MCA

Program Scheme for Master of Computer Applications (MCA)

Course Code Course Title

 

 Credits Sem Th/OP
LMC0101 Object Oriented programming Using C++ 3 1 Th
LMC0102 Computer Architecture 3 1 Th
LMC0103 Discrete Mathematics 3 1 Th
LMC0104 Enterprise Resource Planning 3 1 Th
LMC0105 Database Management System 3 1 Th
LMC0121 C++ Programming Practical 4 1 OP
LMC0122 DBMS Practical 4 1 OP
LMC0106 Computer Fundamentals for CS* NA 1 Th
LMC0107 Basic Mathematics for CS* NA 1 Th
SEM I TOTAL CREDIT 23  

Th -Theory; OP-Practical; Pro-Project; T-Total; Crd –Credit

I Semester

 

 

 

 

 

 

Oriented Object Programming Using C++

Course Code:LMC0101 Course Title: Object Oriented Programming Using C++                                                                  (3 Credits)
Course Objectives:-

Ø  To gain a foundational understanding of object-oriented programming (OOP) concepts.

Ø  To master the use of classes and inheritance in C++ for building efficient and reusable code.

Ø  To explore the role of polymorphism and dynamic binding in creating flexible software solutions.

Ø  To develop the ability to write, compile, and debug C++ programs effectively.

Ø  To apply object-oriented programming principles to other OOP languages.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Overview of oops, Differences between OOP and Procedure Oriented programming, Structure of a C/C++ program, Differences between C/C++. Understand the key principles of OOPs. BTL1: Remembering, BTL 2: Understand.
2 Data Types:  Data Types in C++, Identifier and Keywords, Constants, C++ Operators, Type Conversion, Variable declaration, statements, Expressions, Features of iostream.h. Identify and use C++ data types, operators, variables and expressions effectively. BTL3: Apply
3 Input and Output: Input and Output basics, Conditional Expression, Loop statements, Jump statements. Handle basic input/output, use conditional, loop, and jump statements.BTL3 : Apply
4 Data Abstraction: Class Definition, Controlling access to other functions, Memory Allocation, Different Types of Constructors, Destructor, Dynamic Creation and Destruction of Objects. Define and implement classes, control access, and manage memory.BTL3 : Apply
5 Polymorphism and Inheritance: Overloading functions and operators, Overloading New and Delete operators. Derived Classes, Syntax of Base Class, Types of Inheritance, Overloading Inherited Member Function. Implement inheritance and function operator overloading in C++.  BTL3 : Apply
6 Virtual functions: Static and Dynamic bindings, virtual functions, dynamic binding through virtual functions, virtual function call mechanism. Understand static and dynamic bindings and implement virtual functions. BTL2: Understand
7 Polymorphism: implications of polymorphic use of classes, virtual destructors, calling virtual functions in a base class constructor. Use virtual destructors and call virtual functions in base class constructors. BTL3: Apply
8 Standard I/O: Standard I/O Using C functions, stream I/O in C++, formatted I/O, File I/O, generic Classes in C++. Implement formatted and file I/O using C++ streams and generic classes. BTL3: Apply
9 Templates: Necessity of Templates , generic classes  using  Macros,  class  templates,  Function  Templates,  Advantages  of Templates. Create and use class and function templates for generic programming. BTL3: Apply
10 Exception: Benefits of exception handling, troubles with standard C functions, exception handling mechanism in C++. Evaluate the use of exception handling to enhance programs and manage errors efficiently.
BTL5: Evaluate
11 File Handling: hierarchy of file stream classes, opening and closing files, file modes, testing for errors. Analyze the differences between sequential and random access file handling. BTL4 : Analyze
12 File Pointers: File pointers & their manipulations, ASCII & Binary files, sequential access files & random access files. Evaluate different techniques for file manipulation using pointers. BTL5: Evaluate
Textbook References: –

  1. Schildt Herbert, C++: The Complete Reference, Tata Mcgraw Hills.
  2. Balagurusamy E., Object Oriented Programming With C++, Tata Mcgraw Hills.

Other References:-

  1. Lafore Robert, Object-Oriented Programming in C++, Galgotia publications.
  2. Prata Stephen, C++ Primer Plus, Pearson publication.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

Computer Architecture

Course Code: LMC0102 Course Title: Computer Architecture (3 Credits)
Course Objectives : –

Ø  To gain a thorough understanding of the basic structure and operation of a digital computer.

Ø  To study the operation of the arithmetic unit, including algorithms and implementation of fixed-point and floating-point arithmetic operations (addition, subtraction, multiplication, and division).

Ø  To explore various methods of communication with I/O devices and standard I/O interfaces.

Ø  To understand the hierarchical memory system, including cache memory and virtual memory.

Ø  To learn the organization and functionality of the control unit, arithmetic and logical unit, memory unit, and I/O unit in a computer system.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Von-Neuman   architecture, register transfer and micro operations: register transfer   language,   arithmetic   micro-operations,   logic   micro-operations,   shift   micro- operations, bus and memory transfers. Understand the basic concepts of Von-Neumann architecture, register transfer, and micro-operations including arithmetic, logic, and shift operations.BTL1: Remember
2 Computer organization: Computer organization and design: instruction cycle, computer registers, common bus system, computer instructions, addressing modes, design of a basic computer. Understand and apply the instruction cycle, computer registers, common bus systems, addressing modes, and design principles. BTL 1: Remember, BTL 2:Understand
3 Central  Processing  Unit:  General  register  organization,  stack  organization,  instruction formats,  data  transfer  and  manipulation,  program  control,  Reduced Instruction Set Computer,  Complex Instruction Set Computer characteristics Analyze the general register organization, stack organization, instruction formats, data manipulation, and program control. Compare RISC & CISC.BTL 3: Apply and BTL4: Analyze
4 Pipeline and  vector  processing:  Pipeline  structure,  speedup,  efficiency,  throughput  and bottlenecks, arithmetic pipeline and instruction pipeline Apply concepts of pipeline structure, speedup, efficiency, throughput, and bottlenecks in pipeline and instruction pipelines. BTL3 : Apply
5 Computer Arithmetic: Hardware implementation of addition and subtraction, multiplication algorithms, Booths   multiplier,   array   multiplier,   division   algorithms. Apply hardware-based algorithms for all operations (including Booth’s and array multipliers) in computer systems. BTL 3: Apply
6 Floating Point arithmetic: Floating point representation, add, subtract, multiplication, division. Understand floating point representation and perform operations.BTL3 : Apply
7 Memory & I/O Organization: Memory hierarchy, RAM, ROM chips, memory address map, memory connection, associative memory, cache memory Evaluate the memory hierarchy, and associative memory; understand the memory address map and memory connections. BTL 5  : Evaluate
8 Virtual memory: paging and segmentation, auxiliary memory: magnetic disks, magnetic tapes, Input-output interface, Asynchronous data transfer, modes of transfer, priority interrupt, Direct Memory Access, input-output processor. Analyze paging, segmentation, auxiliary memory, and input-output interfaces; understand modes of transfer, priority interrupts, DMA, and IOP .BTL3 : Apply and BTL 4 Level:  Analyze
9 Hardwired  control,   microprogrammed control:  Microinstruction, microprogram sequencing, wide-branch addressing, microinstruction with next-address field, prefetching microinstruction Analyze and compare hardwired control and microprogrammed control techniques; understand microinstruction sequencing and branching.BTL3:Apply & BTL4:Analyze
10 Input-Output Organization: Input-output devices and characteristics, bus interface, data transfer techniques, I/O interrupts. Analyze basic Input output organization & evaluate transfer techniques. BTL 4: Analyze & BTL 5 : Evaluate
11 Advance computer Architecture: Overview of parallel processing and pipelining, Superscalar processors, study and comparison of uni-processors and parallel processors. Understand the basics of parallel processing, pipelining, and superscalar processors.BTL3: Apply
12 Advance Topics: Case study on Pentium series processors. Evaluate the architecture of Pentium processors & analyse its advancements. BTL  4 : Analyse  and BTL 5 : Evaluate
Textbook References: –

  1. M. Morris Mano, Computer System Architecture, Pearson Education.
  2. Hayes J. P., “Computer Architecture and Organization”, McGraw- Hill, New York.

Other References:-

  1. Hwang K., “Advanced Computer Architecture: Parallelism, Scalability and Programmability”, McGraw-Hill, New York.
  2. William Stallings, Computer Organization and Architecture – Designing for Performance, Pearson Education.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

         Discrete Mathematics

Course Code : LMC0103 Course Title : Discrete Mathematics (3 Credits)
Course Objectives :-

Ø  To introduce the basics of mathematical sets, relations, and functions.

Ø  To familiarize students with partially ordered sets and their properties.

Ø  To impart knowledge on lattices, counting techniques, and important theorems.

Ø  To understand and apply basic concepts of algebraic structures, predicate, and propositional logic.

Ø  To understand graph theory concepts and solve related problems.

Course Contents

Unit No. Unit Description Learning Outcome
1 Sets, Relations: Set theory, combination of sets, multi-sets, ordered pairs, set identities. Recall basic set theory concepts and operations. BTL 1 : Remembering
2 Relations: Definition of Relations, operations on relations, properties of relations, composite relations, equality of relations, order of relations. Define & perform operations on relations. BTL 1: Remembering
3 Functions: Definition of functions, classification of functions, operations on functions, recursively defined functions. Recall function types. BTL 1: Remembering
4 Partially Ordered Sets: Definition, combination of partial order sets, Hasse diagram Explain & construct Hasse diagrams for partially ordered sets. BTL2: Understanding
5 Lattices: definition, properties of lattices: bounded, complemented, modular and complete lattice, morphisms of lattices. Apply lattice properties and morphisms. BTL 3 : Applying
6 Algebraic Structures: Groups, subgroups and order, cyclic groups, cossets, Apply group theory concepts and operations. BTL 3 : Applying
7 Theorems – Lagrange’s theorem, normal subgroups, permutation and symmetric groups, group homo morphisms, rings, fields, basics of linear algebra, integers modulo . Analyze & apply advanced theorems in algebraic structures. BTL 4 : Analyzing
8 Counting: Counting principles, permutations, combinations, summations, principle of inclusion and exclusion, pigeon-hole principle, generating functions and recurrence relations. Apply counting principles & solve combinatorial problems. BTL 3: Applying.
9 Propositional Logic: Proposition Logic basics , well-formed formula, truth  tables in propositional Logic , tautology, satisfiability, contradiction, algebra of proposition, theory of inference, natural deduction. Apply logic rules & construct truth tables. BTL 3: Applying.
10 Predicate logic: first order predicate, well-formed formula of predicate, quantifiers, and inference theory of predicate logic. Understand & apply first-order logic & quantifiers.

BTL2:Understanding
11 Graph Theory: Graphs, Types of graphs, Subgraphs, Eulerian chains and Cycles. Analyze types of graphs and understand Eulerian paths/cycles. BTL 4: Analyzing
12 Algorithms: Hamiltonian chains and cycles, graph traversal algorithms, chromatic number, planar graph. Analyze & evaluate graph algorithms. BTL 4: Analyzing & BTL 5 : &Evaluating
Textbooks References:-

  1. Bernard  Kolman,   Robert  C.,   BusbSharon,   Cutler Ross,   Discrete Mathematical  Structures,PHI.
  2. KennethH.Rosen,DiscreteMathematicsandItsApplications,TataMcGraw-Hill.

Other Reference :-

  1. SwapanKumarChakraborty,BikashKantiSarkar,DiscreteMathematics,OxfordHigher  Education.
  2. C.L.Liu,D.P.Mohapatra, Elements of Discrete Mathematics A Computer Oriented  Approach,TataMcgraw-Hill.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding;BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

Enterprise Resource Planning

Course Code: LMC0104 Course Title: Enterprise Resource Planning (3 Credits)
Course Objectives :-

Ø  To gain knowledge of the ERP implementation cycle.

