Notes of 3rd year
Unit 1: Cyber Security Fundamentals
Symmetric Cipher model, Substitution techniques, Transposition techniques, Steganography, Block cipher principles, Data Encryption Standard, Confidentiality using symmetric encryption Potential locations for confidentiality attacks, Link versus End-to-End Encryption.
Unit 2: Cyber Security Fundamentals
Public key Cryptosystems Principles, applications and requirements, RSA algorithm Key Management Distribution of Public keys and Secret keys, Diffie-Hellman key exchange. Message Authentication Authentication requirements, Authentication Functions like Message Encryption, Message Authentication code and Hash Function. Requirement for a Hash function, simple hash function, Block chaining techniques, Brute-force attack.
Unit 1: Research Methodology
Meaning of research, objectives of research, motivation in research, types of research Descriptive vs Analytical, Fundamental vs Applied, Quantitative vs Qualitative, Conceptual vs Empirical. characteristics and prerequisites of research, significance of research, research process, Research methods vs Methodology.
Unit 2: Research Methodology
Defining and formulating the research problem, Selecting the problem, Necessity of the defining the problem, Importance of literature review in defining a problem, Primary and secondary sources, Critical literature review, Identifying gap areas from literature review
Unit 3: Research Methodology
Introduction, Moral Philosophy, Nature of moral judgements and reactions, Intellectual honesty and research integrity, Scientific misconducts Falsification, Fabrication and Plagiarism (FFP), Redundant publications duplicate and overlapping publications, Use of plagiarism software tools like Turnitin, Urkund, Drillbit and others.
Unit 4: Research Methodology
Observation and Methods of data collection, Data Processing and Analysis strategies, Data Analysis with Statistical Packages, Hypothesis testing, Method Execution of the research,
Unit 5: Research Methodology
Structure of the research report, Formulation rules for writing the report, Guidelines for presenting tabular data, Guidelines for visual representation, Bibliography, referencing and footnotes Indexing Database, Citation database, Web of Science, Scopus, Research Metrics, Use References Management tools like Mendeley, Endnote, Zotero
Unit 1: Programming with XML
XML overview, Markup languages, Comparison with HTML, Usage, Rules for writing XML, XML syntax, Creating notebook XML, Tree structure of XML, Elements, Attributes and values, Root element, Child element, Nesting of elements, Empty elements, Adding attributes, Elements and Attributes uses, Writing comments, Predefined entities, XML tools, XML validation.
Unit 2: Programming with XML
Document Type Definition, DTD syntax, Creating a DTD for notebook XML, Defining elements with children, Empty element, Number of occurrences, Defining choices, Attribute definitions, Internal and external DTD’s, Validating XML with DTD, Pros and cons of using DTD.
Unit 3: Programming with XML
Introduction to Schema, Namespace, Schema definition, Data types, Simple and complex data types, Attributes definition, Restrictions on values, Creating schema definition for notebook XML, Link and Validate XML with schema.
Unit 4: Programming with XML
Introduction to XSL, Layout of an XSL Document and Templates, Linking XSL to your XML Source, Transforming XML with XSLT, xsl:output, xsl:template, xsl:apply templates, Looping over nodes using xsl:for-each, Apply conditions using xsl:if, Processing and output using xsl:value-of, Sorting nodes, Create a XSLT for notebook and XML file and generate output in different conditions.
Unit 5: Programming with XML
Introduction to XPath, Using XPath to navigate an XML document, Predicates. Sample Project Store the information of students in XML file, validate it using XML schema and display the information of students in HTML using XSLT with proper formatting and conditions like having enrollment number, name start with, having CGPA between, in sorted order, etc.
Unit 1: Design and Analysis of Algorithms
Algorithms, Analysis, Performance issues Time and Space complexity; Asymptotic Notations. Mathematical preliminaries functions & their growth rates; Recurrence relations, Methods for solving recurrences. Elementary Sorting techniques and its analysis Selection, Bubble, Insertion sort
Unit 2: Design and Analysis of Algorithms
Advance sorting techniques and its analysis Heap sort, Radix sort and Bucket sort, Divide and Conquer techniques and its analysis - Binary search, Merge Sort, Quick sort, Strassen’s Matrix multiplication.
