B-TECH in Data Science Engineering at Manipal Academy of Higher Education
Udupi, Karnataka
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About the Specialization
What is Data Science & Engineering at Manipal Academy of Higher Education Udupi?
This Data Science & Engineering program at Manipal Academy of Higher Education focuses on equipping students with a robust foundation in data analysis, machine learning, and artificial intelligence. With a curriculum designed to meet the growing demands of the Indian digital economy, the program emphasizes practical application and theoretical understanding, preparing graduates for roles in data-driven innovation across various sectors.
Who Should Apply?
This program is ideal for aspiring data scientists, machine learning engineers, and data analysts. It caters to fresh graduates seeking entry into the thriving data industry, working professionals looking to upskill in advanced analytics, and career changers transitioning to data-centric roles. Candidates should possess strong analytical skills and a foundational understanding of mathematics and programming.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, ML Engineer, Business Intelligence Analyst, or AI Specialist in India. Entry-level salaries range from INR 6-10 lakhs per annum, with experienced professionals earning significantly more. The program fosters critical thinking and problem-solving, aligning with certifications in cloud platforms and advanced analytics, paving the way for leadership roles in Indian tech firms.
Student Success Practices
Foundation Stage
Master Core Programming and Data Structures- (Semester 1-2)
Build a strong foundation in C and Data Structures. Focus on algorithmic thinking, problem-solving, and efficient code writing. Actively participate in coding challenges regularly to enhance practical application.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks
Career Connection
This practice is essential for cracking technical interviews and building efficient data processing solutions, which are fundamental prerequisites for any data science or engineering role.
Develop Strong Mathematical and Statistical Acumen- (Semester 1-2)
Pay close attention to Engineering Mathematics and Probability & Statistics. These subjects form the theoretical backbone of data science algorithms. Practice solving problems thoroughly, understand derivations, and apply concepts to real-world scenarios.
Tools & Resources
Khan Academy, NPTEL courses on Linear Algebra and Probability, Standard Textbooks
Career Connection
Crucial for understanding the underlying mechanisms of machine learning algorithms, performing rigorous model evaluation, and conducting accurate statistical inference in data projects.
Cultivate Effective Communication Skills- (Semester 1-2)
Actively engage in Communication Skills courses. Practice presenting technical topics clearly and concisely to diverse audiences. Participate in group discussions to refine articulation, listening skills, and foster teamwork.
Tools & Resources
Toastmasters International (if available), English Language Learning Apps, Mock Group Discussions
Career Connection
Vital for effectively explaining complex data insights to non-technical stakeholders, writing comprehensive reports, and excelling in professional interviews and team collaborations.
Intermediate Stage
Gain Hands-on Experience with Data Science Tools- (Semester 3-5)
Go beyond theoretical understanding in Introduction to Data Science, DBMS, and Machine Learning. Get hands-on with Python libraries (Numpy, Pandas, Scikit-learn), SQL, and data visualization tools. Work on small, self-driven projects.
Tools & Resources
Kaggle Datasets, GitHub for Project Hosting, DataCamp, Coursera
Career Connection
Directly builds the practical skills sought by recruiters for data analyst and junior data scientist roles, demonstrating readiness for real-world data challenges.
Engage in Mini-Projects and Internships- (Semester 4-5)
Apply learned concepts by developing mini-projects independently or in groups. Proactively seek out short-term internships or virtual internships to gain initial industry exposure and understand real-world project lifecycles.
Tools & Resources
LinkedIn for Internship Searches, University Career Services, Project-Based Learning Platforms
Career Connection
Builds a valuable portfolio of practical work, provides crucial industry experience, and helps in networking, all of which are critical for securing future placements and opportunities.
Participate in Hackathons and Data Competitions- (Semester 4-5)
Join data science hackathons and Kaggle competitions. This enhances problem-solving under pressure, fosters teamwork, and provides exposure to diverse real-world datasets and innovative approaches to data challenges.
Tools & Resources
Kaggle, Analytics Vidhya, University-Organized Hackathons
Career Connection
Sharpens competitive skills, demonstrates initiative and resilience, and provides valuable content for resume building and compelling discussions during job interviews.
Advanced Stage
Specialize through Advanced Electives and Research- (Semester 6-7)
Deep dive into areas of interest like Deep Learning, NLP, Big Data, or specific analytics domains (e.g., Healthcare, Financial) by choosing relevant electives. Consider taking up research papers or participating in advanced study groups to further expertise.
Tools & Resources
Arxiv, Academic Journals, Google Scholar, Specialized Online Courses
Career Connection
Develops niche expertise, making graduates highly desirable for specialized roles and advanced research opportunities within the rapidly evolving AI and Machine Learning domain.
