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M-TECH in Data Science And Engineering at National Institute of Technology Agartala
West Tripura, Tripura
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About the Specialization
What is Data Science and Engineering at National Institute of Technology Agartala West Tripura?
This Data Science and Engineering program at National Institute of Technology Agartala focuses on equipping students with advanced skills in data analysis, machine learning, and big data technologies. It is highly relevant to the burgeoning Indian industry, which demands professionals adept at extracting insights from vast datasets. The program''''s strength lies in its blend of theoretical foundations and practical applications, preparing graduates for cutting-edge roles in data-driven decision-making.
Who Should Apply?
This program is ideal for engineering graduates, particularly from Computer Science, Information Technology, or allied fields, who possess a strong analytical aptitude and a valid GATE score. It caters to fresh graduates seeking entry into the data science domain and working professionals looking to upskill in areas like machine learning, deep learning, and big data analytics. Individuals keen on solving real-world problems using data-driven approaches would find this specialization highly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Machine Learning Engineers, Big Data Architects, or Data Analysts within top Indian companies and multinational corporations operating in India. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more. The strong curriculum helps in aligning with industry-recognized professional certifications in areas like cloud data platforms and AI.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Data Structures- (Semester 1)
Consistently practice coding in Python, focusing on data structures, algorithms, and libraries like NumPy and Pandas. Solve problems on platforms like HackerRank and LeetCode. Engage in peer learning sessions to understand complex concepts.
Tools & Resources
Python, Jupyter Notebook, HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Data Structures and Algorithms
Career Connection
Strong programming skills are fundamental for data science roles, crucial for technical interviews and efficient data manipulation.
Build a Strong Mathematical & Statistical Base- (Semester 1)
Dedicate time to thoroughly understand linear algebra, probability, and statistics concepts, as they underpin machine learning. Solve textbook problems and explore applied examples to see their relevance in data science.
Tools & Resources
Khan Academy, MIT OpenCourseWare, Textbooks on linear algebra and probability, Online courses from Coursera/edX
Career Connection
A robust theoretical foundation is essential for designing and interpreting complex machine learning models, leading to better model performance and explainability.
Engage in Early Data Science Projects- (Semester 1)
Start working on small, personal data science projects using publicly available datasets (e.g., Kaggle). Focus on end-to-end tasks like data cleaning, exploratory data analysis, and basic model building.
Tools & Resources
Kaggle, Google Colab, Scikit-learn, Matplotlib, Seaborn
Career Connection
Practical project experience demonstrates problem-solving abilities and helps in building a portfolio for internships and placements.
Intermediate Stage
Deepen Specialization in Machine Learning/Deep Learning- (Semester 2)
Actively participate in advanced courses like Deep Learning and choose electives that align with specific interests (e.g., NLP, Cloud Computing). Work on projects that apply these advanced techniques to real-world datasets.
Tools & Resources
TensorFlow, PyTorch, Keras, Hugging Face, OpenCV, AWS/Azure/GCP free tiers
Career Connection
Specialization in advanced ML/DL areas opens doors to roles like AI Engineer, Deep Learning Researcher, which are high-demand and high-paying.
Seek Industry Internships and Collaborations- (Semester 2)
Actively look for summer or semester-long internships in data science, analytics, or AI roles at startups, Indian tech giants, or MNCs in India. Apply academic knowledge to solve industry problems.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry networking events
Career Connection
Internships provide invaluable industry exposure, practical experience, and often lead to pre-placement offers, significantly boosting career prospects.
Participate in Data Science Competitions- (Semester 2)
Regularly engage in Kaggle competitions or similar data science challenges. This helps in refining problem-solving skills, learning new techniques, and collaborating with peers. Analyze winning solutions to improve understanding.
Tools & Resources
Kaggle, DrivenData, Zindi
Career Connection
Competition success builds a strong portfolio, showcases practical skills, and attracts attention from recruiters in the data science field.
Advanced Stage
Excel in Project Work and Research- (Semesters 3-4)
Devote significant effort to the M.Tech project (Phase I and II), aiming for innovative solutions and a strong thesis. Consider publishing findings in reputable conferences or journals, showcasing research capabilities.
Tools & Resources
LaTeX for thesis writing, Academic databases (IEEE, ACM, Scopus), Version control (Git)
Career Connection
A well-executed project and potential publications enhance credibility for R&D roles, PhD aspirations, and senior data scientist positions.