Unit 3: Design and Analysis of Algorithms
Greedy problems and its complexity analysis Optimal merge patterns, Huffman coding, Minimum spanning trees, Knapsack problem, Job sequencing with deadlines, Single source shortest path problem - Dijkstra’s Algorithm
Unit 4: Design and Analysis of Algorithms
Dynamic programming problems and its complexity analysis 0/1 Knapsack, Multistage graph, Bellman Ford Algorithm, Reliability design, Floyd-Warshall algorithm, Matrix Chain Multiplication, Longest Common subsequence.
Unit 5: Design and Analysis of Algorithms
Backtracking Approach N-Queen’s problem, Hamiltonian cycle, Graph coloring problem, Sum of Subset problem. Introduction to branch & bound method, examples of branch and bound method like15 puzzle traveling salesman problem, 0/1 knapsack. An introduction to P, NP, NP Complete and NP hard problems.
Unit 1: Machine Learning
Introduction to machine learning, Applications, Classification; Supervised Learning-> Linear Regression Cost function, Gradient descent; Logistic Regression, Nearest-Neighbors, Gaussian function.
Unit 2: Machine Learning
Overfitting and Underfitting, Regularization, Bias and Variance, Decision Trees, Naive Bayes, Support Vector Machines, Kernel Methods.
Unit 3: Machine Learning
Unsupervised Learning Clustering K-means, Dimensionality Reduction PCA, Matrix Factorization and Matrix Completion, Ranking, Recommender System.
Unit 4: Machine Learning
Introduction to Neural Network, Perceptron, Feed forward, Back Propagation, Recurrent Neural Network. Introduction to Python machine learning libraries Keras, Tensorflow and Theano.
Unit 5: Machine Learning
Evaluating Machine Learning algorithms and Model Selection, Ensemble Methods Boosting, Bagging, Random Forests, Deep learning Semi-supervised Learning, Reinforcement Learning.
Unit 1: Agile Development
Understanding Agile Introduction to Agile Project Management, Agile Manifesto, Agile Principles, Agile Benefits Product Development and customers, Development teams etc.
Unit 2: Agile Development
Agile Frameworks Agile approaches, reviewing the big three Lean, Extreme programming and Scrum. Putting Agile in action Environment, Behaviours- Agile roles, New values, Team philosophy.
Unit 3: Agile Development
Working in Agile Planning in Agile, product vision, creating the product roadmap, refining requirement and estimates, release planning and Sprint planning.
Unit 4: Agile Development
Managing in Agile Managing Scope and procurement, managing time and cost, team dynamics and communication, managing quality and risk
Unit 5: Agile Development
Ensuring Agile Success Building a foundation- Commitment, choosing the right project team members-Development team, scrum master etc. Being a change agent, Key benefits and key resources for agile project management.
Unit 1: Compiler Design
Compiler structure Pass Structure of compiler, Translators, Interpreter, Assembler, Phases of Compilers, Symbol Table, Error Handling, Lexical Analyzer Role of Lexical Analyzer, Specification of tokens, Recognition of tokens and input Buffering, The Syntactic Specification of Programming Languages, Cross Compiler, bootstrap Compiler.
Unit 2: Compiler Design
Ambiguous Grammar, LL(0) and LL(1) grammar, Parsing, Basic Parsing Techniques Top Down parsers, Recursive Descent Parsers, First() and Follow(), Recursive and Non- Recursive Predictive
Unit 3: Compiler Design
LR Grammar, Operator Grammar, Bottom Up Parsing Operator precedence parsing, LR(0) parsers, Construction of SLR, Canonical LR and LALR parsing tables.
Unit 4: Compiler Design
Syntax Directed Definition, Translation Scheme, Synthesized and inherited attributes, dependency graph, Construction of syntax trees, S-attributed and L-attributed definitions, Three address codes, quadruples, triples and indirect triples, Translation of assignment statements.
