M-TECH in Data Science at Manipal Institute of Technology
Udupi, Karnataka
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About the Specialization
What is Data Science at Manipal Institute of Technology Udupi?
This M.Tech Data Science program at Manipal Institute of Technology focuses on equipping students with advanced skills in statistical modeling, machine learning, and big data technologies. It addresses the growing demand for skilled data professionals in the Indian industry, emphasizing practical application and cutting-edge research to solve complex real-world problems. The program distinguishes itself through a strong foundation in theoretical concepts coupled with extensive lab work and capstone projects.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech in CS, IT, ECE, EEE) or M.Sc./MCA postgraduates from a recognized university, possessing a minimum of 50% aggregate marks. It caters to fresh graduates aspiring to enter the high-growth field of data science, as well as working professionals seeking to upskill or transition into data-driven roles within various Indian industries like finance, healthcare, e-commerce, and IT services.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths such as Data Scientist, Machine Learning Engineer, AI Engineer, Data Analyst, or Business Intelligence Developer in India. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning upwards of INR 25-40 LPA. The robust curriculum prepares students for roles in leading Indian companies and global MNCs operating in India, fostering growth trajectories in analytics, product development, and research.
Student Success Practices
Foundation Stage
Master Core Programming & Math- (Semester 1-2)
Develop strong foundational skills in Python programming for data science, coupled with a solid understanding of linear algebra, probability, and statistics. Practice regularly using online coding platforms and mathematical problem sets.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy (for math), Python''''s NumPy and Pandas libraries
Career Connection
Essential for passing technical rounds in placements and for building effective data models and algorithms.
Engage in Data Challenges & Bootcamps- (Semester 1-2)
Participate actively in online data science challenges, hackathons, and virtual bootcamps to apply theoretical knowledge to practical problems. This helps in understanding data cleaning, feature engineering, and model building in a competitive environment.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub, local university hackathons
Career Connection
Builds a strong portfolio, demonstrates practical problem-solving skills, and provides networking opportunities with industry experts.
Form Study Groups & Peer Learning- (Semester 1-2)
Collaborate with peers to discuss complex concepts, review assignments, and work on mini-projects. Explaining topics to others solidifies your own understanding and exposes you to different problem-solving approaches.
Tools & Resources
Microsoft Teams, Google Meet, WhatsApp groups, shared whiteboards
Career Connection
Enhances teamwork and communication skills, which are crucial in collaborative data science roles.
Intermediate Stage
Undertake Industry Internships/Projects- (Semester 3)
Seek out summer internships or engage in industry-sponsored projects to gain hands-on experience with real-world data science pipelines, tools, and challenges. Focus on applying machine learning and big data technologies.
Tools & Resources
LinkedIn, Internshala, university placement cell, industry mentorship programs
Career Connection
Provides invaluable industry exposure, builds a professional network, and significantly boosts resume credibility for full-time roles.
Specialize through Electives & Certifications- (Semester 3)
Choose elective courses strategically based on career interests (e.g., NLP, Computer Vision, Deep Learning) and pursue relevant professional certifications. This deepens expertise in a niche area and signals specialized skills to employers.
Tools & Resources
Coursera (DeepLearning.AI, IBM Data Science), edX, Udemy, official vendor certifications (e.g., AWS Certified Machine Learning Specialty)
Career Connection
Differentiates candidates in a competitive job market and aligns skills with specific industry demands.
Contribute to Open-Source Projects- (Semester 3)
Actively contribute to open-source data science libraries or tools. This showcases coding proficiency, collaboration skills, and a commitment to the data science community. Start by fixing bugs or improving documentation.
Tools & Resources
GitHub, GitLab, relevant open-source data science projects (e.g., Scikit-learn, TensorFlow)
Career Connection
Creates a tangible track record of practical contributions, impresses hiring managers, and helps build a strong professional profile.
Advanced Stage
Excel in Capstone Project & Portfolio Building- (Semester 3-4)
Dedicate significant effort to the Capstone Project (MDS 603 & MDS 602). Ensure it is a comprehensive, industry-relevant problem with demonstrable impact. Document the project thoroughly, showcasing your problem-solving process, methodologies, and results in a polished portfolio.
Tools & Resources
GitHub for code, Medium/LinkedIn for project blogs, Tableau/Power BI for visualizations, Streamlit for interactive apps
Career Connection
The Capstone Project is a prime talking point in interviews, demonstrating end-to-end data science capabilities and is critical for placement success.