Ø  To develop awareness of the core and extended modules of ERP systems.

Ø  To understand how ERP systems contribute to multidimensional growth in business organizations.

Ø  To grasp the activities involved in the ERP project management cycle.

Ø  To explore emerging trends and developments in ERP technologies and their impact on businesses.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Overview of enterprise systems – Evolution – Risks and benefits – Fundamental technology. Understand the evolution, risks, benefits  fundamental of ERP.BTL1: Remember, BTL2: Understand
2 ERP Issues: Issues to be consider in planning design and implementation of cross functional integrated ERP systems. Analyze key issues in planning and designing.BTL4: Analyze
3 ERP solutions: Overview of ERP software solutions- Small, medium and large enterprise vendor solutions. Evaluate ERP software solutions for small, medium, and large enterprises. BL5: Evaluate
4 Functional modules: BPR and best business practices – Business process Management, Functional modules. Apply business process reengineering & best practices to functional modules in ERP.
BTL3: Apply
5 Planning Evaluation : Planning Evaluation and selection of ERP systems -Implementation life cycle – ERP implementation Evaluate the planning, selection, & implementation life cycle of ERP systems.BTL4: Analyze, BTL5: Evaluate
6 Methodology and Frame work: Methodology and Frame work, Training – Data Migration. People Organization in implementation-Consultants, Vendors and Employees. Understand ERP implementation methodology, data migration, and the roles of consultants, vendors, and employees. BTL2: Understand
7 Post implementation: Maintenance of ERP- Organizational and Industrial impact; Success and Failure factors of ERP Implementation. Assess the maintenance of ERP systems and identify success & failure factors in implementation.BTL3: Apply,
8 Emerging trends on ERP: Extended ERP systems and ERP add-ons -CRM, SCM, Business analytics. Evaluate extended ERP systems & add-ons like CRM, SCM, and Business Analytics.BTL5: Evaluate
9 Future trends in ERP: Future trends in ERP systems-web enabled, Wireless technologies, cloud computing. Apply knowledge of future ERP trends and cloud computing. BTL3: Apply
10 Functional modules of ERP: Functional modules of ERP software, Implementation of ERP. Evaluate the functional modules of ERP systems.BTL5: Evaluate
11 Implementation: Success and failure in implementation – factors responsible for it. Operation and Maintenance of an ERP system. Understand the failure of ERP implementation.BTL1: Remember, BTL2: Understand
12 ERP CASE STUDIES: ERP Case Studies E-Commerce to E-business E-Business structural transformation, Flexible Business Design, Customer Experience, Create the new techno enterprise, Integrate Sales and Service, Integrated Enterprise applications Analyze ERP case studies related to e-commerce, business transformation, and integrated enterprise applications.BTL3: Apply, BTL6: Create
Textbook References: –

  1. S. Sadagopan, “Enterprise Resource Planning”, Tata McGraw Hill.
  2. Bajaj, Kamlesh K. and Nag, Debjani, E-Commerce: The Cutting Edge of Business, TataMcGraw-Hill Publishing Company

Other References:-

  1. Sinha P. Magal and Jeffery Word,Essentials of Business Process and Information System,
    Wiley India.
  2. Jagan Nathan Vaman, ERP in Practice, Tata McGraw-Hill.

(Bloom’s Taxonomy level:  BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

Database Management Systems

Course Code: LMC0105 Course Title: Database Management Systems (3 Credits)
Course Objectives :-

Ø  To analyse various database models and entity-relationship models.

Ø  To understand the architecture and processes involved in database development.

Ø  To design entity-relationship (E-R) diagrams for database representation.

Ø  To develop proficiency in using Structured Query Language (SQL) for complex queries.

Ø  To understand transaction processing control and the role of DBMS, RDBMS, and ODBMS in organizational environments.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Databases and database users, database system concepts and architecture, Advantages of database management systems Understand database concepts, DBMS architecture. BTL1: Remember , BTL2: Understand
2 Data Modeling: data modeling using the Entity-Relationship (ER) model, Enhanced Entity Relationship model. Objective: Apply ER and EER models to design database structures.BTL3: Apply
3 Relational Data Model: Concepts, relational model constraints, relational database schemas, update operations, dealing with constraint violations. Apply relational model concepts and handle constraint violations. BTL3: Apply, B TL4: Analyze
4 Relational Algebra: Introduction, relational algebra operations, relational calculus, mapping ER model to relational model. Perform relational algebra operations and map ER models to relations.BTL4 : Analyze, BTL5 : Evaluate
5 Structured Query Language (SQL): SQL data definition and data types, specifying constraints, schema change statements, basic queries, complex queries, update statements, views. Write SQL queries for data manipulation, updates, and schema changes.BTL3: Apply, BTL6: Create
6 Database Design: Functional dependencies and normalization of relational databases, First Normal Form (1NF), 2NF, 3NF, Boyce-Codd normal form (BCNF), multivalued dependency, 4NF. Apply normalization techniques and functional dependencies for database design.
BTL4: Analyze, BTL6: Create
7 Transaction Processing: Introduction, properties, recoverability, serializability. Examples of recoverability and serializability. Understand transaction properties and their importance in databases.
BTL2: Understand
8 Concurrency control techniques: Two phase locking protocol in concurrency control techniques. Evaluate the Two-Phase Locking protocol.BTL5: Evaluate
9 Database recovery techniques: Recovery concepts, recovery techniques based on deferred and immediate update. Analyze and apply recovery techniques based on update types.BTL4 :Analyze
10 Recovery Algorithm: recovery techniques based on deferred and immediate update, the ARIES recovery algorithm. Understand the ARIES recovery algorithm for database consistency.BTL2: Understand.
11 Object and Object-Relational Databases: Object database standards and design, object relational systems, object relational features, object database extensions to SQL, ODMG object model and the object definition language (ODL) Understand and differentiate object and object-relational databases.BTL1:Remember, BTL2: Understand
12 Database Security: Vulnerabilities in database, authentication, SQL injections, Encryptions & public Key infrastructures, privacy issues and preservation process. Analyze the importance of secure database systems using authentication, encryption, and privacy techniques.BTL4 : Analyze
Textbook References:-

1. R.Elmasri &S.B.Navathe, Fundamentals of DBMS, Pearson     Education.

2. R. Ramakrishnan, Database Management Systems, Tata McGraw Hill.

Other References:-

  1. G.W. Hansen, and J.V. Hansen, Database Management and Design, Prentice Hall India.
  2. Korth and Schilberschatz, Database System Concepts, Tata McGraw Hill.
  3. C.J. Date, Database Systems, Tata McGraw Hill.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

Computer Fundamentals

 

Course Code: LMC0106 Course Title: Computer Fundamentals(Credits-NA)
Course Objectives :-

Ø To identify computer hardware components and describe their function

Ø To describe the essential elements of the computer’s architecture and discuss how this architecture functions.

Ø To evaluate and describe the characteristics and representations of data, and interpret and compare data in different representations.

Ø To understand and identify operating system features and internet

Ø To understand the fundamentals of computers like basic working and structure of computer, data representation, and office automation and networking

 

Course Contents

 

Unit No. Unit Description Learning Outcome
1 Introduction to Computers: Defining Computers, features, History, Generations of computer, Components, classification of computers. Recall the basics of computers.BTL 1: Remembering
2 Functional Units of Computer Processing System: Processing Unit, Input Devices & Output Devices, Hardware, Memory and secondary storage devices, Information Concept and Processing. Understand the role of CPU, input/output devices, Type of memory. BTL 2: Understanding
3 Data Communication and Networks: Data Communication Definition, Network, Types of Network – LAN, MAN, SAN, WAN, Network Structure- Star Network, Bus Network, Mesh Network. Understand data communication, network types (LAN, MAN, WAN), and topologies.
BTL 2: Understanding
4 Data representation and Number System: Representation of Data, Bits and Bytes,: Decimal, Binary, Octal, Hexadecimal, Conversions b/w number system, Arithmetic Operations in Binary Number, ASCII and BCD Codes. Convert between number systems and perform binary arithmetic.BTL3: Applying
5 Introduction to Boolean Algebra: AND, OR, NOT, NAND gates, Adder, Subtractor. Apply logic gates and design basic circuits. BTL 3: Applying
6 Classification of languages: Assembly Language, High Level Language, Machine Languages, Compiler, Interpreter, Assembler. Understand programming languages and translators.
BTL 2: Understanding
7 Algorithms: Representation of Algorithms, Structure of Algorithms, Properties of Algorithms, Analysis of an algorithm, Need of Algorithms. Analyze algorithm structure, properties, and importance.
BTL 4: Analyzing
8 Internet and its Applications: Internet, History and Importance of internet, History of email, Advantage of email, Email program, Email account. Email address, parts of email messages. Computer Threats: Virus, Worm, Trojan horse, Malware. Explain Internet and email basics, and identify common computer threats. BTL 2: Understanding
Textbook References: –

1.    Rajaraman, V., Introduction to Information Technology, PHI.

2.    Hall, J.A., “Accounting Information System”, South-Western College Publishing.

3.    P.K.Sinha ,P.Sinha, Computer Fundamentals, 6th ed., BPB Publications.

Other Reference:-

1.    Boockholdt, J.L., “Accounting Information System: Transaction Processing andControl”, Irwin Mcraw-Hill.

2.    Gelinas, Ulric J., and Steve G. Sutton, Accounting Information System, South Western Thomson Learning.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Computer Fundamentals – Practical

Course Code: LMC0106 Course Title: Computer Fundamentals Practical (Credits-NA)
Course Objectives :-

Ø  To create, edit, format and perform basic operations in MS Word.

Ø  To use Mail Merge to automate document generation.

Ø  To create and manage worksheets, and use basic functions like AutoFill and sum calculation to organize data efficiently.

Ø  To utilize themes and templates in PowerPoint, and create engaging slideshows for effective communication.

Ø To design and organize a database using MS Access

Course Contents

Unit No. Unit Description Learning Outcome
1 MS word – part-1: Introduction, Objectives, What is Word-Processing, Important Features of MS-Word, Main menu option. Creating Documents, Entering Text In the documents, Editing Operations. Create, edit, and perform basic operations in MS Word.BTL 1: Remembering and BTL 6 :Creating
2 MS word–part-2: Formatting a Document: Default and Customized Format, Character Formatting, Line Spacing, Alignment, Boarding and Shading, Page Breaks, Columns, Changing Case, Adding and Removing Numbers. Mail Merge concept. Apply document formatting and use Mail Merge for document automation. BTL 2: Applying

 
3 MS Excel – part-1:  Introduction to Microsoft Excel. Starting excel, Component of Excel window, changing the active cell, Creating, save and Deleting a worksheet, Switching between the worksheet, creating a series. Navigate the Excel interface, create worksheets, and manage data. BTL 2: Understanding

 
4 MS Excel–part-2: Calculate the Sum, AutoCorrect and AutoFill, Display Formulas, Manage Page Layout. Use Excel functions and manage page layout. BTL 2: Applying
5 MS Power point introduction: Introduction, Using themes and templates, Applying team, and templates, Changing PowerPoint different views, Slideshow view, Modifying a background, Project. Use themes, templates, and modify PowerPoint views for presentations. BTL 2: Applying
6 MS Access: Database basic, Use Microsoft Access, Planning the Database, Design Rules, Organizing Data, and Data is broken down into Smallest Logical Parts, Descriptive Field Names, Unique Field Names, Unique Records, and Basic Access Objects. Design and organize databases with MS Access, using correct field names and records.
BTL 2: Understanding

 

 

Basic Mathematics

 

Course Code: LMC0106 Course Title: Computer Fundamentals(Credits-NA)
Course Objectives :-

Ø  To introduce students to the fundamental concepts of algebra, including functions, permutations, combinations, and matrix operations, enabling them to solve real-world problems in science and engineering.