Undertake a Comprehensive Major Project- (Semester 8)
Dedicate significant effort to the Major Project in Semester 8. Choose a challenging, real-world problem, apply advanced data science and engineering techniques, and aim for a deployable solution or a research publication. Collaborate with industry mentors if possible.
Tools & Resources
Industry Collaboration, University Labs, Open-Source Projects, Cloud Platforms (AWS, Azure, GCP)
Career Connection
The major project is a flagship item on a resume, showcasing end-to-end capabilities, advanced problem-solving prowess, and often leads to pre-placement offers from leading companies.
Focus on Placement Preparation and Networking- (Semester 7-8)
Systematically prepare for placements through mock interviews, resume workshops, and aptitude tests. Network proactively with alumni, industry professionals, and attend career fairs to explore opportunities and gain crucial industry insights.
Tools & Resources
University Placement Cell, LinkedIn, Professional Networking Events, Interview Preparation Platforms (e.g., InterviewBit)
Career Connection
Ensures comprehensive readiness for the competitive job market, significantly improves interview performance, and opens doors to a wider range of career prospects in top companies across India.
Program Structure and Curriculum
Eligibility:
- Minimum 50% aggregate marks in 10+2 or equivalent examination with Physics, Mathematics and English as compulsory subjects, along with Chemistry or Biotechnology or Biology or any technical vocational subject as optional subjects.
Duration: 8 semesters (4 years)
Credits: 170 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 1101 | Engineering Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Infinite Series, Differential Equations, Vector Calculus |
| PH 1101 | Engineering Physics | Core | 4 | Classical Mechanics, Special Relativity, Oscillations and Waves, Optics, Quantum Mechanics, Solid State Physics |
| PH 1102 | Engineering Physics Lab | Lab | 1 | Experiments on Mechanics, Optics, Electricity, Properties of Materials |
| CS 1101 | Problem Solving using C | Core | 3 | C Language Basics, Control Flow, Functions, Arrays, Pointers, Structures and Unions, File Handling |
| CS 1102 | Problem Solving using C Lab | Lab | 1 | Hands-on C Programming Exercises, Debugging Techniques, Problem-Solving using C |
| EC 1101 | Basic Electrical and Electronics Engineering | Core | 3 | DC Circuits, AC Circuits, Electrical Machines, Diodes, Transistors, Operational Amplifiers |
| EC 1102 | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Verification of Circuit Laws, Characteristics of Diodes and Transistors, Op-Amp Applications |
| ME 1101 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sections of Solids, Development of Surfaces, CAD Basics |
| ME 1102 | Engineering Graphics Lab | Lab | 1 | Drafting using CAD Software, Manual Drafting Practice |
| HS 1101 | Communication Skills in English | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Presentation Skills, Technical Communication |
| CV 1101 | Environmental Studies | Core | 1 | Ecosystems, Natural Resources, Environmental Pollution, Social Issues and Environment, Environmental Management |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 1201 | Engineering Mathematics - II | Core | 4 | Multivariable Calculus, Laplace Transforms, Fourier Series, Complex Numbers, Introduction to Probability |
| CY 1201 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Water Treatment, Polymers, Phase Rule, Green Chemistry |
| CY 1202 | Engineering Chemistry Lab | Lab | 1 | Experiments on Titrations, Water Analysis, Instrumental Analysis, Synthesis of Compounds |
| CS 1201 | Data Structures | Core | 3 | Arrays, Stacks and Queues, Linked Lists, Trees, Graphs, Searching and Sorting Algorithms |
| CS 1202 | Data Structures Lab | Lab | 1 | Implementation of Data Structures, Algorithm Design, Complexity Analysis |
| ME 1201 | Basic Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Manufacturing Processes, Fluid Mechanics |
| ME 1202 | Basic Mechanical Engineering Lab | Lab | 1 | Experiments on IC Engines, Pumps, Mechanical Properties of Materials, Workshop Practices |
| CV 1201 | Basic Civil Engineering | Core | 3 | Building Materials, Surveying, Concrete Technology, Structural Elements, Water Resources, Transportation Engineering |
| CV 1202 | Basic Civil Engineering Lab | Lab | 1 | Material Testing, Surveying Measurements, Concrete Mix Design |
| HS 1201 | Constitution of India | Core | 1 | Preamble, Fundamental Rights and Duties, Directive Principles of State Policy, Union and State Government, Judiciary, Constitutional Amendments |
