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MCA in Data Science Informatics at National Institute of Technology Patna
Patna, Bihar
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About the Specialization
What is Data Science & Informatics at National Institute of Technology Patna Patna?
This MCA Data Science & Informatics program at National Institute of Technology Patna focuses on equipping students with advanced theoretical knowledge and practical skills in data science, machine learning, and big data technologies. It is designed to meet the escalating demand for data professionals in the Indian industry, emphasizing core competencies required for intelligent data analysis and decision-making. The program integrates computational methods with statistical insights, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for engineering graduates, especially those from Computer Science, Information Technology, or related fields, and also for science graduates with strong mathematical foundations, seeking entry into data-centric roles. Working professionals looking to upskill in areas like machine learning, deep learning, and big data analytics can also benefit significantly. Career changers with a knack for quantitative analysis and programming aiming to transition into the booming data industry in India will find this program highly relevant.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding India-specific career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, and Big Data Engineer. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning INR 15-30+ LPA in top Indian companies and MNCs. The comprehensive curriculum aligns with industry-recognized certifications and prepares students for advanced research or leadership roles in the data domain.
Student Success Practices
Foundation Stage
Master Core Programming & Data Structures- (Semester 1-2)
Focus intensely on Python, C++, and Java fundamentals, alongside advanced data structures and algorithms. Utilize platforms like HackerRank, LeetCode, and GeeksforGeeks for daily coding practice. This strong base is crucial for tackling complex machine learning and data science algorithms, forming the bedrock for successful placements in product-based and service-based companies.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Online IDEs
Career Connection
Develops problem-solving skills, critical for technical interviews and efficient code development in any data science role.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Pay close attention to Discrete Mathematics, Probability, and Statistics. Leverage online courses from Coursera (e.g., ''''Probability and Statistics for Data Science'''') or NPTEL modules. A solid understanding of these areas is indispensable for grasping the theoretical underpinnings of machine learning and artificial intelligence, vital for analytical roles.
Tools & Resources
Coursera (Statistics Specializations), NPTEL (Probability and Statistics), Khan Academy
Career Connection
Enables understanding and implementation of complex data models, crucial for advanced data science and ML engineering positions.
Engage in Early Project-Based Learning- (Semester 1-2)
Beyond lab assignments, start building small projects showcasing data structures, OOP, and basic database concepts. Collaborate with peers on GitHub-hosted projects. This hands-on experience not only solidifies learning but also creates an impressive portfolio, demonstrating problem-solving capabilities to potential employers early on.
Tools & Resources
GitHub, Jupyter Notebooks, VS Code
Career Connection
Builds a portfolio of practical applications, significantly improving chances for internships and entry-level positions.
Intermediate Stage
Specialize in Machine Learning & Data Analytics- (Semester 3-4)
Dive deep into Machine Learning and Data Warehousing/Mining concepts. Apply learnings through Kaggle competitions, develop end-to-end data analysis projects, and experiment with different ML libraries like Scikit-learn, Pandas, and NumPy. This specialization provides the essential skills for roles in data science and analytics.
Tools & Resources
Kaggle, Scikit-learn, Pandas, NumPy, TensorFlow/PyTorch basics
Career Connection
Directly develops expertise for Data Scientist, Machine Learning Engineer, and Data Analyst roles, enhancing employability.
Develop Expertise in Elective Domains- (Semester 3-4)
Carefully choose electives like Big Data Analytics, NLP, or Deep Learning based on career interests. Pursue certifications (e.g., Hadoop, Spark) and undertake dedicated mini-projects in these areas. This targeted skill development allows students to stand out in specific high-demand niches within the data science landscape.
Tools & Resources
Certifications (e.g., AWS Certified Data Analytics), Online tutorials for specific electives, Industry whitepapers
Career Connection
Creates a specialized skill set, making candidates highly attractive for niche roles and competitive in the job market.