Unit 5: Compiler Design
Storage organization, activation trees, activation records, allocation strategies, Parameter passing symbol table, dynamic storage allocation, Basic blocks and flow graphs, Optimization of basic blocks, Loop optimization, Global data flow analysis, Loop invariant computations.
Unit 1 : FMEA
Definition, characteristics and importance of management Management Science or Art, Difference between Management and Administration, Levels of management, Functions of Management, Managerial Roles, Managerial skills and competencies; Decision Making Definition, process and types; Decision making under certainty, uncertainty and risk Cross cultural issues in management and challenges
Unit 2 : FMEA
Introduction to Marketing Definition, importance, function and scope of marketing, Core Concepts of marketing. Marketing concepts and orientations, Marketing environment, Marketing-mix, Holistic marketing concept, Customer Relationship Management (CRM).Introduction to Human Resource Management (HRM) Nature, Scope. Objectives and Functions Role of HR manager, Process and need for Human Resource Planning, Human resource policies, Changing role of Human Resource in India, Globalization and its impact onHuman Resource.
Unit 3 : FMEA
Introduction to Economics Definition, nature, scope and significance; Difference between micro and macro economics; Time value of money, Law of diminishing marginal utility; Theory of Demand and Supply, Price elasticity of demand; Meaning and types of costs, Law of variable proportions; Types of market structure; National income and related aggregates; Meaning and types of Inflation; Meaning and phases of business cycle.
Unit 4 : FMEA
Accounting Principles and Procedure, Double entry system, Journal. Ledger, Trail Balance, Cash Book; Preparation of Trading, Profit and Loss Account; Balance sheet; Cost Accounting Introduction. Classification of costs, Methods and Techniques of costing, Cost sheet and preparation of cost sheet; Breakeven Analysis Meaning and its application.
Unit 5 : FMEA
Introduction of Business Finance Meaning, Definition of Financial Management, Goals of Financial Management (Profit Maximization and Wealth Maximization), Modern approaches to Financial Management — (Investment Decision, Financing Decision and Dividend Policy Decisions).
Unit 1 : Cloud Computing
Introduction to cloud computing, characteristics of cloud computing as per NIST, cloud reference model, application of cloud computing ECG analysis, protein structure prediction, cloud deployment models.
Unit 2 : Cloud Computing
Virtualization, virtualization advantages, Full virtualization, para-virtualization, hypervisors. Cloud interoperability, cloud service management, cloud analytics, Cloud broker, Capex, Opex, cloud architecture.
Unit 3 : Cloud Computing
Platform as a service. Infrastructure as a service, software as a service, Desktop as a service, Backup as a service, DRaaS, Introduction to SLA, SLA lifecycle, SLA management,Business continuity plan.
Unit 4 : Cloud Computing
Cloud security fundamentals, vulnerability assessment, security architecture, identity management and access control, data at rest, data in flight, data in motion, security in virtualization.
Unit 5 : Cloud Computing
Cloud application development platforms, Xen hypervisor, AWS, Google app engine,open stack.
Unit 1 : NO SQL
Understanding NoSQL Databases, History of NoSQL, Features of NoSQL, Scalability, Cost, Flexibility, NoSQL Business Drivers, Classification and Comparison of NoSQL Databases, Consistency - Availability - Partitioning (CAP), Limitations of Relational Databases, Comparing NoSQL with RDBMS, Managing Different Data Types, Columnar, Key-Value Stores, Triple and Graph Stores, Document, Search Engines, Hybrid NoSQL Databases, Applying Consistency Methods, ACID, BASE, Polyglot persistence
Unit 2 : NO SQL
The Technical Evaluation, Choosing NoSQL, Search Features, Scaling NoSQL, Keeping Data Safe, Visualizing NoSQL, Extending Data Layer, Business Evaluation, Deploying Skills, Deciding Open Source versus commercial software, Business critical features, Security
Unit 3 : NO SQL
Introduction to Key-Value Databases, Key Value Store, Essential Features, Consistency, Transactions, Partitioning, Scaling, Replicating Data, Versioning Data, how to construct a Key, Using Keys to Locate Values, Hash Functions, Store data in Values, Use Cases, Introduction to Document Databases, Supporting Unstructured Documents, Document Databases Vs. Key-Value Stores, Basic Operation on Document database, Partition, Sharding, Features, Consistency, Transactions, Availability, Scaling, Use Cases.