| HS 1202 | Professional Communication and Ethics | Core | 2 | Technical Report Writing, Presentation Skills, Group Discussion Techniques, Professional Ethics, Communication Strategies |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 2101 | Engineering Mathematics - III | Core | 4 | Linear Algebra, Numerical Methods, Transform Techniques, Probability and Statistics |
| CS 2101 | Object Oriented Programming | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling, File I/O |
| CS 2102 | Object Oriented Programming Lab | Lab | 1 | Hands-on OOP Implementation using Java/C++, Debugging and Testing |
| CS 2103 | Computer Organization and Architecture | Core | 3 | Digital Logic Circuits, Data Representation, CPU Design, Memory Hierarchy, I/O Organization, Pipelining |
| CS 2104 | Computer Organization and Architecture Lab | Lab | 1 | Logic Gate Implementation, CPU Simulation, Memory System Design |
| DS 2101 | Introduction to Data Science | Core | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization, Introduction to Machine Learning |
| DS 2102 | Introduction to Data Science Lab | Lab | 1 | Data Analysis using Python (Numpy, Pandas), Data Visualization (Matplotlib, Seaborn), Basic Data Preprocessing |
| DS 2103 | Database Management Systems | Core | 3 | Relational Model, SQL, ER Diagrams, Normalization, Transaction Management, Concurrency Control |
| DS 2104 | Database Management Systems Lab | Lab | 1 | SQL Queries and Operations, Database Design, PL/SQL Programming, Database Connectivity |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA 2201 | Probability and Statistics for Data Science | Core | 4 | Probability Theory, Random Variables and Distributions, Hypothesis Testing, Regression Analysis, Statistical Inference, Bayesian Statistics |
| DS 2201 | Data Structures and Algorithms | Core | 3 | Advanced Data Structures, Algorithm Design Techniques, Complexity Analysis, Graph Algorithms, Dynamic Programming, Amortized Analysis |
| DS 2202 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Advanced Data Structures, Algorithm Optimization, Competitive Programming |
| DS 2203 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Clustering Techniques, Model Evaluation and Selection |
| DS 2204 | Machine Learning Lab | Lab | 1 | Implementation of ML Algorithms using Python, Data Preprocessing for ML, Model Training and Evaluation |
| DS 2205 | Web Technologies | Core | 3 | HTML, CSS, JavaScript, Client-Server Architecture, Web Frameworks, APIs and Web Services, Web Security Basics, Responsive Design |
| DS 2206 | Web Technologies Lab | Lab | 1 | Building Responsive Web Pages, Interactive Web Applications, API Integration, Frontend Frameworks |
| HS 2201 | Universal Human Values | Core | 1 | Self-Exploration, Human Aspirations, Harmony in Self, Family, Society, Harmony in Nature, Professional Ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 3101 | Advanced Database Management Systems | Core | 3 | Distributed Databases, NoSQL Databases, Data Warehousing, Data Mining, Big Data Storage, Database Security |
| DS 3102 | Advanced Database Management Systems Lab | Lab | 1 | Implementation with NoSQL Databases, Data Warehousing Tools, Big Data Platforms |
| DS 3103 | Artificial Intelligence | Core | 3 | AI Principles, Problem Solving, Search Algorithms, Knowledge Representation, Expert Systems, Natural Language Processing Introduction |
| DS 3104 | Artificial Intelligence Lab | Lab | 1 | AI Programming using Python, Implementation of Search Algorithms, Knowledge-Based Systems |
| DS 3105 | Big Data Analytics | Core | 3 | Big Data Ecosystem, Hadoop, Spark, MapReduce, Data Ingestion, Stream Processing, Data Lakes |
| DS 3106 | Big Data Analytics Lab | Lab | 1 | Hands-on with Hadoop/Spark, Data Processing, Distributed Computing Exercises |
| DS 3107 | Operating Systems | Core | 3 | OS Structure, Process Management, CPU Scheduling, Memory Management, File Systems, I/O Systems |
| DS 3108 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management, Memory Allocation, File System Operations |
| HS 3101 | Engineering Economics and Financial Management | Core | 1 | Engineering Economics, Cost Analysis, Financial Statements, Investment Decisions, Project Evaluation |
| DS 3121 | Predictive Analytics | Elective | 3 | Regression Models, Time Series Forecasting, Classification Models, Ensemble Methods, Model Deployment |
| DS 3122 | Data Security and Privacy | Elective | 3 | Data Security Principles, Cryptography for Data, Privacy-Preserving Techniques, Data Governance Frameworks, Compliance Regulations |
| DS 3123 | Time Series Analysis | Elective | 3 | Time Series Components, ARIMA Models, SARIMA Models, Forecasting Techniques, Spectral Analysis |
| DS 3124 | Reinforcement Learning | Elective | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| DS 3125 | Cloud Computing for Data Science | Elective | 3 | Cloud Service Models, Cloud Storage for Data, Big Data on Cloud, Machine Learning on Cloud, Cloud Security |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 3201 | Deep Learning | Core | 3 | Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs and GRUs, Generative Models, Deep Learning Frameworks |