Ø  To provide students with a deep understanding of determinants, their properties, and applications, along with differential calculus techniques, preparing them for advanced applications in engineering.

Ø  To equip students with the knowledge of integration methods and partial fraction decomposition, enabling them to solve complex integrals in various scientific and engineering contexts.

Ø  To introduce students to basic statistics, focusing on frequency distributions, measures of central tendency, correlation analysis, and statistical techniques for data interpretation.

Ø  To ensure that students can apply the theoretical concepts learned in algebra, matrix theory, calculus, and statistics to solve real-world problems in engineering and related disciplines.

 

Course Contents

Unit No. Unit Description Learning Outcome
1 Algebra: General introduction of function of single variable, factorial functions, Permutations and Combinations and related problems. Understand basic algebraic concepts. BTL: Understanding
2 Matrix theory I: Definition of matrix, different types of matrices (rectangular matrix, square matrix, column matrix , row matrix, unit matrix, diagonal matrix, null matrixes, symmetric matrix, skew symmetric matrix). Categorize different types of matrices. BTL: Remembering
3 Matrix TheoryII: Algebra of matrices, addition, subtraction and multiplication of matrices, inverse of matrix. Perform matrix operations
BTL: Applying
4 Determinant I: Definition, formation of determinant by system of equations,properties of determinants, multiplication of determinants. Understand the concept of determinants BTL2: Understanding
5 Determinant II:Expansion of determinants up to third order by row and column minors and co-factors. Apply determinant techniques in solving engineering problems.
BTL: Applying
6 Differential Calculus I:Introduction and basic definition of differentiation, derivatives of standard functions. Solve calculus problems in various fields. BTL1: Remembering
7 Differential Calculus II:Product rule of derivation, derivative of quotient of two functions, derivation of function of function, chain rule, derivation by substitution. Apply differentiation techniques such as the product rule, quotient rule etc. BTL3: Applying
8  Partial Fraction: Partial fraction of different forms, denominator with linear factors and denominator with repeated factors. Applying Partial fraction
BTL3: Applying
9 Integral Calculus I: Introduction and basic definition of integration, Integration of Standard functions. Understand the concept of integration.BTL2: Understanding
10 Integral Calculus II: Integration by parts, integration by substitution, definite integrals. Apply advanced integration methods BTL4: Applying
11 Statistics I: Frequency distribution, Mean, Median, Mode and standard deviation of frequency distribution, Karl Pearson coefficient of correlation. Calculate and interpret the mean, median, mode, and standard deviation of a frequency distribution. BTL3: Applying
12 Statistics II: Correlation analysis, different types of correlations, Karl Pearson coefficient of correlation and applications. Perform correlation analysis, calculate the Karl Pearson coefficient of correlation.BTL4: Analyzing
Textbooks References:

1.      R.D. Sharma, 11th and 12th Mathematics, Dhanpatrai Publication.

2.      Dass and Verma, Higher Engineering Mathematics, S.Chand.

Other References:

1.      Ramana B.V., Higher Engineering Mathematics, Hill Education.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

Program Scheme for Master of Computer Applications (MCA)

Th -Theory; OP-Practical; Pro-Project; T-Total; Crd -Credit

II Semester

Course Code Course Title

 

 Credit Sem Th/OP
LMC0201 Operating System 3 2 Th
LMC0202 Java Programming 3 2 Th
LMC0203 Software Engineering 3 2 Th
LMC0204 Advance Data Structure 3 2 Th
LMC0205 Data warehousing and mining 3 2 Th
LMC0221 Java Programming Practical 4 2 OP
LMC0222 Advance Data Structure Practical 4 2 OP
TOTAL  SEM II CREDIT  23  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Operating System

Course Code: LMC0201 Course Title: Computer Organization (3 Credits)
Course Objectives: –

Ø  To understand the evolution and fundamental concepts of operating systems.

Ø  To explore CPU scheduling algorithms and their evaluation.

Ø  To develop an understanding of process management, synchronization, and deadlock handling.

Ø  To gain knowledge of memory virtualization and storage systems.

Ø  To learn about distributed operating systems and their communication, resource management, protection, and security.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Evolution of operating systems, Types of OS, Multitasking, Timesharing, Multithreading, Multiprogramming and, Real time operating systems, Different views of the operating system, System Programmer’s view, User’s view,  Layered O.S, Monolithic Systems. Understanding the evolution of operating systems across different generations of computers, multitasking, and real-time systems.  BTL2: Understanding.
2 Processes: Process concept, control block, Systems programmer’s view of processes, Operating system services for process management, Scheduling algorithms, First come first serve, Round Robin, Shortest run time next, Highest response ratio next, Multilevel Feedback Queues, Performance evaluation. Remembering and Understanding the process management system, including the role of the Process Control Block (PCB) and how different process scheduling algorithms. BTL1 :Remembering and  BTL2 : Understanding
3 Memory Management : Memory management, Concepts of swapping and paging, Page replacement algorithms, Segmentation, Segmented Paging, Paged Segmentation Remembering and Understanding the different memory management techniques.BTL1 :Remembering  BTL2 : Understanding
4 Inter-process Communication and Synchronization: The need for inter-process synchronization, Mutual exclusion, binary and counting semaphores, hardware support for mutual exclusion, queuing implementation of semaphores, Classical problems in concurrent programming, Dining Philosopher’s problem, Bounded Buffer Problem etc. Analyse the different synchronization techniques and apply these concepts to solve classical problems such as the Dining Philosopher’s and Sleeping Barber problems.BTL4: Analyzing, BTL3: Applying.
5 Deadlocks: Concepts of deadlock detection, deadlock prevention, deadlock avoidance. Banker’s Algorithm. Evaluate different deadlock techniques. BTL 5: Evaluating.
6 File System: File systems, directories, file system implementation, security protection mechanisms. Analyze the structure of file systems, focusing on management and security. BTL 4: Analyzing.
7 UNIX File APIs: General File APIs, File and Record Locking, Directory File APIs, Device File APIs, FIFO File APIs,Symbolic Link File APIs. Understanding UNIX file APIs to develop system-level applications. BTL2 : Understanding
8 Input/output: Principles of I/O Hardware: I/O devices, device controllers, direct memory access. Analyze the working of I/O hardware systems and DMA.BTL4: Analyzing.
9 Principles of I/O software: Goals interrupt handlers, device drivers, and device independent I/O software. User space I/O Software. Understand the role of interrupt handlers, device drivers, and device-independent I/O software. BTL2 : Understanding.
10 Disks: Disk hardware, Disk scheduling algorithms (FCFS, SSTF, SCAN etc.) Error handling, track-at-a-time caching, RAM Disks.  Analyze disk scheduling algorithms (e.g.  FCFS, SSTF, SCAN) BTL 4: Analyzing.
11 Clocks: Clock hardware, memory-mapped terminals, I/O software. Evaluate the functionality of clock hardware. BTL 5: Evaluating
12 Processes and Processors in Distributed Systems: Threads, System models, processor allocation, scheduling. Distributed File Systems: Design, Implementation, & trends. .Performance Measurement, monitoring and evaluation, important trends, performance monitoring & evaluation, performance measures, evaluation techniques, bottlenecks & saturation, feedback loops. Evaluate distributed system models, processor allocation, and performance monitoring techniques, and analyze their implications for distributed OS design and implementation. BTL5: Evaluating and BTL 4: Analyzing
Textbook References: –

1.      Deitel, H.M. “An Introduction to Operating Systems”. Addison Wesley Publishing Company 1984.

2.      Milenkovic, M., “Operating Systems – concepts and Design” McGraw Hill International Edition- Computer Science series 1992.

Other References:-

1.      M.G. Venkatesh Murthy: UNIX & Shell Programming, Pearson Education.

2.      Richard Blum , Christine Bresnahan : Linux Command Line and Shell Scripting Bible

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

Java Programming

Course Code: LMC0202 Course Title: Computer Organization (3 Credits)
Course Objectives: –

Ø  To understand object-oriented programming concepts, and apply them in solving problems.

Ø  To introduce the principles of inheritance and polymorphism; and demonstrate how they relate to the design of abstract classes

Ø  To introduce the implementation of packages and interfaces

Ø  To introduce the concepts of exception handling and multithreading.

Ø  To introduce the design of Graphical User Interface using applets and swing controls.

Course Contents

Unit No. Unit Description Learning Outcome
1 Overview of Object-Oriented Programming and Java: object-oriented paradigm, basic concept of OOP, benefits of OOP, application of OOP. Recall the core concepts of OPPs.BTL1: Remembering
2 Java Fundamentals: Token in java, JDK, Java virtual machine, JRE, Reflection byte codes, Byte code interpretation, Data types, variable, arrays, expressions, operators, and control structures, Objects and classes Identify the fundamental components of Java, including the JDK, JVM etc.
BTL 1: Remembering
3 Java Classes: Abstract classes, Static classes, Inner classes, Packages, Wrapper classes, Interfaces, This, Super, Access control, constructor overloading, , static keyword, finalize () method in java. Understand the structure and usage of classes in Java. BTL 2: Understanding
4 Decision making and loops: if statement, if else statement, nested if statement, switch case, while, do while, for, for each loop in java. Apply decision-making structures and loop constructs to control program flow in Java.BTL3: Applying.
5 Inheritance: Inheritance in java, aggregation, instance initializer block, static block, final keyword, garbage collection in java. Understand inheritance and  aggregation.BTL1: Remembering
6 Exception Handling: Exception as objects, Exception hierarchy, Try catch finally, Throw, throws, Multiple catch block in java, nested try block in java. Analyze the exception hierarchy and effectively use try-catch-finally blocks for robust error handling in Java.
BTL 4: Analyzing.
7 Multithreaded Programming: Thread Life cycle, Multithreading advantages and issues, Simple thread program, Thread synchronization. Identify multithreading concepts, thread lifecycle, and basic thread synchronization to write concurrent programs in Java. BTL4: Analyzing.
8 Java Applets and Servlets: Applet Introduction, applet class and its skeleton, graphics in applet, displaying image in applet. Understand the basics of Java applets and servlets.BTL1: Remembering
9 Java Swing and Abstract Windowing Toolkit (AWT): Layout and component managers, Event handling, Applet class, Applet life-cycle, Passing parameters embedding in HTML, Swing components – JApplet, JButton, JFrame, etc.  Sample swing programs Understand and Analyse  GUI applications using Java Swing and AWT and event handling.BTL1: Remembering, BTL4: Analyzing.
10 IO package: Input streams, Output streams, Object serialization, Deserialization, Sample programs on IO files, Filter and pipe streams. Understand Java I/O streams, file operations, and object serialization.BTL2: Understanding
11 Database Connectivity: JDBC architecture, Establishing connectivity and working with connection interface, Working with statements, Creating and executing SQL statements, Working with Result Set. Establish JDBC connections, execute SQL queries, and work with the ResultSet in Java for database interaction.
BTL6: Creating
12 Java Networking: Networking concepts, socket programming, URL class, URL Connection class, HTTP URLConnection, InetAddress class. Analyze Java networking concepts.BTL4: Analyze.
Textbook References: –

  1. Java: The Complete Reference Hebert Schildt, Mc Graw Hill
  2. Object-Oriented Programming with C++ and Java Debasis Samanta, Prentice Hall India.

Other References:-                                                                                  

  1. Programming with Java A Primer, E. Balaguruswamy Tata McGraw Hill Companies.
  2. Java Programming John P. Flynt Thomson.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

Software Engineering

Course Code: LMC0203 Course Title: Computer Organization (3 Credits)
Course Objectives:-

Ø  To understand the foundational principles of software engineering, including software characteristics, crisis, and myths.

Ø  To analyze and apply software process models in real-world software development scenarios.

Ø  To apply software project planning, estimation techniques, and software measurement tools effectively.

Ø  To demonstrate the ability to conduct software verification and validation, including testing strategies and quality assurance practices.