Unit 4 : NO SQL
Introduction to Column Family Database, Features, Architectures, Differences and Similarities to Key Value and Document Database, Consistency, Transactions, Scaling, Use Cases. Introduction to Graph Databases, Advantages, Features, Consistency, Transactions, Availability, Scaling, Graph & Network Modelling, Properties of Graphs and Nodes, Types of Graph, Undirected and directed Graph, Flow Network, Bipartite Graph, Multigraph, Weighted Graph.
Unit 5 : NO SQL
Common Feature of Search Engine, Dissecting a Search Engine, Search versus query, Web crawlers, Indexing, Searching, indexing Data Stores, Altering, Using Reverse queries, Use Cases, Types of Search Engine, Elastic Search
Unit 1: Data Science
Introduction to Data Science, Definition and description of Data Science, history and development of Data Science, terminologies related with Data Science, basic framework and architecture, importance of Data Science in today’s business world, primary components of Data Science, users of Data Science and its hierarchy, overview of different Data Science techniques.
Unit 2: Data Science
Sample spaces, events, Conditional probability, and independence, Random variables, Discrete and Continuous random variables, densities and distributions, Normal distribution and its properties, Introduction to Markov chains, random walks, Descriptive, Predictive, and prescriptive statistics, Statistical Inference, Populations and samples, Statistical modelling.
Unit 3: Data Science
Exploratory Data Analysis and the Data Science Process - Basic tools (plots, graphs, and summary statistics) of EDA - Philosophy of EDA - The Data Science Process - Case Study
Unit 4: Data Science
Data Visualization Basic principles, ideas and tools for data visualization, Examples of inspiring (industry) projects, Exercise create your own visualization of a complex dataset.
Unit 5: Data Science
NoSQL, use of Python as a data science tool, Python libraries, SciPy and sci-kitLearn, PyBrain, Pylearn, Matplotlib, challenges and scope of Data Science project management.
Unit 1 : Computer Network
MAC Sublayer, Static and Dynamic Channel Allocation in LAN, MAC protocols-ALOHA and Slotted ALOHA, CSMA, CSMA/CD, CSMA/CA, Collision-Free protocols, Limited Contention Protocols, Ethernet-Ethernet Cabling, Frame Format, Binary Exponential Back-off Algorithm, Ethernet Performance, Fast Gigabit Ethernet, MAC address
Unit 2 : Computer Network
Internetworking, Tunneling, Fragmentation and Reassembly, IP protocol, IPv4 Addresses, Subnet Addressing, Subnet Mask, Supernetting CIDR, NAT, ICMP-header, message type, trace route, ARP & RARP, BOOTP and DHCP, Address allocation, configuration & packet format, OSPF and BGP, Comparative study of IPv4 & IPv6
Unit 3 : Computer Network
Network Layer, Design Issues, Routing algorithms, Dijkstra's algorithm, Bellman-ford algorithm, Link State Routing, Hierarchical Routing, Congestion Control Algorithms, General Principles of Congestion control, Prevention Policies, Congestion Control in Virtual-Circuit Subnets, Congestion Control in Datagram subnets, QOS-techniques for achieving good QOS, Traffic Management, Integrated and Differentiated Services, RSVP
Unit 4 : Computer Network
Transport Layer, Design Issues, Transport Service Primitives, Socket Programming, TCP, Connection Management, Reliability of Data Transfers, TCP Flow Control, TCP Congestion Control, TCP Header Format, TCP Timer Management, UDP, Header Format, RPC, RTP, Session layer, Authentication, Authorization, Session layer protocol (PAP, SCP, H.245)
Unit 5 : Computer Network
Presentation Layer, Data conversion, Character code translation, Presentation layer protocol, Application Layer, WWW Architectural Overview, URL-Static and Dynamic Web, FTP, SSH, Email- Architecture and Services, SMTP, DNS-Name System, Resource Records, Name Servers, Network Management (SNMP)
Unit 1 : Internet Of Things
Introduction Definition, Characteristics of IoT, IoT Architectural view, Physical design of IoT, IoT Protocols, Communication Models of IoT, IoT Communication APIs, IoT Enabling Technologies.