Prepare for Placements and Technical Interviews- (Semesters 3-4)
Start placement preparation early, focusing on mock interviews, resume building, and developing strong communication skills for technical discussions. Brush up on core computer science and data science concepts.
Tools & Resources
InterviewBit, LeetCode, Company-specific interview guides, Campus placement workshops
Career Connection
Effective preparation is key to securing desired job roles in leading data science and tech companies, ensuring a smooth transition to industry.
Network and Build Professional Connections- (Semesters 3-4)
Attend webinars, workshops, and industry conferences. Connect with alumni and professionals in the data science field on platforms like LinkedIn. Participate in professional communities to stay updated on industry trends.
Tools & Resources
LinkedIn, Industry-specific forums, Local meetups, Alumni network
Career Connection
Networking can open doors to new opportunities, mentorship, and insights into career progression, which are invaluable for long-term career growth.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 4 semesters
Credits: 70 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDS 5101 | Mathematical Foundations of Data Science | Core | 4 | Linear Algebra, Probability and Statistics, Optimization Techniques, Random Variables, Hypothesis Testing, Multivariate Analysis |
| CSDS 5102 | Advanced Data Structures and Algorithms | Core | 4 | Advanced Data Structures, Amortized Analysis, Graph Algorithms, Dynamic Programming, NP-Completeness, Approximation Algorithms |
| CSDS 5103 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Evaluation Metrics, Model Selection, Ensemble Methods, Neural Networks |
| CSDS 5104 | Distributed Computing for Data Science | Core | 4 | Distributed Systems Concepts, Hadoop Ecosystem, Apache Spark, MapReduce Paradigm, Distributed Storage, Consensus Algorithms, Distributed Machine Learning |
| CSDS 5105 | Data Science Lab-I | Lab | 3 | Python for Data Science, Data Manipulation (Pandas), Data Visualization (Matplotlib, Seaborn), Machine Learning Libraries (Scikit-learn), Data Preprocessing Techniques, SQL for Data Retrieval |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDS 5201 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers and Attention, Generative Models (GANs, VAEs), Deep Learning Frameworks (TensorFlow, PyTorch) |
| CSDS 5202 | Big Data Analytics | Core | 4 | Big Data Technologies, Data Streams Processing, Real-time Analytics, Data Warehousing Concepts, Data Lakes Architecture, Data Governance, Cloud Data Platforms |
| CSDS 5221 | Natural Language Processing (Elective-I Example) | Elective | 4 | Text Preprocessing, Language Models, Text Classification, Named Entity Recognition, Machine Translation, Sentiment Analysis |
| CSDS 5227 | Cloud Computing (Elective-II Example) | Elective | 4 | Cloud Architectures, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Security Challenges, Containerization (Docker, Kubernetes), Serverless Computing, Cloud Deployment Models |
| CSDS 5205 | Data Science Lab-II | Lab | 3 | Deep Learning Frameworks Implementation, Big Data Tools (Spark, Hadoop) Practical, NLP Libraries Usage, Database Integration with Python, Cloud Platform APIs for Data, Advanced Data Visualization |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDS 6121 | Reinforcement Learning (Elective-III Example) | Elective | 4 | Markov Decision Processes, Dynamic Programming for RL, Monte Carlo Methods, Q-Learning and Sarsa, Deep Reinforcement Learning, Policy Gradient Methods |
| CSDS 6122 | Computer Vision (Elective-IV Example) | Elective | 4 | Image Processing Fundamentals, Feature Detection and Description, Object Recognition, Image Segmentation, 3D Computer Vision, Deep Learning for Vision |
| CSDS 6101 | Seminar | Project/Seminar | 4 | Research Paper Presentation, Technical Report Writing, Literature Review Techniques, Effective Presentation Skills, Academic Research Ethics, Q&A Handling |
| CSDS 6102 | Project Phase-I | Project | 4 | Problem Identification and Formulation, Extensive Literature Survey, Project Proposal Development, Methodology Design and Planning, Initial Implementation and Experimentation, Report Writing and Documentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSDS 6201 | Project Phase-II | Project | 16 | System Design and Architecture, Full-scale Implementation, Testing and Validation, Performance Evaluation and Analysis, Thesis Writing and Formatting, Viva Voce Preparation, Potential for Research Publication |