Ø  To understand and implement risk management, agile methodologies, object-oriented principles, and web engineering concepts in software development projects.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: The Evolving role of software, software characteristics, software crisis, software Myths. State the basics of software.BTL1:Remembering
2 Software Process Model: Software Process, Methods and Tools, software process, software process models: prototyping model, RAD model, Incremental models, spiral model Compare and contrast different software process models.BTL2: Understanding
3 Software process and project metrics: Measures, metrics and indicators, process metrics and software process improvement, software measurement: size-oriented metrics, function-oriented metrics. Define and apply software process metrics and project metrics for process improvement.BTL3: Applying
4 Software project planning: Observations on estimating, project planning objectives, software scope: obtaining information necessary for scope, resources: human resources, reusable software resources, software project estimation, software sizing, problem based estimation, LOC,FP-based estimation, process based estimation, COCOMO model Apply various estimation methods like LOC, Function Points, and COCOMO in software project planning.
BTL3: Applying
5 System engineering: computer based systems, system modelling, system simulation, requirement engineering: requirement elicitation, requirement analysis and negotiation, requirement specification, requirement validation, requirement management. Explain the process of requirement engineering,.
BTL2: Understanding
6 Analysis concepts and principles: requirement analysis, requirement elicitation for software, initiating the process, analysis principles: modeling, partitioning, software prototyping: prototyping methods and tools. Apply requirement analysis techniques such as partitioning and prototyping.
BTL3: Applying
7 Design concepts and principles: The design process, design principles, Design concepts, cohesion and coupling. Evaluate the principles of cohesion and coupling.
BTL5: Evaluating
8 Software Verification And Validation: Unit Testing, Integration and System Testing, Static Confirmation, Dynamic Testing, Traceability Matrices , Automated Testing,  Other Specialized Testing, white box testing, basis path testing, black box testing. Apply software testing  Analyzing
9 Software Quality And Security:  Software Quality Concepts, Software Configuration Management (CM), Software Quality Assurance (SQA), Software Quality and Agile Methods Software Metrics and Analytics, Quality and Process Standards and Guidelines. Analyze and Implement software quality assurance practices.BTL3: Applying and BTL4: Analyzing
10 Risk Management: Project Management Concepts, Project Planning and Estimation, Cooperative roles of software engineering and project management, Developing risk response strategies, Risk Management in Agile Processes, Agile Project Planning, Project Management Metrics, and Software Support Strategies. Identify, assess, and manage risks in techniques such as unit testing, integration testing, and system testing.. BTL3: Applying
11 Object Oriented Concepts And Principles: The object-oriented paradigm, object oriented concepts, identifying the elements of an object models, management of object oriented software projects, object oriented analysis, OOA process Apply object-oriented analysis (OOA) techniques in managing software projects.
BTL3: Applying
12 Web Engineering: The attributes of web based applications, quality attributes, Web process, analyzing web based systems, design for web based applications, management issues, reengineering, reverse engineering. Analyze the importance of Web Engineering process in designing and managing web-applications. BTL4: Analyzing
Textbook References: –

  1. Pressman R.S., “Software Engineering”, Tata McGraw Hill Publishing Company Ltd.
  2. Sommerville, Ian “Software Engineering”, Pearson Education

Other References:-

  1. Jalote Pankaj, “An Integrated Approach to Software Engineering”, NAROSA.
  2. Fairely, R.E., “Software Engineering Concepts”, McGraw-Hill

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

Advance Data Structure

Course Code: LMC0204 Course Title: Computer Organization (3 Credits)
Course Objectives:-

Ø  To understand the concepts of algorithms, data structures, and their performance analysis, and apply them to solve computational problems.

Ø  To analyse the time and space complexity of algorithms using asymptotic notation and perform comparisons between different algorithms and data structures.

Ø  To implement and manipulate different data structures such as arrays, linked lists, stacks, queues, trees, and graphs, both manually and using Java.

Ø  To apply searching and sorting algorithms effectively in solving problems, and understand hashing techniques and their applications.

Ø  To explore advanced algorithmic paradigms such as greedy strategy, dynamic programming, backtracking, and randomized algorithms, and apply them in real-world applications.

Course Contents

Unit No. Unit Description Learning Outcome
1 Algorithms Performance analysis: Time complexity and space complexity, Asymptotic Notation-Big Oh, Omega and Theta notations, Complexity Analysis Examples. Recall algorithms and its performance parameters. BTL1: Remembering.
2 Data structures: Linear and non – linear data structures, ADT concept, Linear List ADT, Array representation, Linked representation, Vector representation, singly linked lists -insertion, deletion, search operations, doubly linked lists-insertion, deletion operations, circular lists. Representation of single, two dimensional arrays, Sparse matrices and their representation. Understand and implement linear and non-linear data structures, such as arrays, linked lists, and vectors.BTL2: Understanding, BTL3: Applying
3  Stack and Queue: ADTs, array and linked list representations, infix to postfix conversion using stack, implementation of recursion, Circular queue-insertion and deletion, Dequeue ADT, array and linked list representations, Priority queue ADT . Implement stack and queue operations, including conversion and recursion.
BTL3: Applying
4 Implementation using Heap and Linked List:  Implementation using Heaps, Insertion into a Max Heap, Deletion from a Max Heap, java.util package-Array List, Linked List, Vector classes, Stacks and Queues in java.util, Iterators in java.util. Apply search algorithms and understand hashing techniques. BTL3: Applying.
5 Searching: Linear and binary search methods, Hashing-Hash functions, Collision Resolution methods-Open Addressing, Chaining, Hashing in java.util-HashMap, HashSet, Hashtable. Understand and apply various sorting techniques. BTL2: Understanding,BTL3: Applying
6 Sorting : Bubble sort, Insertion sort, Quick sort, Merge sort, Heap sort, Radix sort, comparison of sorting methods. Implement and traverse binary trees, and understand its properties BTL3: Applying
7 Trees:  Ordinary and Binary trees terminology, Properties of Binary trees, Binary tree ADT, representations, recursive and non-recursive traversals, Java code for traversals, Threaded binary trees. Understand and analyze  search trees like binary search trees etc  .BTL3: Applying, BTL4: Analyzing
8 Graphs: Graphs terminology, Graph ADT, representations, graph traversals/search methods-depth first search and breadth first search, Java code for graph traversals.  Understand basics and apply   graph traversals.BTL2: Understanding, BTL3: Applying.
9  Applications of Graphs: Minimum cost spanning tree using Kruskal’s algorithm, Dijkstra’s algorithm for Single Source Shortest Path Problem. Apply Huffman coding for text compression and use the KMP algorith.BTL3: Applying
10 Search Trees: Binary search tree-Binary search tree ADT, insertion, deletion and searching operations, Balanced search trees, AVL trees-Definition and examples only, Red Black trees – Definition and examples only. Analyze greedy, dynamic programming, backtracking, and branch-and-bound algorithmic paradigms.BTL4: Analyzing
11 Algorithms (Part 1): B-Trees-definition, insertion and searching operations, Trees in java.util- TreeSet, Tree Map Classes, Tries (examples only), Comparison of Search trees. Text compression-Huffman coding and decoding, Pattern matching-KMP algorithm.  Understand and analyze  algorithms in problem-solving scenarios.BTL2: Understanding,  BTL4: Analyzing
12 Algorithms (Part 2): Algorithmic paradigms Greedy Strategy, Dynamic programming, Backtracking, Branch-and-Bound, Randomized algorithms. Implement and use Java’s built-in data structures such as Array List. BTL3: Applying
Textbook References: –

1.       S. Sahni, “Data structures, Algorithms and Applications in Java”, Universities Press.

2.      Adam Drozdek, “Data structures and Algorithms in Java”,

Other References:-

1.      R.Lafore “Data structures and Algorithms in Java”, Pearson education.

2.      J.P.Tremblay and G.A.Cheston “Data structures and Software Development in an Object- Oriented Domain Pearson Education.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

Data Warehousing and Mining

 

Course Code: LMC0205 Course Title: Data Warehousing and Mining (3 Credits)
Course Objectives:-

Ø  To understand the fundamental concepts and applications of data warehousing and mining.

Ø  To learn the different data types and their significance in data mining.

Ø  To master the techniques of data preprocessing, classification, prediction, and clustering.

Ø  To develop the ability to implement data mining algorithms and techniques.

Ø  To analyze the practical applications and tools currently available in the field of data mining.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Data type for Data Mining: Motivation, importance, relation Databases, Data Warehouses, Transactional databases, advanced database system and its applications, Data mining Functionalities. Understand  databases, data warehouses, and its functionalities.BTL2: Understanding
2 Classification & Prediction: Concept/Class description, Association Analysis classification & Prediction, Cluster Analysis, Outlier Analysis, Evolution Analysis, Classification of Data Mining Systems. Differentiate between operational databases and data warehouses, & explain its   architecture.BTL2: Understanding
3 Data Warehouse and OLAP Technology for Data Mining: Differences between Operational Database Systems and Data Warehouses. Apply data cleaning, integration,  techniques to prepare data for mining.BTL3: Applying
4 Data Models and Architecture: Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Examples, Data Warehouse Implementation, Data Cube Technology. Understand and describe the Multidimensional Data Model architecture. BTL2: Understanding
5 Data Pre-processing: Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation. Apply data preprocessing techniques such as cleaning, integration, etc. BTL3: Applying
6 Data Mining Concept Description: Primitives, Languages, and System Architectures, Concept Description, Characterization and Comparison, Analytical Characterization. Understand the basic of data Mining.BTL2: Understanding.
7 Mining Association Rules in Large Databases: Association Rule Mining: Market Basket Analysis, Basic Concepts. Explain the association rule mining. BTL 2: Understanding.
8 Mining Single -Dimensional Boolean Association Rules from Transactional Databases: The Apriori algorithm, Generating Association rules from Frequent items, Improving the efficiency of Apriori.  Understanding Traditional database and analyze its efficiency  BTL2: Understanding BTL4:Analyzing
9 Association Rules: Mining Multilevel Association Rules, Multidimensional Association Rules, Constraint -Based Association Mining. Differentiate between types of association mining.BTL:4 Analyzing.
10 Classification & Prediction and Cluster Analysis: Issues regarding classification & prediction, Different Classification Methods, Prediction. Implement various classification methods and prediction techniques. BTL3: Applying
11 Clustering: Cluster Analysis, Major Clustering Methods, and Applications & Trends in Data Mining. Identify issues in classification and prediction. BTL: 4 Analyzing.
12 Data Mining Applications: Data Mining applications in real world, currently available tools. Evaluate real-world data mining applications BTL5: Evaluate.
Textbook References: –

  1. Data Mining: Concepts and Techniques by Jiawei Han & Micheline Kamber
  2. Data Mining and Data Warehousing: Principles and Practical Techniques by Parteek Bhatia

Other References:-

  1. The Data Warehouse Life Cycle Toolkit by Ralph Kimball
  2. Data Mining by Vikram Pudi & P. Radha Krishna.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

Program Scheme for Master of Computer Applications (MCA)

Th -Theory; OP-Practical; Pro-Project; T-Total; Crd -Credit

III Semester

Course Code Course Title

 

 Credits Sem Th/P
LMC0301 Visualization with R Programming 3 3 Th
LMC0302 Data Communication and Network 3 3 Th
LMC0303 Python Programming 3 3 Th
LMC0304 Research Methodology 3 3 Th
LMC034X Elective Group (A/B/C/D) 4 3 Th
LMC0321 R Programming Practical 4 3 P
LMC0322 Python Programming Practical 4 3 P
TOTAL  SEM 3 CREDIT 24  

Elective Group (A/B/C/D)

Course Code Course Title

 

 Group / Elective Th/P
LMC0340 Artificial Intelligence and  Machine learning Group A (AI & ML) Th
LMC0341 Cloud Computing Group B (Cloud Computing) Th
LMC0342 Wireless Sensor Networks & IoT Standards Group C (Internet of Things ) Th
LMC0343 Web Development (C #) Group D  (Web Technology) Th

 

                                                                          

 

Visualization with R Programming

Course Code: :  LMC0301 Course Title: Visualization with R Programming            (3 Credits)
Course Objectives: –

Ø  To introduce the fundamentals of data visualization using R programming.