Network and Seek Industry Mentorship- (Semester 3-4)
Actively participate in seminars, workshops, and industry meetups (online/offline). Connect with alumni and industry professionals on LinkedIn. Seek mentorship to understand industry trends, refine career goals, and gain insights into real-world data challenges, significantly boosting internship and placement prospects.
Tools & Resources
LinkedIn, Professional conferences, Alumni network platforms
Career Connection
Opens doors to internships, job opportunities, and invaluable career guidance, improving overall career trajectory.
Advanced Stage
Undertake a Comprehensive Major Project- (Semester 6)
Dedicate significant effort to the Major Project (Semester 6), focusing on a real-world problem involving Data Science/Deep Learning. Aim for an impactful solution, leveraging cutting-edge techniques and showcasing a complete project lifecycle. This project is a cornerstone for demonstrating advanced capabilities to recruiters.
Tools & Resources
Research Papers, Cloud Platforms (AWS, Azure, GCP), Git for version control, Project management tools
Career Connection
Serves as a strong portfolio piece, demonstrating advanced problem-solving, technical depth, and research capabilities to employers.
Prepare Rigorously for Placements & Interviews- (Semester 5-6)
Begin intensive preparation for technical interviews, focusing on data structures, algorithms, system design, and case studies relevant to data science roles. Practice mock interviews, refine resume and cover letters, and attend all campus placement training sessions. Utilize platforms like InterviewBit and Glassdoor for company-specific interview experiences.
Tools & Resources
InterviewBit, LeetCode, Glassdoor, Mock interview platforms
Career Connection
Maximizes chances of securing high-paying placements in reputable companies by honing interview skills and technical knowledge.
Explore Internship Opportunities for Practical Exposure- (Semester 5)
Actively seek industrial training or internships in relevant companies during Semester 5. This practical exposure to corporate environments, tools, and workflows is invaluable for bridging the academic-industry gap and often leads to pre-placement offers, accelerating career entry into top tech firms and data consultancies.
Tools & Resources
College Placement Cell, Internshala, LinkedIn Jobs, Company career pages
Career Connection
Provides real-world experience, often leading to full-time job offers and a smooth transition from academics to professional career.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree with at least 60% marks or 6.5 CGPA (on a 10-point scale) or equivalent. Candidates must have Mathematics/Statistics as one of the subjects at graduation level or 10+2 level. For SC/ST/PwD candidates, the eligibility is 55% marks or 6.0 CGPA (on a 10-point scale) or equivalent.
Duration: 3 years (6 semesters)
Credits: 117 Credits
Assessment: Internal: 40% (for theory courses), 60% (for practical/lab courses), External: 60% (for theory courses), 40% (for practical/lab courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCAD 101 | Advanced Data Structures | Core | 4 | Array, Stack, Queue, Linked List, Tree (Binary, AVL, B-Tree), Graph (Traversal, Shortest Path), Hashing, Collision Resolution, Sorting and Searching Algorithms |
| MCAD 102 | Object Oriented Programming | Core | 4 | OOP Concepts (Classes, Objects), Inheritance, Polymorphism, Abstraction, Encapsulation, Exception Handling, Templates, File I/O, STL (Standard Template Library) |
| MCAD 103 | Discrete Mathematics | Core | 4 | Logic and Propositional Calculus, Set Theory, Relations, Functions, Graph Theory (Paths, Cycles, Trees), Combinatorics (Permutations, Combinations), Algebraic Structures (Groups, Rings) |
| MCAD 104 | Computer Organization and Architecture | Core | 4 | Digital Logic Circuits, Data Representation and Arithmetic, CPU Organization, Instruction Sets, Memory Hierarchy (Cache, Virtual Memory), I/O Organization, Pipelining |
