M-TECH in Data Science at Manipal Academy of Higher Education
Udupi, Karnataka
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About the Specialization
What is Data Science at Manipal Academy of Higher Education Udupi?
This Data Science program at Manipal Academy of Higher Education focuses on equipping students with advanced skills in data analytics, machine learning, and big data technologies. It''''s highly relevant in the booming Indian data industry, offering a blend of theoretical foundations and practical applications. The program differentiates itself through a robust curriculum aligned with current industry demands and emerging technologies.
Who Should Apply?
This program is ideal for fresh engineering or science graduates seeking entry into the data science domain, and working professionals looking to upskill in AI/ML or transition into data-driven roles. Candidates with a strong mathematical or programming background and a passion for problem-solving with data will thrive. Prerequisites include a relevant B.E./B.Tech. or M.Sc./MCA degree.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as Data Scientists, Machine Learning Engineers, Big Data Analysts, or AI Specialists across various sectors like IT, finance, healthcare, and e-commerce. Entry-level salaries typically range from INR 6-10 LPA, with significant growth potential up to INR 20-30 LPA for experienced professionals in leading tech companies and startups.
Student Success Practices
Foundation Stage
Master Core Mathematical and Programming Concepts- (Semester 1-2)
Dedicate significant time to solidify understanding in linear algebra, calculus, probability, statistics, and advanced Python programming. Regularly practice problem-solving through coding challenges to build a strong analytical base for data science.
Tools & Resources
NPTEL courses on Mathematics for Machine Learning, HackerRank, LeetCode, Kaggle (for beginner-friendly challenges)
Career Connection
A strong foundation is crucial for excelling in machine learning and deep learning, directly impacting project quality and interview performance for analytical and ML engineering roles in India.
Actively Participate in Data Science Labs and Projects- (Semester 1-2)
Go beyond assigned tasks in data science and machine learning laboratories. Experiment with different libraries, algorithms, and datasets. Collaborate with peers on small-scale projects to apply theoretical knowledge to practical scenarios and build initial project experience.
Tools & Resources
Jupyter Notebooks, Google Colab, Scikit-learn, Pandas, Matplotlib, GitHub for version control
Career Connection
Practical experience is highly valued by Indian employers. Early project work builds a portfolio that demonstrates hands-on skills and problem-solving abilities during campus placements and interviews.
Engage in Peer Learning and Study Groups- (Semester 1-2)
Form study groups with classmates to discuss complex topics, solve assignments collaboratively, and prepare for examinations. Teaching others reinforces your own understanding and exposes you to different problem-solving approaches and perspectives.
Tools & Resources
WhatsApp groups, Discord, shared online whiteboards for collaborative problem-solving, college library resources
Career Connection
Develops essential teamwork and communication skills, which are critical in corporate data science teams. It also strengthens a professional network early on, which can be beneficial for future career opportunities in India.
Intermediate Stage
Deep Dive into Elective Specializations- (Semester 3)
Beyond core subjects, thoroughly explore chosen elective areas like Natural Language Processing, Computer Vision, or Reinforcement Learning. Undertake mini-projects or extended assignments specifically in these domains to build specialized expertise.
Tools & Resources
TensorFlow, PyTorch, Hugging Face (for NLP), OpenCV (for Computer Vision), OpenAI Gym (for Reinforcement Learning)
Career Connection
Builds expertise in high-demand niches, making candidates more attractive for specialized roles in AI/ML companies, particularly in the competitive Indian tech landscape.
Seek Out Relevant Internships and Industry Projects- (Semester 3 (or summer break after Semester 2))
Actively look for summer internships or part-time industry projects that align with your data science interests. This provides real-world exposure and practical application of learned concepts in a professional business setting.
Tools & Resources
LinkedIn, Internshala, college placement cell, networking with faculty and alumni through workshops
Career Connection
Internships are often a direct pipeline to full-time employment and provide invaluable experience that distinguishes candidates in the competitive Indian job market, showcasing readiness for industry challenges.
