Mining Algorithms, Classification and Prediction Techniques, Clustering Algorithms (K-Means, DBSCAN), Web Mining and Text Mining |
| CSC-DS23302 | Data Governance and Ethics | Core | 3 | Data Privacy Regulations (GDPR, DPDP Act), Data Security and Compliance Frameworks, Ethical Considerations in AI and Data, Data Quality Management and Stewardship, Bias and Fairness in Algorithmic Systems, Data Ownership and Accountability |
| CSC-DS23E04 | Social Network Analysis | Professional Elective | 3 | Network Structure and Properties, Centrality Measures (Degree, Betweenness, Closeness), Community Detection Algorithms, Link Prediction and Recommendation Systems, Graph Databases and Analytics, Diffusion and Influence in Networks |
| CSC-DS23E05 | Time Series Analysis and Forecasting | Professional Elective | 3 | Time Series Components (Trend, Seasonality, Cyclical), Stationarity and ARIMA Models, Exponential Smoothing Methods, Forecasting Techniques and Evaluation, Spectral Analysis for Time Series, Multivariate Time Series Models |
| CSC-DS23E06 | Reinforcement Learning | Professional Elective | 3 | Markov Decision Processes (MDPs), Dynamic Programming in RL, Monte Carlo Methods and Temporal Difference Learning, Q-Learning and SARSA Algorithms, Policy Gradients and Actor-Critic Methods, Deep Reinforcement Learning |
| CSC-DS23E07 | Edge Analytics | Professional Elective | 3 | Introduction to Edge Computing Architecture, IoT Data Processing at the Edge, Real-time Analytics on Edge Devices, Distributed Data Processing Frameworks, Fog Computing and Microservices, Security and Privacy in Edge Analytics |
| CSC-DS23E08 | Generative AI | Professional Elective | 3 | Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models for Image Generation, Large Language Models (LLMs) and Architectures, Text-to-Image and Text-to-Text Generation, Ethical Implications of Generative AI |
| CSC-DS23E09 | Text Analytics | Professional Elective | 3 | Information Retrieval Systems, Text Summarization Techniques, Topic Modeling (LDA, NMF), Named Entity Recognition (NER), Text Classification and Clustering, Sentiment Analysis and Opinion Mining |
| CSC-DS23303 | Mini Project | Project | 2 | Problem Identification and Scope Definition, Literature Survey and Methodology Selection, Data Collection and Preprocessing, System Design and Implementation, Testing and Evaluation of Results, Project Report Writing and Presentation |
| CSC-DS23304 | Comprehensive Viva Voce | Core | 2 | Overall Data Science Program Concepts, Advanced Machine Learning Theories, Big Data Technologies and Applications, Deep Learning Architectures and Use Cases, Database Management Principles, Research Methodology and Ethical Considerations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSC-DS23401 | Project Work and Viva Voce | Project | 20 | Advanced Problem Formulation and Research, System Design and Architecture for Real-world Problems, Extensive Implementation and Development, Comprehensive Testing and Performance Evaluation, Dissertation Writing and Documentation, Final Project Defense and Viva Voce Examination |