| MCAD 105 | Advanced Data Structures Lab | Lab | 2 | Implementation of Linked Lists, Stacks, Queues, Tree and Graph Traversal Algorithms, Hashing Techniques and Collision Resolution, Sorting and Searching Algorithm Implementations, Application of various data structures |
| MCAD 106 | Object Oriented Programming Lab | Lab | 2 | C++ / Java Program Development, Implementing Classes, Objects, Constructors, Inheritance and Polymorphism Exercises, Exception Handling and File Operations, STL usage and GUI programming basics |
| MCAD 107 | Communication Skills | Lab | 1 | Public Speaking and Presentation Skills, Group Discussions and Debates, Interview Techniques and Etiquette, Technical Report Writing, Non-verbal Communication |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCAD 201 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis and Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Backtracking, Branch and Bound, NP-Completeness and Approximation Algorithms |
| MCAD 202 | Database Management Systems | Core | 4 | DBMS Architecture, Data Models, ER Model, Relational Model, Relational Algebra, SQL (DDL, DML, DCL), Joins, Views, Normalization (1NF, 2NF, 3NF, BCNF), Transaction Management, Concurrency Control |
| MCAD 203 | Operating System | Core | 4 | OS Concepts, Services, Types, Process Management, CPU Scheduling, Deadlocks, Inter-process Communication, Memory Management (Paging, Segmentation), File Systems, I/O Systems |
| MCAD 204 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical and Data Link Layer Protocols, Network Layer (IP, Routing Protocols), Transport Layer (TCP, UDP, Congestion Control), Application Layer (HTTP, DNS, Email Protocols) |
| MCAD 205 | Data Base Management Systems Lab | Lab | 2 | SQL Querying and Database Design, PL/SQL Programming (Procedures, Functions, Triggers), Data Definition and Data Manipulation Language, Database Connectivity (JDBC/ODBC), Mini project on database application |
| MCAD 206 | Operating System Lab | Lab | 2 | Shell Programming and Scripting, Process Management (Creation, Scheduling), Inter-process Communication Mechanisms, Thread Synchronization Problems, Memory Management Simulation |
| MCAD 207 | Programming in Python | Lab | 2 | Python Fundamentals (Syntax, Data Types), Control Structures, Functions, Modules, File Handling and Exception Handling, Libraries for Data Science (NumPy, Pandas), Data Visualization with Matplotlib/Seaborn |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCAD 301 | Machine Learning | Core | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Validation, Ensemble Methods (Bagging, Boosting), Introduction to Deep Learning |
| MCAD 302 | Probability and Statistics | Core | 4 | Probability Theory, Conditional Probability, Random Variables, Probability Distributions, Sampling Theory, Central Limit Theorem, Hypothesis Testing, ANOVA, Correlation and Regression Analysis |
| MCAD 303 | Elective I: Big Data Analytics | Elective | 4 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Spark Architecture and Programming, NoSQL Databases (MongoDB, Cassandra), Data Warehousing and ETL Process |
| MCAD 303 | Elective I: Digital Image Processing | Elective | 4 | Image Fundamentals and Sensing, Image Transforms (Fourier, Wavelet), Image Enhancement (Spatial, Frequency Domain), Image Restoration, Noise Reduction, Image Segmentation, Feature Extraction |
| MCAD 303 | Elective I: Compiler Design | Elective | 4 | Lexical Analysis and Finite Automata, Syntax Analysis (Parsing Techniques), Semantic Analysis and Type Checking, Intermediate Code Generation, Code Optimization and Generation |
| MCAD 303 | Elective I: Internet of Things | Elective | 4 | IoT Architecture and Paradigms, IoT Protocols (MQTT, CoAP, HTTP), Sensors, Actuators, Microcontrollers, Edge Computing and Cloud Integration, IoT Security and Privacy |