Ø  To enable students to manipulate and visualize data effectively using various R libraries.

Ø  To develop proficiency in creating informative and interactive visualizations for data analysis and presentation.

Ø  To understand the principles of visual perception and how they influence data representation.

Ø  To create maps and choropleth visualizations with ggmap and leaflet

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Data Visualization: Overview of data visualization, importance in data analysis, introduction to R programming, setting up R environment, basic R syntax. Recall data visualization in data analysis and  understand the  basic  of R programming.BTL1: Remember, BTL2: Understanding
2 Data Types and Structures in R: Vectors, matrices, lists, data frames, importing and exporting data, data cleaning and transformation. Identify and manipulate different data types and structures in R and practice data import/export and cleaning techniques.BTL2: Understanding
3 Basic Plotting with R: Introduction to base graphics in R, scatter plots, line graphs, bar charts, histograms, pie charts, and box plots. Create and interpret basic visualizations such as scatter plots, line graphs etc.BTL3: Applying
4 Advanced Graphics with ggplot2: Introduction to ggplot2, understanding the grammar of graphics, layers, scales, themes, and facets in ggplot2. Develop advanced plots using ggplot2 by understanding its grammar of graphics, layers etc.
BTL3: Applying
5 Data Manipulation with dplyr and tidyr: Using dplyr for data manipulation, filtering, selecting, mutating, summarizing data, tidying data with tidyr, reshaping data. Use dplyr for data manipulation tasks such as filtering, selecting, mutating, and summarizing, and apply tidyr for reshaping and tidying data.BTL3: Applying
6 Statistical Data Visualization: Visualizing distributions, statistical summaries, creating plots for regression analysis, visualizing correlations, and understanding distributions. Analyze Visualize distributions, statistical summaries, regression plots, and correlations .BTL4: Analyzing
7 Interactive Visualizations with plotly and Shiny: Creating interactive plots with plotly, introduction to Shiny for building interactive web applications, integrating plots with Shiny apps. Create interactive plots with plotly and build interactive web applications with Shiny, integrating dynamic visualizations into web interfaces.BTL4: Analyzing
8 Time Series Visualization: Plotting time series data, understanding trends and seasonality, using ts objects in R, advanced time series plots. Understand and visualize time series data, including trends and work with ts objects in R for advanced time series plots.BTL3: Applying
9 Geospatial Data Visualization: Handling geospatial data in R, plotting maps using ggmap and leaflet, creating choropleth maps, visualizing spatial data. Handle and visualize geospatial data using R, creating maps and choropleth visualizations with ggmap and leaflet.BTL3: Applying
10 Case Studies and Project: Applying visualization techniques to real-world datasets, creating comprehensive data visualizations, project work on developing a visualization dashboard.  Apply data visualization techniques to real-world datasets, and develop a comprehensive visualization dashboard as part of a project.BTL5: Evaluating
Textbook References: –

  1. Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer.
  2. Chang, W. R Graphics Cookbook: Practical Recipes for Visualizing Data. O’Reilly Media.

Other References:-

  1. Matloff, N. The Art of R Programming: A Tour of Statistical Software Design. No Starch Press.
  2. Kabacoff, R. I. R in Action: Data Analysis and Graphics with R. Manning Publications.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

 

 

 

Advanced Data Communication and Network

Course Code:  LMC0302 Course Title: Advanced Data Communication and Network (3 Credits)
Course Objectives: –

Ø  To learn the basic concepts of computer networks.

Ø  To understand the concept of network topology.

Ø  To understand network management systems.

Ø  To gain knowledge of Client-Server applications.

Ø  To understand Communication and Network.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction – Overview, Need for computer communication, Goals of communication systems/Networking, Transmission Modes, Transmission Media: Guided: Twisted Pair, Coaxial and Fiber-Optic Cables, Unguided Media: Radio, VHF, Microwaves, and Satellite, Topologies: Star, Mesh, Bus, etc. Understand the fundamentals of data communication, networking concepts, transmission modes, media, and network topologies.BTL2: Understanding
2 Physical Layer- Multichannel Data Communication: Message, Circuits, Packets (Connection-Oriented vs Connectionless Services), Components of LAN, WAN, MAN, Multiplexing: FDM, TDM, and WDM, Protocol Layering: ISO/OSI Reference Model, TCP/IP Reference Model, OSI vs TCP/IP Learn the concepts of message transmission, packet communication, LAN, WAN, MAN, multiplexing, and protocol layering through ISO/OSI and TCP/IP models.
BTL2: Understanding
3 Network Layer- IPv4 addresses – Network and Host part, Network Masks, Network addresses, and Broadcast addresses, Sub-net Masking, Super Net Masking, Numerical based on IP Address, Address Classes, IPv4 Structure, IP routing concept, Distance Vector algorithm, Routing Table, IPv6 Structure, Addresses. Learning Outcome: Gain knowledge of IPv4 addressing, subnetting, routing concepts, and the difference between IPv4 and IPv6 structures and addressing.
BTL3: Applying
4 Transport Layer- Transport Services, Elements of Transport Protocols, Connection Management, Connection Management, TCP and UDP Protocols Understand transport services, protocol elements, and the management of connections.
BTL2: Understanding
5 Application Layer- DNS and DNS Servers, DNS and DNS Servers, Electronic Mail: Architecture and Services, Electronic Mail: Architecture and Services, Message Format, SMTP, Mail Gateways, FTP, WWW: Introduction, Static and Dynamic web pages, WWW pages and browsing, HTTP request and response, Basics of DHCP. Explore the application layer protocols such as DNS, SMTP, FTP, HTTP, and DHCP, and understand their role in electronic communication.BTL2: Understanding
6 Common Network Architecture- X.25 Networks, Ethernet (Standard and Fast): Frame format and specifications, Wireless LAN’s – 802.11x, 802.3, Bluetooth, etc.. Understand network architectures like X.25, Ethernet, Wireless LAN, and Bluetooth, along with their frame formats and specifications.
BTL3: Applying
7 Network Security – Threat: Active and Passive Attacks, Cryptography:  Symmetric and Asymmetric Key Cryptography, Digital Signature, Firewall, Types of Firewall Identify network security threats, understand cryptographic techniques, and learn about firewalls and their types.BTL3: Applying
8 Network Management– OSI Network Management Model, ISO Network Management Functions, Remote Network Monitoring (RMON), Network Management Tools, Wireless Network Management Understand the OSI network management model, network management functions, and tools.
BTL2: Understanding
9 Emerging Trends and Technologies– Software-Defined Networking (SDN), Network Function Virtualization (NFV), Internet of Things (IoT), Cloud Computing and Networking, Cloud Computing and Networking, Future Internet Architectures Gain insights into modern networking technologies like SDN, NFV, IoT, and cloud computing, and their impact on future internet architectures.BTL4: Analyzing
10 Broadband Network Management – ATM Networks, Future Internet Architectures, Broadband Networks and Services, Broadband Networks and Services, ATM Technology, ATM Technology  Understand the concepts of ATM networks, broadband network services, and the future of broadband technologies.BTL3: Applying
Textbook References: –

  1. Data and Computer Communications by William Stallings.
  2. Computer Networking: A Top-Down Approach by James F. Kurose and Keith W. Ross.O’Reilly Media.

Other References:-

  1. Data Communications and Networking by Behrouz A. Forouzan.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

Advance Python Programming

Course Code: LMC0303 Course Title: Advance Python Programming (3 Credits)
Course Objectives: –

Ø  To utilize advanced Python features for efficient programming.

Ø  To develop web applications and APIs using Python frameworks.

Ø  To apply Python in data manipulation, analysis, and machine learning.

Ø  To implement concurrency and parallelism for optimized performance.

Ø  To explore Python’s application in networking, automation, and cloud computing.

Course Contents

Unit No. Unit Description Learning Outcome
1 Advanced Python Syntax: Iterators, generators, decorators, and context managers. Understanding the nuances of Python syntax. Recall advanced Python syntax concepts.BTL1: Remembering
2 Advanced Object-Oriented Programming: Metaclasses, abstract classes, multiple inheritance, and design patterns in Python. Gain expertise in advanced OOP concepts in Python. BTL4: Analyzing
3 Data Manipulation and Analysis: Using libraries like NumPy, Pandas for data analysis, performing complex data manipulations, and understanding dataframes. Perform complex data manipulations using Python libraries like NumPy and Pandas.
BTL3: Applying
4 Working with APIs: Understanding RESTful APIs, consuming APIs using Python, and building simple APIs with Flask/Django Understand RESTful APIs, consume APIs using Python.BTL2: Understanding
5 Web Development with Python: Basics of web development using Django/Flask, setting up a web server, creating and managing views, and handling forms and templates. Learn the fundamentals of web development with Python, including web servers, views, forms, templates, and using frameworks.BTL3: Applying
6 Concurrency and Parallelism: Understanding threading, multiprocessing, asynchronous programming with async/await, and concurrent futures. Understand concurrency and parallelism concepts, including threading, multiprocessing, asynchronous programming. BTL4: Analyzing
7 Testing and Debugging: Writing unit tests, using testing frameworks like pytest, and debugging techniques in Python. Use testing frameworks like pytest, and implement debugging techniques.BTL3: Applying
8 Python for Data Science: Introduction to machine learning using Python, basics of scikit-learn, and performing exploratory data analysis. Perform exploratory data analysis, and work with scikit-learn.BTL3: Applying.
9 Networking and Sockets Programming: Introduction to network programming, creating sockets, handling client-server communication, and working with protocols. Create sockets, handle client-server communication, and work with network protocols using Python.
BTL3: Applying, BTL6:Create
10 Advanced Topics in Python: Overview of Python’s role in cloud computing, working with AWS/GCP SDKs, and automation scripting.  Evaluate  Python’s role in cloud computing and adv. python
BTL5: Evaluating
Textbook References: –

  1. Luciano Ramalho, Fluent Python, O’Reilly Media, 1st Edition.
  2. Mark Lutz, Programming Python, O’Reilly Media.

Other References:-

  1. David Beazley, Python Cookbook, O’Reilly Media.
  2. Jake VanderPlas, Python Data Science Handbook, O’Reilly Media.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

 

 

 

 

 

                  

 

                   Research Methodology

Course Code: LMC0304 Course Title: Research Methodology (3 Credits)
Course Objectives: –

Ø  To provide an understanding of research concepts and methodologies.

Ø  To equip students with the skills to design and conduct research studies.

Ø  To enable students to analyze data and interpret research findings.

Ø  To develop the ability to critically evaluate research literature.