Participate in Data Science Competitions and Hackathons- (Semester 3)
Regularly participate in platforms like Kaggle, Analytics Vidhya, or college-level hackathons. This helps in sharpening problem-solving skills under time pressure and building a public portfolio of practical, competitive work.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for sharing solutions, Google Colab for computational resources
Career Connection
Showcases practical abilities, resilience, and a passion for data science, all highly regarded by hiring managers in India. Winning or performing well in these competitions can significantly boost your resume.
Advanced Stage
Excellence in Dissertation Research- (Semester 4)
Dedicate comprehensive effort to the Dissertation Phase-II. Choose a challenging and relevant research problem, meticulously execute the methodology, and produce a high-quality thesis. Focus on original contributions and rigorous analysis.
Tools & Resources
Research papers (IEEE, ACM, arXiv), academic databases, LaTeX for thesis writing, advanced ML frameworks for experimentation
Career Connection
A strong dissertation can lead to publications, demonstrate advanced research capabilities, and open doors to R&D roles in companies or academic positions and PhD programs in India and globally.
Intensive Placement Preparation- (Semester 4)
Start rigorous preparation for placements well in advance of the final semester. Practice technical interviews, aptitude tests, and case studies commonly used by top Indian companies and MNCs. Refine your resume and LinkedIn profile for maximum impact.
Tools & Resources
InterviewBit, LeetCode, GeeksforGeeks for coding and ML interview questions, mock interviews with seniors and mentors
Career Connection
Directly impacts securing desirable placements in leading tech companies and startups, offering competitive salary packages and accelerated career growth in India''''s dynamic IT sector.
Network Actively and Build Professional Presence- (Semester 4)
Attend industry webinars, conferences, and alumni meets (both online and offline). Engage with data science professionals on LinkedIn, seeking mentorship and insights into industry trends and emerging opportunities in India.
Tools & Resources
LinkedIn, industry associations (e.g., NASSCOM, TiE), alumni network platforms, professional meetups
Career Connection
Expands professional contacts, potentially leading to referral opportunities, and provides valuable career guidance, crucial for long-term career growth and navigating India''''s competitive tech job market.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Science & Engineering/Information Technology/Computer & Communication Engineering/Computer Engineering/Electronics & Communication Engineering/Instrumentation & Control Engineering/Electrical & Electronics Engineering/Electrical Engineering/Electronics Engineering/Bioinformatics/Biomedical Engineering or MCA or M.Sc. (Computer Science/Information Technology/Mathematics/Statistics/Physics/Computational Sciences/Bioinformatics) from a recognized University, with minimum 50% aggregate marks. Candidates with certain other B.E./B.Tech. or M.Sc. degrees are NOT eligible. Degree must be AICTE/UGC approved.
Duration: 2 years (4 semesters)
Credits: 80 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 5101 | Advanced Data Structures and Algorithms | Core | 4 | Algorithm analysis techniques, Advanced data structures (trees, heaps, graphs), Sorting and searching algorithms, Dynamic programming, Greedy algorithms |
| MDS 5102 | Data Science & Engineering | Core | 4 | Introduction to Data Science, Data collection and preprocessing, Data warehousing and ETL, Data governance and quality, Big Data concepts |
| MDS 5103 | Mathematical Foundation for Data Science | Core | 4 | Linear Algebra fundamentals, Calculus for optimization, Probability theory, Statistical inference, Regression analysis |
| MDS 5104 | Data Science Laboratory | Lab | 2 | Python programming for data science, Data manipulation with Pandas, Data visualization techniques, SQL for database querying, Basic scripting for data tasks |
| MDS 5105A | Advanced Computer Networks | Elective Option (Elective-I) | 4 | Network architecture and models, Routing protocols and algorithms, Transport layer services, Network security principles, Wireless and mobile networks |