| MCAD 304 | Elective II: Soft Computing | Elective | 4 | Fuzzy Logic and Fuzzy Sets, Artificial Neural Networks (ANN), Genetic Algorithms and Optimization, Swarm Intelligence (PSO, ACO), Hybrid Soft Computing Systems |
| MCAD 304 | Elective II: Natural Language Processing | Elective | 4 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), POS Tagging, Named Entity Recognition, Sentiment Analysis and Text Classification, Machine Translation and Text Summarization |
| MCAD 304 | Elective II: Parallel and Distributed Computing | Elective | 4 | Parallel Architectures (Shared, Distributed Memory), Distributed Systems Concepts, Concurrency Control and Message Passing, MPI (Message Passing Interface), OpenMP, Cloud Computing Paradigms |
| MCAD 304 | Elective II: Mobile Computing | Elective | 4 | Wireless Technologies (GSM, GPRS, Wi-Fi), Mobile Operating Systems (Android, iOS), Mobile Application Development Basics, Location-Based Services (GPS), Mobile Security and Management |
| MCAD 305 | Machine Learning Lab | Lab | 2 | Implementation of Supervised Learning Algorithms, Unsupervised Learning (Clustering) Experiments, Data Preprocessing and Feature Engineering, Model Training, Evaluation, and Hyperparameter Tuning, Using Scikit-learn, TensorFlow/PyTorch Basics |
| MCAD 306 | Data Analytics Lab | Lab | 2 | Statistical Analysis with Python (SciPy), Data Cleaning and Transformation, Data Visualization Techniques (Matplotlib, Seaborn), Exploratory Data Analysis (EDA), Case Studies in Data Analytics |
| MCAD 307 | Mini Project | Project | 2 | Problem Identification and Scope Definition, System Design and Architecture, Implementation and Testing, Project Documentation and Presentation, Teamwork and Project Management |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCAD 401 | Deep Learning | Core | 4 | Neural Network Architectures (ANN, CNN, RNN), Activation Functions and Optimizers, Convolutional Neural Networks (CNN) for Image, Recurrent Neural Networks (RNN) for Sequence Data, Transformers, Generative Adversarial Networks (GANs) |
| MCAD 402 | Data Warehousing and Data Mining | Core | 4 | Data Warehousing Concepts, OLAP, Data Mining Techniques and Applications, Association Rule Mining (Apriori), Classification Algorithms (Decision Trees, SVM), Clustering Algorithms (K-Means, Hierarchical) |
| MCAD 403 | Elective III: Blockchain Technology | Elective | 4 | Blockchain Fundamentals and Cryptography, Distributed Ledger Technologies (DLT), Consensus Mechanisms (PoW, PoS), Smart Contracts and Decentralized Applications (DApps), Ethereum, Hyperledger, Blockchain Use Cases |
| MCAD 403 | Elective III: Reinforcement Learning | Elective | 4 | Markov Decision Processes (MDP), Dynamic Programming in RL, Monte Carlo Methods, Temporal Difference Learning, Q-Learning, SARSA Algorithms, Policy Gradient Methods, Deep Reinforcement Learning |
| MCAD 403 | Elective III: Cryptography and Network Security | Elective | 4 | Symmetric Key Cryptography (DES, AES), Asymmetric Key Cryptography (RSA), Hash Functions, Digital Signatures, Network Security (Firewalls, IDS), Web Security, Email Security |
| MCAD 403 | Elective III: Cloud Computing | Elective | 4 | Cloud Computing Architecture, Service Models (IaaS, PaaS, SaaS), Deployment Models (Private, Public, Hybrid), Virtualization Technologies, Cloud Security and Data Privacy |
| MCAD 404 | Elective IV: Computer Vision | Elective | 4 | Image Features and Descriptors, Object Detection and Recognition, Image Segmentation, Motion Analysis, Deep Learning for Computer Vision (CNNs), Applications in Medical Imaging, Augmented Reality |
| MCAD 404 | Elective IV: Internet of Things Lab | Elective | 4 | IoT Device Programming (Arduino, Raspberry Pi), Sensor Interfacing and Data Acquisition, Cloud Platform Integration (AWS IoT, Azure IoT), Home Automation Projects, Data Visualization from IoT Devices |