Ø  To guide students in the preparation of research proposals and reports.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Research Methodology: Meaning, Objectives, Types of Research, Research Approaches, Research Process. Remember the meaning, objectives, types, and processes involved in research methodology.
BTL1: Remembering
2 Literature Review: Importance of Literature Review, Sources of Information, Reviewing Process, Writing a Review. Understand the importance of literature review, identify sources of information. BTL2: Understanding
3 Research Design: Research Problem Formulation, Hypothesis Development, Research Design Types: Exploratory, Descriptive, Experimental. Understand how to formulate research problems, develop hypotheses, and explore different research design types like exploratory, descriptive, and experimental.BTL2: Understanding
4 Data Collection Methods: Primary and Secondary Data, Sampling Techniques, Questionnaire Design, Interviews, Observation. Use knowledge of primary and secondary data, sampling techniques, and effective data collection methods.BTL3: Applying
5 Measurement and Scaling: Types of Scales, Measurement Errors, Reliability and Validity of Measurements. Understand the types of measurement scales, measurement errors, and assess the reliability and validity of measurements.BTL3: Applying
6 Data Analysis: Descriptive and Inferential Statistics, Hypothesis Testing, Use of Software Tools for Data Analysis. Learn the concepts of descriptive and inferential statistics, hypothesis testing, and the use of software tools for data analysis.BTL4: Analyzing
7 Qualitative Research: Characteristics, Techniques, and Methods, Content Analysis, Case Study Method. Explore the characteristics, techniques, and methods of qualitative research, including content analysis and case study methods.BTL2: Understanding
8 Research Report Writing: Structure of Research Report, Writing Techniques, Presentation of Data, Referencing Styles, Plagiarism Understand the structure and writing techniques for research reports, including data presentation, referencing styles, and avoiding plagiarism.BTL3: Applying
9 Ethical Issues in Research: Ethical Considerations, Informed Consent, Confidentiality, Publication Ethics. Learn about ethical considerations in research, informed consent, confidentiality, and publication ethics.BTL2: Understanding
10 Emerging Trends in Research: Big Data Analytics, Artificial Intelligence in Research, Multidisciplinary Research, and Trends in Publishing.  Explore emerging trends in research, such as Big Data Analytics, AI. BTL4: Analysing
Textbook References: –

  1. C.R. Kothari & Gaurav Garg, “Research Methodology: Methods and Techniques,” New Age International Publishers.
  2. Ranjit Kumar, “Research Methodology: A Step-by-Step Guide for Beginners,” SAGE Publications.

Other References:-

  1. John W. Creswell, “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches,” SAGE Publications.
  2. Zikmund, Babin, Carr & Griffin, “Business Research Methods,” Cengage Learning.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

 

 

 

 

 

Artificial Intelligence and Machine Learning

Course Code: LMC0340 Course Title: Artificial Intelligence and Machine Learning  (3 Credits)
Course Objectives: –

Ø  To understand the foundational concepts and methods in Artificial Intelligence (AI) and Machine Learning (ML).

Ø  To implement and apply machine learning algorithms for different types of problems.

Ø  To analyze the trade-offs between different AI and ML techniques in various contexts.

Ø  To develop AI systems that can solve real-world problems using appropriate ML models.

Ø  To evaluate AI and ML models based on performance metrics and improve them through hyper parameter tuning and cross-validation techniques.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Artificial Intelligence: Definition and Scope of AI, History and Evolution of AI, Types of AI: Narrow AI, General AI, Super intelligent AI, AI vs Machine Learning vs Deep Learning, AI applications in various domains. Define the scope, history, evolution, and types of AI, and differentiate AI from machine learning and deep learning.BTL1: Remembering
2 Problem Solving and Search Algorithms: Problem Representation and Formulation, Uninformed Search Algorithms (BFS, DFS, Uniform Cost Search),, Informed Search Algorithms (A*, Greedy Search), Heuristic functions, Optimization in Search Algorithms Understand the fundamentals of problem representation and formulation, uninformed and informed search algorithms, heuristic functions and optimization. BTL2: Understanding
3 Knowledge Representation and Reasoning- Introduction to Knowledge Representation and reasoning, Logic: based Representation in AI, Propositional Logic and Predicate Logic, Rule-based Systems, Forward Channing and Backward Chaining, Inference and Reasoning Techniques Understand knowledge representation and reasoning techniques in AI, including logic-based representation, propositional and predicate logic, rule-based systems, and inference techniques.
BTL2: Understanding
4 Machine Learning: Introduction and Overview: Definition of Machine Learning, Supervised, Unsupervised, and Reinforcement Learning, Overview of ML Algorithms, Difference between AI and ML, Basic Applications of ML Recall & Gain knowledge of machine learning, including different types of learning key algorithms, and applications. BTL1: Remembering, BTL2: Understanding
5 Supervised Learning: Regression and Classification, Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) Understand and apply supervised learning techniques decision trees, SVM, KNN for regression & classification tasks.BTL3: Applying
6 Unsupervised Learning: Clustering and Dimensionality Reduction, K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), t-SNE for Dimensionality Reduction, Applications in Data Preprocessing. Use  unsupervised learning techniques like K-Means clustering, hierarchical clustering, and apply them in data pre-processing and dimensionality reduction.BTL3: Applying
7 Neural Networks and Deep Learning: Introduction to Neural Networks, Perceptron and Multi-Layer Perceptron (MLP), Backpropagation Algorithm, Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs) Understand the structure and functioning of neural networks, including Perceptrons, Multi-Layer Perceptrons (MLPs), backpropagation, DNNs, CNNs.BTL2: Understanding
8 Model Evaluation and Improvement-   Introduction to Evaluation and Improvement , Performance Metrics: Accuracy, Precision, Recall, F1-score, Cross-Validation Techniques, Hyperparameter Tuning, Overfitting and Underfitting, Regularization Methods: L1, L2 Evaluate ml models using performance metrics, cross-validation, hyper parameter tuning etc to prevent overfitting and underfitting.BTL4: Analyzing
9 Reinforcement Learning – Overview of Reinforcement Learning, Markov Decision Processes (MDPs), Q-Learning Algorithm, Policy Gradient Methods, Applications of RL in Games and Robotics Understand reinforcement learning, including Markov Decision Processes (MDPs), Q-learning, policy gradient methods.
BTL2: Understanding
10 Natural Language Processing (NLP)- Introduction to NLP, Text Pre-processing and Tokenization, Bag of Words, TF-IDF Models, Named Entity Recognition (NER), Sentiment Analysis using ML. Apply  text pre-processing, tokenization, Bag of Words, Named Entity Recognition (NER), and sentiment analysis using machine learning.BTL3: Applying
11 AI Ethics and Societal Impacts- Ethical Issues in AI and ML, Bias and Fairness in AI Systems, Transparency and Explainability of ML Models. Analyze  ethical issues , such as bias, fairness, transparency, explainability, and the societal impacts of AI and ML systems.BTL4: Analyzing
12 AI Ethics and Societal Impacts(Part- 2)- Privacy and Security in AI, Societal Impact of AI and Automation Understand the privacy, security concerns, and broader societal impacts of AI.BTL4: Analyzing
13 Future Trends in AI and Machine Learning(Part -1)- AI in Healthcare, Autonomous Vehicles & Finance, Transfer Learning, Quantum ML, AI and Creativity: Art, Music, and Design. Explore future trends in AI applications, such as AI in healthcare, autonomous vehicles, finance etc. BTL5: Evaluating
14 Future Trends in AI and Machine Learning(Part- 2)- Emerging Trends and Technologies in AI, AI Applications and Tools, ChatGPT, Deep Seek and other real world uses. Investigate emerging AI trends, the application and tools of AI like ChatGPT, Deep Seek etc.BTL5: Evaluating
Textbook References: –

  1. C.R. Kothari & Gaurav Garg, “Research Methodology: Methods and Techniques,” New Age International Publishers.
  2. Ranjit Kumar, “Research Methodology: A Step-by-Step Guide for Beginners,” SAGE Publications.

Other References:-

  1. John W. Creswell, “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches,” SAGE Publications.
  2. Zikmund, Babin, Carr & Griffin, “Business Research Methods,” Cengage Learning.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

Cloud Computing

Course Code: LMC0341 Course Title: Cloud Computing  (3 Credits)
Course Objectives: –

Ø  To understand the fundamental concepts and architecture of cloud computing.

Ø  To analyze various cloud service models and deployment models.

Ø  To explore virtualization technologies and their role in cloud computing.

Ø  To understand cloud storage solutions and data management in the cloud.

Ø  To implement cloud-based applications and services.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Cloud Computing: Definition and Characteristics, Cloud Computing Models, Architecture, Evolution, Cloud Ecosystem, Benefits and Challenges of Cloud Computing. Remember the characteristics, models ,benefits and challenges of cloud computing.BTL1: Remembering
2 Cloud Service Models: Overview of IaaS, PaaS, SaaS, Key Features of Each Model, Examples of Cloud Service Providers (AWS, Azure, Google Cloud), Compare and Contrast IaaS, PaaS, SaaS. Understand and compare IaaS, PaaS, SaaS models, their features, and cloud service providers. BTL2: Understanding
3 Public Cloud-Private Cloud, Hybrid Cloud, Community Cloud, Deployment Model Selection Criteria, Benefits and Limitations of Each Model, Use Cases of Each Deployment Model Understand the different cloud deployment models and their use cases, benefits, and limitations.BTL2: Understanding
4 Virtualization in Cloud Computing– Introduction to Virtualization, Hypervisor Types and Architectures, Virtual Machines and Virtual Networks, Virtualization and Cloud Computing Integration Analyze the concepts of virtualization and their integration with cloud computing. BTL4: Analyzing
5 Cloud Storage Overview– Types of Cloud Storage: Block, File, Object, Cloud Storage Providers, Storage Management Techniques, Data Replication and Availability Explore different types of cloud storage and cloud storage providers.BTL3: Applying
6 Data Management and Big Data in the Cloud– Data Management in Cloud Environments, Cloud Databases: NoSQL, SQL Databases, Data Sharding and Partitioning, Big Data Technologies in Cloud (Hadoop, Spark). Use cloud data storage management and big data technologies. BTL3: Applying
7 Cloud Application Development: Basics of  Cloud Application Development, Cloud APIs and SDKs, Cloud-Based Application Deployment, Cloud App Development Best Practices Learn about cloud application development, using APIs and SDKs, and best practices for cloud-based application deployment.BTL3: Applying
8 Cloud Computing Tools and Platforms- Introduction to Cloud Platforms (AWS, Azure), Cloud Management Tools, Automation and Orchestration Tools, Monitoring and Performance Tuning Tool Use cloud platforms and management tools for automation, orchestration, monitoring, and performance tuning.BTL3: Applying
9 Cloud Security Challenges – Security Risks in Cloud Computing, Cloud Data Protection Techniques, Cloud Security Models ,Compliance in Cloud, Multi- Tenant Security in the Cloud, Secure Cloud Architecture, Cloud Security Best Practices Analyze security risks, protection techniques, and compliance in cloud environments.BTL4: Analyzing
10 Privacy and Compliance in Cloud Computing- Privacy Concerns in Cloud Computing, Data Privacy Regulations, Secure Data Storage and

Transfer in Cloud, Risk Management in Cloud Environments.

 Understand privacy concerns, data privacy regulations, and secure data storage and transfer practices in cloud computing.
BTL4: Analyzing
11 Cloud Computing for Mobile and IoT Applications-Mobile Cloud Computing , IoT and Cloud Integration, IoT Cloud Architecture. Understand mobile cloud computing, IoT-cloud integration.BTL2:Understanding
12 Cloud Computing for Mobile and IoT Applications- Cloud Services for Mobile Devices, Security and Privacy for Mobile Cloud Applications. Analyze the importance of Cloud services for mobile devices, and security/privacy concerns. BTL4: Analyzing
13 Future Trends and Research in Cloud Computing (Part -1)- Emerging Cloud Computing Models, artificial Intelligence  and Machine Learning, Evaluate emerging cloud computing models, AI and ML. BTL5: Evaluating
14 Future Trends and Research in Cloud Computing (Part -2)- Sustainability, Cloud Computing in the Digital Transformation Era. Analyze the importance of cloud computing in the Digital Transformation Era.BTL4: Analyzing
Textbooks Reference:-

1.      Cloud Computing: Principles, Systems and Applications by Nikos Antonopoulos, Lee Gillam

2.      Cloud Computing: Concepts, Technology & Architecture by Thomas Erl

Other References :-

1.      Cloud Computing: A Hands-On Approach by Arshdeep Bahga, Vijay Madisetti

2.      Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS) by Michael Kavis

Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3:

Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

Wireless Sensor Network and IOT Syllabus

Course Code: LMC0342 Course Title: Wireless Sensor Network and IOT  (3 Credits)
Course Objectives: –

Ø  To understand the fundamentals of Wireless Sensor Networks (WSN) and the Internet of Things (IoT).