| MDS 5105B | Advanced Operating Systems | Elective Option (Elective-I) | 4 | Process management and scheduling, Memory management techniques, Distributed operating systems, File systems and I/O management, Virtualization concepts |
| MDS 5105C | Advanced Database Management Systems | Elective Option (Elective-I) | 4 | Relational database design, Query processing and optimization, Transaction management and concurrency control, Distributed database systems, NoSQL databases |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 5201 | Machine Learning | Core | 4 | Supervised learning algorithms, Unsupervised learning techniques, Model evaluation and selection, Ensemble methods, Introduction to Neural Networks |
| MDS 5202 | Big Data Technologies | Core | 4 | Hadoop ecosystem, MapReduce programming, Apache Spark for big data, HDFS and distributed storage, Stream processing with Kafka/Flink |
| MDS 5203 | Artificial Intelligence | Core | 4 | AI agents and intelligent systems, Search algorithms (informed/uninformed), Knowledge representation and reasoning, Planning and decision making, Introduction to NLP |
| MDS 5204 | Machine Learning Laboratory | Lab | 2 | Scikit-learn implementation, TensorFlow/Keras for neural networks, Model training and hyperparameter tuning, Data preprocessing for ML models, Visualization of model performance |
| MDS 5205A | Cloud Computing | Elective Option (Elective-II) | 4 | Cloud service models (IaaS, PaaS, SaaS), Virtualization and containerization, Cloud deployment models, AWS/Azure core services, Cloud security and management |
| MDS 5205B | Research Methodology & Technical Communication | Elective Option (Elective-II) | 4 | Research problem identification, Literature review techniques, Data collection and analysis methods, Report writing and presentation, Ethics in research |
| MDS 5205C | Statistical Computing | Elective Option (Elective-II) | 4 | R programming for statistics, Hypothesis testing and p-values, Regression analysis with R, Time series analysis, Data visualization with R |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 6101 | Deep Learning | Core | 4 | Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Generative Adversarial Networks (GANs), Transfer learning and fine-tuning, Attention mechanisms and Transformers |
| MDS 6102 | Data Visualization | Core | 4 | Principles of effective data visualization, Statistical graphics, Interactive dashboards, Tools like Tableau/PowerBI, Data storytelling |
| MDS 6103A | Natural Language Processing | Elective Option (Elective-III) | 4 | Text preprocessing and tokenization, Word embeddings (Word2Vec, GloVe), Sequence models for NLP, Sentiment analysis and topic modeling, Neural language models |
| MDS 6103B | Computer Vision | Elective Option (Elective-III) | 4 | Image processing fundamentals, Feature extraction and matching, Object detection algorithms, Image segmentation, Deep learning for computer vision |
| MDS 6103C | Reinforcement Learning | Elective Option (Elective-III) | 4 | Markov Decision Processes (MDPs), Q-learning and SARSA algorithms, Policy gradient methods, Deep Reinforcement Learning, Multi-agent reinforcement learning |
| MDS 6104A | Distributed Computing | Elective Option (Elective-IV) | 4 | Concurrency and parallelism, Distributed consensus algorithms, Message passing interface (MPI), Fault tolerance in distributed systems, Cloud-native distributed computing |
| MDS 6104B | Cyber Security & Data Privacy | Elective Option (Elective-IV) | 4 | Cryptography and encryption, Network security principles, Data protection regulations (GDPR, India''''s DPA), Privacy-preserving AI techniques, Ethical hacking basics |
| MDS 6104C | Internet of Things (IoT) | Elective Option (Elective-IV) | 4 | IoT architecture and layers, Sensors, actuators, and devices, IoT communication protocols (MQTT, CoAP), Edge computing and fog computing, IoT data analytics |
| MDS 6105 | Dissertation Phase-I | Project | 4 | Problem identification and definition, Comprehensive literature review, Research methodology and design, Preliminary experimental setup, Project proposal and presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 6201 | Dissertation Phase-II | Project | 20 | Advanced research and implementation, Data analysis and interpretation, Validation and testing of results, Technical report writing (thesis), Oral defense and presentation |