| DS 3202 | Deep Learning Lab | Lab | 1 | Implementation of Deep Learning Models, TensorFlow/Keras/PyTorch, Image and Sequence Data Processing |
| DS 3203 | Natural Language Processing | Core | 3 | NLP Fundamentals, Text Preprocessing, Language Models, Sentiment Analysis, Machine Translation, Text Generation |
| DS 3204 | Natural Language Processing Lab | Lab | 1 | NLP Tasks using NLTK/SpaCy, Building Chatbots, Text Classification, Information Extraction |
| DS 3205 | Data Visualization Techniques | Core | 3 | Principles of Visualization, Data Storytelling, Interactive Visualizations, Tools (Tableau/Power BI), Infographics, Visual Analytics |
| DS 3206 | Data Visualization Techniques Lab | Lab | 1 | Creating Dashboards and Reports, Designing Effective Visualizations, Using Visualization Tools for Data Exploration |
| DS 3207 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Patterns, Software Testing, Project Management, Agile Methodologies |
| DS 3208 | Software Engineering Lab | Lab | 1 | Case Studies in Software Engineering, Use Case Diagrams, Software Design Tools |
| HS 3201 | Industrial Management | Core | 1 | Principles of Management, Production and Operations Management, Human Resource Management, Marketing Management, Supply Chain Management |
| DS 3221 | Ethical AI | Elective | 3 | AI Ethics Principles, Bias in AI, Fairness and Transparency, Accountability in AI, Ethical AI Frameworks |
| DS 3222 | Web Mining | Elective | 3 | Web Content Mining, Web Structure Mining, Web Usage Mining, Sentiment Analysis on Web Data, Link Analysis |
| DS 3223 | Computer Vision | Elective | 3 | Image Processing Fundamentals, Feature Detection, Object Recognition, Image Segmentation, Deep Learning for Vision |
| DS 3224 | Blockchain Technologies | Elective | 3 | Blockchain Fundamentals, Cryptocurrencies, Smart Contracts, Distributed Ledger Technologies, Blockchain Applications |
| DS 3225 | IoT Analytics | Elective | 3 | IoT Architecture, Sensor Data Processing, Edge Analytics, Time Series Data from IoT, IoT Security and Privacy |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 4101 | Data Governance and Ethics | Core | 3 | Data Privacy Laws, Data Security Standards, Ethical AI Considerations, Data Ownership and Compliance, Data Quality Management, Regulatory Frameworks |
| DS 4102 | Data Governance and Ethics Lab | Lab | 1 | Case Studies on Data Ethics, Implementing Data Privacy Measures, Security Audits for Data Systems |
| DS 4103 | Research Methodology and IPR | Core | 2 | Research Design, Data Collection and Analysis, Report Writing, Intellectual Property Rights, Patents and Copyrights |
| DS 4104 | Minor Project | Project | 3 | Project Planning, Literature Review, Design and Implementation, Testing and Documentation, Project Presentation |
| DS 4121 | Recommendation Systems | Elective | 3 | Collaborative Filtering, Content-Based Filtering, Hybrid Recommenders, Evaluation Metrics, Scalability Issues |
| DS 4122 | Geospatial Data Analysis | Elective | 3 | GIS Fundamentals, Spatial Data Models, Geospatial Databases, Spatial Statistics, Geospatial Visualization |
| DS 4123 | Healthcare Analytics | Elective | 3 | Healthcare Data Standards, Predictive Models in Healthcare, Medical Image Analysis, Electronic Health Records, Population Health Analytics |
| DS 4124 | Financial Analytics | Elective | 3 | Financial Data Processing, Risk Analytics, Fraud Detection, Algorithmic Trading, Portfolio Optimization |
| DS 4125 | Social Media Analytics | Elective | 3 | Social Network Analysis, Sentiment Analysis from Social Media, Influence Maximization, Community Detection, Social Media Marketing Analytics |
| DS 4126 | Human-Computer Interaction | Elective | 3 | HCI Principles, User-Centered Design, Usability Testing, Interaction Design, Cognitive Psychology in HCI |
| DS 4127 | Edge AI | Elective | 3 | Edge Computing Architecture, TinyML, On-Device AI, Distributed AI Models, Edge AI Applications |
| DS 4128 | Quantum Machine Learning | Elective | 3 | Quantum Computing Basics, Quantum Superposition and Entanglement, Quantum Algorithms for ML, Quantum Neural Networks, Quantum Optimization |
| DS 4129 | Graph Neural Networks | Elective | 3 | Graph Theory Basics, Graph Embeddings, Convolutional GNNs, Recurrent GNNs, Applications of GNNs |
| DS 4130 | MLOps | Elective | 3 | ML Lifecycle Management, Model Versioning, Model Deployment Strategies, Monitoring and Retraining, CI/CD for ML |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 4201 | Major Project | Project | 12 | Advanced Project Development, System Design and Architecture, Implementation and Testing, Deployment and Evaluation, Technical Report Writing, Viva-Voce Examination |