Ø  To analyse the architecture and protocols used in WSN and IoT systems.

Ø  To evaluate various wireless communication technologies used in IoT and WSN.

Ø  To Investigate the security, privacy, and standards in WSN and IoT.

Ø  To examine emerging trends and standards in WSN and IoT, including 5G and IoT protocols.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Wireless Sensor Networks & IoT- Basics of WSN, Architecture of WSN, Components of WSN: Sensors, Actuators, and Nodes, Architecture, Types of WSNs and IoT Networks, Applications of WSN and IoT. Remember and Understand the components, architecture, and applications of WSN and IoT. BTL1: Remembering ,BTL2: Understanding
2 WSN Design Principles & Communication Models- WSN Design Constraints, Topologies in WSN, Broadcast, Multicast, and Unicast, Analog and Digital Transmission. Understand WSN design constraints, topologies, and communication models
BTL2: Understanding
3 IoT Protocols and Communication Technologies- IoT Protocol Stack: Application, Transport, Network, and Data Link Layers, Low Power WAN , Communication Technologies, Role of IP in IoT, Interoperability Issues in IoT. Understand the IoT protocol & communication technologiesBTL2: Understanding
4 WSN Routing Protocols –  WSN Routing Protocols, Proactive vs Reactive Routing, Flat and Hierarchical Routing Protocols, Data Aggregation and Routing in WSN, Energy-Efficient ,Multipath Routing Protocols, Sensor Network Mobility and its Impact on Routing Analyze WSN routing protocols & routing techniques.BTL4: Analyzing
5 IoT Data Management and Processing- Data Collection, Data Storage &  Cloud Integration in IoT Systems, Big Data, Data Processing Techniques in IoT, Real-time Data Analytics, Data Fusion and Integration Techniques. Utilize cloud data storage, processing, and analytics in IoT.BTL3: Applying
6 Security Challenges in WSN and IoT- Security in WSN and IoT: Introduction and Challenges, Authentication and Access Control, Data Privacy and Integrity, Key Management Techniques in WSN, Security Protocols for IoT , Secure Communication in WSN, Attacks in WSN and IoT and Mitigation Understand the security challenges in WSN and IoT, including data privacy, integrity, and key management techniques.
BTL4: Analyzing
Strategies
7 IoT Standards and Protocols- Standards for IoT, Role of Standards in IoT Interoperability IPv6 and IoT Standards, Industry-Specific IoT Standards (Healthcare, Smart Cities), Open Standards vs Proprietary Standards Understand IoT standards, IPv6, and industry-specific protocols for IoT .BTL2: Understanding
8 WSN and IoT Application Development- Application Design in WSN and IoT, Building Smart Systems: Smart Homes, Smart Cities . Develop applications for smart homes, smart cities, and other IoT apps.BTL3: Applying
9 WSN and IoT Application Development- IoT Application Development Platforms , WSN-based Environmental Monitoring Systems, IoT in Healthcare: Remote Monitoring, Wearables. Analyze IoT application platforms used for environmental monitoring and healthcare. BTL4: Analyzing
10 Energy Management in WSN and IoT-Energy- Efficiency Challenges in WSN, Power Management Techniques in WSN. Understand energy management challenges. BTL2: Understanding
11 Energy Management in WSN and IoT -Low Power Operation in IoT Devices ,Energy Harvesting in IoT, Power-Aware Routing in WSN, Battery Life

Optimization Techniques, Energy-Efficient IoT Applications

 Analyze energy management challenges in WSN BTL4: Analyzing
12 Emerging Trends in WSN and IoT- 5G and Its Impact on IoT,Cloud and Fog Computing Integration with IoT,Artificial Intelligence and Machine Learning in IoT. Justify  the impact of 5G, cloud, AI, and ML in IoT.BTL5: Evaluating
13 IoT in Industry 4.0Industry 4.0 Concepts and IoT,Smart Manufacturing and IoT,IoT in Industrial Automation, Cyber-Physical Systems and IoT,Predictive Maintenance using IoT,Industrial IoT Protocols and Standards. Understand the role of IoT in smart manufacturing, industrial automation.
BTL2: Understanding
14 Future Directions in WSN and IoT Standards- Next-Generation IoT Architectures, Integration of IoT with AI and Big Data, Autonomous Systems and IoT,The Role of 5G in IoT Evolution. Evaluate next-generation IoT architectures and its integration.BTL5: Evaluating
Textbooks References:

1.      Wireless Sensor Networks: Principles and Practice by Kazem Sohraby, Daniel Minoli, Taieb Znati

2.      Internet of Things: A Hands-On Approach by Arshdeep Bahga, Vijay Madisetti

Other References:

1.      Internet of Things: From Research and Innovation to Market Deployment by Ovidiu Vermesan, Peter Friess

2.      Wireless Sensor Networks: Technology, Protocols, and Applications by Ian F. Akyildiz, Mehmet Can Vuran

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3:            Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

  Web Development (C#) Syllabus

Course Code: LMC0343 Course Title: Web Development(C#)   (3 Credits)
Course Objectives: –

Ø  To gain the basic knowledge of Web Development

Ø  To understand the role of HTML and C# in web Development.

Ø  To focus on Fundaments of OOPs in C# language.

Ø  To get a basic knowledge of object-oriented programming concepts with c#.

Ø  Understand static and dynamic webpages and implementing using HTML and C#.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction: Basic concept of Internet and Web, Internet Protocols, URL and Domain Names, Uses of Web Understanding basic concepts of the Internet, Web, and associated technologies. BTL: 2 (Understanding)
2 HTML: Document Type Definition, Web Page Template, HTML Element, Ordered List, Unordered List and Anchor Element. Applying HTML elements to create a structured web page with lists, links, and other HTML components. BTL: 3 (Applying)
3 CSS: Overview of Cascading Style Sheet, Inline CSS, Embedded CSS, Using External Style sheet. Applying CSS techniques to style a webpage. BTL: 3 (Applying)
4 Tables : Overview, Table rows, cells , Headers, Span Rows and Columns, Colspan, Rowspan Style a table with CSS, Applying HTML and CSS to create and style tables effectively.BTL: 3 (Applying)
5 Forms: Form Elements, Form Controls, Input Element form Control, Scrolling Text Box, Style a form with CSS. Applying form elements and controls to create forms in HTML, with CSS. BTL: 3 (Applying)
6 Search Engine Optimization: Popular Search Engines, Search Engine Optimization, Role of Keywords, Tags in SEO, Monitoring Search engine Analyzing SEO strategies, identifying the role of keywords and meta tags. BTL: 4 (Analyzing)
7 C# and .NET – Developing C# Applications within Visual Studio .Net, Basics of C# and .NET. Understanding the basics of the C# language and the .NET framework, as well as how to set up a development environment in Visual Studio. BTL: 2 (Understanding)
8 C# Foundation – Basics of C#, Features of C#, Keywords, variables in C#, data types in C#, and operators in C #. Applying basic C# syntax, variables, data types, and operators in simple programs. BTL: 3 (Applying)
9 Control Statements and Loops – If, switch statement, fort loop, While loop, do while, break and continue. Applying control flow statements (if, switch) and loops (for, while, do while) to solve programming problems. BTL: 3 (Applying)
10 Functions:  Functions in C#, Access specifiers, Call by value, Call by reference. Applying functions in C# to write modular and efficient code. BTL3: Applying
11 Arrays: Arrays in C#, Example of Arrays Applying arrays in C# to write modular and efficient code. BTL3: Applying
12 Object Oriented Programming(Part 1): OOPs concepts, Classes, Class members, Constructors, Objects. Applying object-oriented principles such as classes and objects in C# to build structured programs. BTL3: Applying
13 Object Oriented Programming (Part 2): Inheritance in C#, Derived Class, Base Class. Applying object-oriented principles of inheritance in C#. BTL3: Applying
14 Methods: Creating Methods in detail, Calling a method, Parameters and Arguments in detail with example. Create modular, reusable code in C# with proper use of parameters and arguments. BTL6: Create
Textbook References: –

  1. Web Development and Design Foundations with HTML5 Terry Ann Felke-Morris Pearson publication
  2. Web Technology: A Developer’s Perspective N.P. Gopalan.

 Other References:

  1. Web Technologies – Uttam K.Roy – Oxford Publication.
  2. Web Technologies – Black Book – Kogent Learning Solutions Inc.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

Program Scheme for Master of Computer Applications (MCA)

Th -Theory; OP-Practical; Pro-Project; T-Total; Crd -Credit

IV Semester

Course Code Course Title

 

 Credits Sem Th/P
LMC0401 Data Science and Analytics 4 4 Th
LMC0402 Mobile Application Design and Development 4 4 Th
LMC0421 Project 4 4 OP
LMC0422 Mobile Application Design and Development Practical 4 4 OP
LMC044X Group A/B/C/D 4 4 Th
TOTAL  SEM 4 CREDIT 20  

 

Course Code Course Title

 

 Group / Elective Th/P
LMC0440 Natural Language Processing Group A (AI & ML) Th
LMC0441 Cloud Security Management Group B (Cloud Computing) Th
LMC0442 Descriptive Analytics for IoT Group C (Internet of Things ) Th
LMC0443 Full Stack Development Group D  (Web Technology) Th

 

 

Data Science and Analytics Syllabus

Course Code: LMC0401 Course Title: Data Science & Analytics (4-Credits)
Course Objectives :-

Ø  To gain the basic knowledge of Data Analytics.

Ø  To understand the role of data analysis in Data Science.

Ø  To get a knowledge of Visualization.

Ø  To master in data Visualization using different tools and software’s.

Ø  The course focuses on concepts and uses of charts, diagrams, and other graphic elements in data visualization are to be studied in this subject.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Data Science: Data Science: Benefits and uses – facets of data – Data Science Process: Defining research goals – Retrieving data – Data preparation – Exploratory Data analysis, Build the model, Advantages of data science, Disadvantages of data science. Remember the benefits, basics and uses of data science in real-world applications. BTL 1: “Remembering”
2 Real World Data: Relational Data, Transactional Data, Multi-Dimensional Data, Distributed Data, Spatial Data, Data Streams, Time Serial Data, Text and Web Data, Multimedia Data. Identify and describe different facets of data and how they impact data science processes. BTL 2: “Understanding”
3 Data Warehouse: Fundamentals of Data Warehouse and Data Marts, Data Warehouse Characteristics, Components , Approaches to build Data Marts and Data Warehouse Understand the stages of the data science process, including research goals, data retrieval etc. BTL 2: “Understanding”
4 OLAP: Introduction to OLAP, OLAP Applications, OLAP operations – Drill Down, Roll Up, Dice and Slice. Demonstrate OLAP operations such as drill down, roll up, dice etc.
BTL 3: “Applying”
5 OLTP: Introduction to OLTP, Examples of OLTP, Drawbacks and Benefits of OLTP, Difference between OLAP and OLTP. Differentiate between OLAP and OLTP systems. BTL 2: “Understanding”
6 Frequent Pattern Mining: Basic Problem Definition, Mining Association Rules ,  Apriori Algorithm , FIM Algo (Frequent Item set Mining  Algorithm), Frequent Pattern Growth Algorithm,  ECLAT Algorithm Explain algorithms such as Apriori, Frequent Itemset Mining (FIM), Frequent Pattern Growth, and ECLAT for mining association rules.BTL 2: “Understanding”
7 Regression: Concept of Supervised and Unsupervised Machine Learning , Regression- Linear Regression, Multiple Linear Regression , Polynomial Regression Implement and interpret linear regression, multiple linear regression etc. BTL 3: “Applying”
8 Classification: Introduction to classification , Evaluation of Classifiers , Linear Model- Logistic Regression , SVM , Non-Linear   Model- K-Nearest Neighbour (KNN) Implement classification models, including Logistic Regression, SVM, and K-Nearest Neighbours. BTL 3: “Applying”
9 Python Libraries for Data Wrangling-NumPy: Creating Arrays, Array Indexing, Array Iterating, Joining Array, Array Split, Array Search , Array Sort. Use NumPy for creating arrays, array indexing, iterating, joining, splitting, searching, and sorting.BTL 3: “Applying”
10 Pandas: Series, DataFrames , Read CSV, Analyzing DataFrames, Cleaning Data, Data Correlations. Matplotlib – Pyplot , Plotting, Subplot, Scatter, Bars, Histograms Understand how to work with Pandas for data analysis, including Series, DataFrames, reading CSV files etc. BTL 2: “Understanding”
11 Query Processing: Data Warehouse  with Materialized Views, Loading Data into Data Warehouses, Materialization View in DBMS, Query Optimization in DBMS, Query processing in DBMS Explain query optimization techniques and the role of materialized views in query processing within a DBMS.BTL 2: “Understanding”
12 Power BI: Power BI basics, Power BI as Data Analytics powerhouse, Business Intelligence and Role of Power BI, Data Collection in Power BI, Data Visualizations in Power BI, Decision through Dashboards and Reports in Power BI. Visualize data in Power BI to make data-driven decisions through dashboards and reports.
BTL 3: “Applying”
13 DESCRIBING DATA(Part 1): Types of Data  Types of Variables Understand different types of data and variables. BTL 1: “Remembering”
14 DESCRIBING DATA (Part 2): Describing Data with Tables and Graphs , Describing Data with Averages Manage data using tables and graphs, summarize data with averages. BTL 3: “Applying”
Textbook References: –

  1. Joel Grus’s Data Science from scratch , Oreilly
  2. Vikram Pudi and P. Radha Krshna , Data Mining , Oxford publication.