Intensive Placement Preparation- (Semester 4)
Focus on preparing for technical interviews, aptitude tests, and HR rounds. Practice coding, review core data science concepts, and work on mock interviews. Understand common algorithms, SQL, and system design questions relevant to data roles.
Tools & Resources
InterviewBit, LeetCode, GeeksforGeeks, Glassdoor for company-specific interview questions, university career services
Career Connection
Direct preparation for securing placement offers from top companies, maximizing conversion rates in interview processes.
Network Strategically & Attend Conferences- (Semester 3-4)
Actively network with industry professionals, alumni, and recruiters through LinkedIn, career fairs, and data science conferences. Attend webinars and workshops to stay updated on emerging trends and technologies.
Tools & Resources
LinkedIn, industry meetups (e.g., PyData, Data Science Congress), university alumni network
Career Connection
Opens doors to hidden job opportunities, provides insights into industry expectations, and helps build a valuable professional support system.
Program Structure and Curriculum
Eligibility:
- B.E. / B.Tech. in Computer Science & Engineering / Information Technology / Computer & Communication Engineering / Software Engineering / Electrical & Electronics Engineering / Electronics & Communication Engineering / Instrumentation & Control Engineering / Telecommunication Engineering / Data Science / Artificial Intelligence & Machine Learning OR M.Sc. in Computer Science / Information Technology / Data Science / Applied Mathematics / Statistics / Artificial Intelligence & Machine Learning OR MCA from a recognized University with minimum 50% aggregate marks or equivalent.
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 501 | Mathematical Foundations for Data Science | Core | 4 | Probability and Statistics, Linear Algebra, Optimization, Discrete Mathematics, Information Theory |
| MDS 503 | Advanced Data Structures & Algorithms | Core | 4 | Advanced Data Structures, Algorithm Design Techniques, Graph Algorithms, Dynamic Programming, Complexity Analysis |
| MDS 505 | Modern Data Management | Core | 4 | Relational Databases, NoSQL Databases, Distributed Databases, Data Warehousing, Data Governance |
| MDS 507 | Foundations of Data Science | Core | 4 | Introduction to Data Science, Data Collection, Data Preprocessing, Exploratory Data Analysis, Data Visualization |
| MDS 509 | Data Science Lab - I | Lab | 2 | Python Programming for Data Science, Data Manipulation (Pandas), Numerical Computing (Numpy), Visualization (Matplotlib, Seaborn), Basic Statistical Analysis |
| MDS 511 | Distributed Systems (Elective 1 - Example) | Elective | 3 | Distributed System Architectures, Communication, Consistency, Fault Tolerance, Distributed Algorithms |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 502 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression, Classification, Model Evaluation, Deep Learning Basics |
| MDS 504 | Big Data Technologies | Core | 4 | Hadoop Ecosystem, MapReduce, HDFS, Spark, Kafka, NoSQL Databases |
| MDS 506 | Data Visualization | Core | 4 | Principles of Visualization, Visual Perception, Data Storytelling, Interactive Visualizations, Visualization Tools |
| MDS 508 | Research Methodology & IPR | Core | 3 | Research Design, Literature Review, Data Collection Methods, Statistical Analysis for Research, Intellectual Property Rights |
| MDS 510 | Data Science Lab - II | Lab | 2 | Machine Learning Libraries (Scikit-learn), Big Data Frameworks (Spark), Data Visualization Tools, Advanced Python for Data Science, Model Deployment Basics |
| MDS 512 | Natural Language Processing (Elective 2 - Example) | Elective | 3 | Text Preprocessing, Language Models, Text Classification, Named Entity Recognition, Sentiment Analysis |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 601 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Generative Adversarial Networks |
| MDS 603 | Data Science Capstone Project - I | Project | 6 | Problem Definition, Literature Review, Project Planning, Data Collection, Methodology Design |
| MDS 609 | Ethical AI and Data Governance (Elective 3 - Example) | Elective | 3 | AI Ethics Principles, Bias in AI, Data Privacy, Regulations (GDPR, India-specific), AI Governance Frameworks |
| MDS 611 | Business Intelligence (Elective 4 - Example) | Elective | 3 | Data Warehousing, OLAP, BI Tools, Reporting, Dashboarding, Performance Management |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MDS 602 | Data Science Capstone Project - II | Project | 16 | Implementation, Experimentation, Results Analysis, Report Writing, Presentation, Deployment |