Unit 2 : Internet Of Things
IoT and M2M Machine-to-Machine (M2M), Difference between M2M and IoT, SDN (Software Defined Networking) and NFV (Network Function Virtualization) for IoT, Data Storage in IoT, IoT Cloud Based Services.
Unit 3 : Internet Of Things
IoT Platform Design Methodology Specifications of Purpose and Requirement, Process, Domain Model, Information Model, Service, IoT Level, Functional View, Operational View, Device and Component Integration, Application Development.
Unit 4 : Internet Of Things
Security issues in IoT Introduction, Vulnerabilities, Security requirements and threat analysis, IoT security Tomography, layered attacker model, identity management and establishment, access control.
Unit 5 : Internet Of Things
Application areas of IoT Home Automation, smart lighting, home intrusion detection, smart cities, smart parking, environment, weather monitoring system, agriculture.
Unit 1 : Software Engineering
Introduction to Software Engineering, Definition, Process Evolution, and Myths of Software Engineering Generic Process Model Framework and Process Models - Waterfall Model Incremental Model Evolutionary Model Spiral Model Component-Based Development (CBD) Model Rational Unified Process (RUP)
Unit 2 : Software Engineering
Requirement Analysis and Stakeholders, Elicitation Techniques, Requirement Modeling, Use Cases, Activity Diagrams, Swimlane Diagrams, Data Modeling, Data Flow Diagrams, Overview of Class-Based Modeling, Requirement Tracking
Unit 3 : Software Engineering
Principles of Software Design, Design Concepts, Abstraction, Architecture, Modularity, Relationships, Design Models, Component Design, User Interface Design, Configuration Management
Unit 4 : Software Engineering
Software Quality, Approaches for Quality Assurance, Software Testing, Verification and Validation, Types of Testing, Risk Assessment, Risk Mitigation, Monitoring and Management
Unit 5 : Software Engineering
Software Metrics, Process Metrics, Product Metrics, Function-Oriented Metrics, Software Project Estimation, Function Point-Based Metrics, COCOMO Models, Project Scheduling, Effort Distribution
Unit 1 : Software Engineering
Introduction to Software Engineering, Definition, Process Evolution, and Myths of Software Engineering Generic Process Model Framework and Process Models - Waterfall Model Incremental Model Evolutionary Model Spiral Model Component-Based Development (CBD) Model Rational Unified Process (RUP)
Unit 2 : Software Engineering
Requirement Analysis and Stakeholders, Elicitation Techniques, Requirement Modeling, Use Cases, Activity Diagrams, Swimlane Diagrams, Data Modeling, Data Flow Diagrams, Overview of Class-Based Modeling, Requirement Tracking
Unit 3 : Software Engineering
Principles of Software Design, Design Concepts, Abstraction, Architecture, Modularity, Relationships, Design Models, Component Design, User Interface Design, Configuration Management
Unit 4 : Software Engineering
Software Quality, Approaches for Quality Assurance, Software Testing, Verification and Validation, Types of Testing, Risk Assessment, Risk Mitigation, Monitoring and Management
Unit 5 : Software Engineering
Software Metrics, Process Metrics, Product Metrics, Function-Oriented Metrics, Software Project Estimation, Function Point-Based Metrics, COCOMO Models, Project Scheduling, Effort Distribution