| MCAD 404 | Elective IV: Quantum Computing | Elective | 4 | Quantum Mechanics Basics (Qubits, Superposition), Quantum Gates and Circuits, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography, Quantum Error Correction |
| MCAD 404 | Elective IV: Human Computer Interaction | Elective | 4 | HCI Principles and Paradigms, Usability Engineering, User Centered Design, User Interface Design and Evaluation, Interaction Styles and Techniques, Cognitive Psychology in HCI |
| MCAD 405 | Deep Learning Lab | Lab | 2 | Implementation of CNNs for Image Classification, RNNs for Sequence Prediction, Using TensorFlow/Keras/PyTorch Frameworks, Hyperparameter Tuning and Model Optimization, Building Simple Generative Models |
| MCAD 406 | Data Mining Lab | Lab | 2 | Data Preprocessing and Data Transformation, Implementation of Association Rule Mining, Classification and Clustering Algorithms, Using Data Mining Tools (WEKA, RapidMiner), Evaluation of Data Mining Models |
| MCAD 407 | Minor Project | Project | 2 | Project Proposal and Literature Survey, Design and Development of a System, Testing and Debugging, Report Writing and Presentation, Problem-solving and Innovation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCAD 501 | Elective V: Information Retrieval | Elective | 4 | IR Models (Boolean, Vector Space), Indexing and Query Processing, Ranking Algorithms and Evaluation, Search Engine Architecture, Web Mining and Link Analysis |
| MCAD 501 | Elective V: Optimization Techniques | Elective | 4 | Linear Programming, Simplex Method, Non-Linear Programming, Dynamic Programming, Integer Programming, Heuristic and Metaheuristic Algorithms, Network Optimization |
| MCAD 501 | Elective V: Software Project Management | Elective | 4 | Project Planning and Scheduling, Risk Management and Mitigation, Software Cost Estimation, Quality Assurance and Control, Agile Methodologies (Scrum, Kanban) |
| MCAD 501 | Elective V: Bio-Informatics | Elective | 4 | Biological Databases and Tools, Sequence Alignment (BLAST, FASTA), Phylogenetic Trees and Analysis, Gene Prediction, Protein Structure Prediction, Drug Discovery and Genomics |
| MCAD 502 | Elective VI: Ethical Hacking & Cyber Forensics | Elective | 4 | Hacking Phases and Penetration Testing, Vulnerability Assessment and Management, Malware Analysis and Reverse Engineering, Digital Evidence Collection and Analysis, Incident Response and Cyber Laws |
| MCAD 502 | Elective VI: GPU Computing | Elective | 4 | GPU Architecture and Parallelism, CUDA Programming Model, OpenCL for Heterogeneous Computing, Parallel Algorithms for GPUs, Performance Optimization Techniques |
| MCAD 502 | Elective VI: Software Testing and Quality Assurance | Elective | 4 | Software Testing Levels (Unit, Integration, System), Test Case Design Techniques, Test Automation and Tools, Software Quality Models (CMMI, ISO), Quality Metrics and Defect Management |
| MCAD 502 | Elective VI: Data Visualization | Elective | 4 | Principles of Data Visualization, Data Storytelling and Infographics, Visualization Tools (Tableau, Power BI), Interactive Visualizations (D3.js), Designing Effective Dashboards |
| MCAD 503 | Comprehensive Viva | Viva | 2 | Overall Subject Knowledge, Understanding of Core Concepts, Project and Internship Learning, Critical Thinking and Problem-Solving, Communication and Presentation Skills |
| MCAD 504 | Industrial Training / Internship | Practical/Project | 4 | Practical Industry Experience, Application of Academic Knowledge, Report Submission and Presentation, Learning Industry Best Practices, Networking and Professional Development |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MCAD 601 | Major Project | Project | 16 | In-depth Research and Problem Definition, Advanced System Design and Architecture, Implementation with Latest Technologies, Comprehensive Testing and Evaluation, Thesis Writing and Public Defense |