Other References:-

  1. https://www.geeksforgeeks.org/data-science-for-beginners/?ref=shm

2.      https://www.geeksforgeeks.org/power-bi-tutorial/

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

Mobile Application Design and Development

Course Code:  LMC0422 Course Title: Mobile application design and development.  (4 Credits)
Course Objectives: –

Ø  To learn Kotlin syntax, variable, function, decision making and loops.

Ø  To learn object-oriented programming concepts like Class, Object, Method, and inheritance and use them in Kotlin .

Ø  To use Android SDK, UI development, activities, intents, and working with various UI widgets.

Ø  To manage preferences, SQLite databases for data storage and retrieval.

Ø  To create responsive layouts using Constraint, Linear, and Relative layouts in Android

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Kotlin Programming: Basics of Kotlin, Decision Making Statements in Kotlin, Loop Control Statements, Creating and Calling Functions, Advantages and Disadvantages. Understand decision-making statements and loop controls  in Kotlin.BTL2:”Understanding”
2 Kotlin OOP Concept: Class, Object, Class Properties, Method, This keyword, Super keyword.  Remember the basics of OOPs in Kotlin. BTL 1: “Remembering”
3 Advance OOP Concept: Basics of Object-Oriented Programming, Inheritance, Sub class and super class, Abstract and Interface. Apply subclasses and superclasses in Kotlin. BTL 3: “Applying”
4 Introduction to Android: History of Android, Android lifecycle, Open Handset Alliance, The Android Platform Architecture, Advantages and Disadvantages. Understand the Android platform architecture and its components.
BTL 2: “Understanding”
5 Android Studio IDE: Software Development Kit in detail, Android Virtual Design, Project Panel, Logcat Panel, Anatomy of an Android application, Creating a sample Android application. Create a simple Android application in Android Studio.
BTL 6: “Create “.
6 Basics of Android: Android Terminologies like XML, View, Layout, Activity, Emulator, Manifest, Service, Broadcast Receiver, Content Providers and Intent. Understand key Android terminologies such as XML, View, Layout, Activity etc. BTL 2: “Understanding”
7 Basic Android Views: TextView with properties method and listener, EditText  and listener, Button with properties method and listener, ImageButton with properties method and listener. Implement TextView, EditText, Button, and ImageButton with appropriate properties, methods, and listeners.BTL 3: “Applying”
8 Advances Android Views: Radio Group, Radio Button with properties method and listener, CheckBox with properties method and listener. Implement RadioGroup, RadioButton, and CheckBox with their  properties, methods, and listeners.BTL 3: “Applying”
9 Date & Time Views: DatePicker with properties method and listener, TimePicker with properties method and listener. Implement DatePicker and TimePicker with properties, methods, and listeners in Android.BTL 3: “Applying”
10 Android Layouts: Android Layouts basics, Constraint Layout with Properties, Linear Layout with Properties, Relative Layout with Properties. Create responsive layouts using Constraint, Linear, and Relative layouts in Android.BTL 3: “Applying”
11 Create Shared Preferences: Basics of Preference, Creating Preference, difference between getPreference() method and getSharedPreference() method Understand the concept of shared preferences and their basic usage. BTL 2: “Understanding”
12 Shared Preferences Operations: Storing Data into Preference, Editor class, putString() method, difference between commit() and apply(), Retrieving data from preferences, use of getString() Implement operations for storing data into shared preferences using the Editor class. BTL 3: “Applying”
13 Managing data using SQLite: SQLite Database Creation using MVC Architecture, Insert Data Dynamically using MVC Architecture, retrieve data from SQLite Database using MVC Architecture. Dynamically insert & Retrieve data into a SQLite database using MVC architecture. BTL 3: “Applying”
14 Database Operations: Update Data to SQLite Database table Dynamically using MVC Architecture, Delete Data from table Dynamically using MVC Architecture. Perform delete operations on data in SQLite tables using MVC architecture. BTL 3: “Applying”
Textbook References: –

  1. Java: The Complete Reference Hebert Schildt, Mc Graw Hill
  2. Object-Oriented Programming with C++ and Java Debasis Samanta, Prentice Hall India.

Other References:-                                                                                  

  1. Programming with Java A Primer, E. Balaguruswamy Tata McGraw Hill Companies.
  2. Java Programming John P. Flynt Thomson.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

       Natural Language Processing

Course Code: LMC0440 Course Title: Natural Language Processing                                     (4-Credits)
Course Objectives :-

Ø  To understand and explain the basic concepts of NLP

Ø  To understand machine learning techniques to NLP.

Ø  To analyze text data for extracting meaningful insights using NLP techniques.

Ø  To use  tools and libraries for real-world NLP applications

Ø  To assess and evaluate NLP models for their effectiveness and efficiency on specific tasks.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Natural Language Processing: Definition and Scope of NLP, Applications of NLP, Challenges in NLP, Overview of the NLP Pipeline. Define the scope, applications, and challenges of NLP.BTL 1: “Remembering”
2

Text Preprocessing Techniques: Tokenization, Stop words Removal, Stemming and Lemmatization, Text Normalization, Part-of-Speech Tagging (POS), Handling Non-English Text

 Understand and apply text preprocessing techniques.
BTL 3: “Applying”, BTL 2: “Understanding”
3

Introduction to Language Models: Definition and Importance of Language models, Types of Language models: Statistical vs. Neural, Applications of Language Models in NLP

 Understand the various applications of language models in NLP tasks.BTL 2: “Understanding”
4

Probabilistic Language Models and Smoothing: Probabilistic Language Models, Markov Models, Smoothing Techniques (Additive Smoothing, Good-Turing Smoothing), Evaluation of Language Models

 Analyse the performance of language models using appropriate evaluation metrics. BTL 4: “Analysing”
5

Word Representations: Basics of Word representation in NLP, One-Hot Encoding, Distributed, Dense vs. Sparse Vectors, Evaluation of Word Embeddings.

 Understand the basic concept of word representation in NLP. BTL 2: “Understanding”
6

Word Embedding Techniques: Word2Vec: Skip-gram and CBOW, GloVe: Global Vectors for Word Representation, FastText: Subword Embeddings for Rare Words, Contextual Embeddings: BERT, ELMo

 Understand & implement Word Embedding Techniques. BTL 2: “Understanding”, BTL 3: “Applying”
7 Text Classification-Supervised Learning: Naive Bayes, Logistic Regression, SVM, Feature Extraction, Classifiers for Text Classification, Evaluation Metrics. Analyze text classification models. BTL 4: “Analyzing”
8

Sentiment Analysis: Introduction to Sentiment Analysis, Lexicon-Based Approaches, Machine Learning Approaches, Feature Extraction for Sentiment Analysis, Evaluating Sentiment Models.

 Understand the concept of sentiment analysis and its importance in NLP applications.
BTL 2: “Understanding”.
9 Named Entity Recognition (NER): Definition of Named Entities, Rule-Based NER Systems, Statistical Methods for NER, Deep Learning Methods for NER, Evaluation of NER Models Implement statistical and deep learning-based methods for NER. BTL 3: “Applying”
10

Part-of-Speech Tagging: Basics of POS Tagging, Rule-Based & Statistical POS Tagging, Deep Learning Methods for POS, POS Tagging Evaluation .

 Analyze the fundamentals of Part-of-Speech (POS) tagging in NLP.BTL 4: “Analyzing”
11 Machine Translation: Overview of Machine Translation (MT), Rule-Based and Statistical Machine Translation, Neural Machine Translation, Challenges in MT, Evaluation of Machine Translation. Differentiate between rule-based, statistical, and neural machine translation approaches. BTL 2: “Understanding”
12 Information Retrieval and Question Answering: Introduction to Information Retrieval (IR), Indexing and Searching Techniques, QA Systems, Evaluating IR and QA Systems (Precision, Recall) Develop question-answering systems, evaluating them with precision and recall. BTL 3: “Applying”
13 Real-Life Applications of NLP: NLP in Healthcare, NLP in Legal Systems, NLP for Social Media Analysis, NLP for Chatbots and Virtual Assistants, Ethical Considerations and Bias in NLP Applications Analyze the use of NLP for chatbots, virtual assistants, and other conversational AI systems. BTL 4: “Analyzing”
14 Future Trends and Challenges in NLP: Transformer Models,  Pre-trained Language Models and Fine-Tuning, Transfer Learning in NLP, Multimodal NLP, NLP for Low-Resource Languages Evaluate the latest trends in NLP, including pre-trained language models. BTL 5: “Evaluate “
Textbook References: –

  1. Speech and Language Processing by Daniel Jurafsky and James H. Martin
  2. Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Other References:-

  1. Foundations of Statistical Natural Language Processing by Christopher Manning and Hinrich Schütze
  2. Deep Learning for Natural Language Processing  by Palash Goyal, Sumit Pandey, Karan Jain

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

Cloud Security Management

Course Code:  LMC0441 Course Title: Cloud Security Management (4 Credits)
Course Objectives:-

Ø  To understand the fundamentals of cloud computing and deployment models.

Ø  To learn cloud security management strategies and risk mitigation techniques.

Ø  To explore the tools and technologies used in cloud security.

Ø  To understand how to design, implement, and manage secure cloud infrastructure.

Ø  To evaluate security best practices for different cloud environments.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Cloud Computing: Importance and Evolution, Cloud Computing Models (IAAS, PAAS, SAAS) & their distinctions, Deployment Models: Private , Public and Hybrid. Define cloud computing and explain its importance and evolution over time.BTL1: “Remembering”
2 Cloud Service Providers & Marketplace: Overview of AWS, Azure, Google Cloud, Comparing Cloud Service Offerings,and Understanding Cloud Market Trends. Compare and contrast the cloud services provided by AWS, Azure, and Google Cloud. BTL 2: “Understanding”
3 Cloud Architecture and Components: Virtualization, Elasticity, and Resource Pooling, Cloud Infrastructure: Compute, Storage, and Network components, Service Level Agreements (SLAs) and Cloud Management. Explain the concepts of virtualization, elasticity, and resource pooling in the cloud.
BTL 2: “Understanding”
4 Designing Secure Cloud Architectures: Security-focused cloud design principles, Implementing secure cloud infrastructure, Key security configurations  Implement secure cloud infrastructure with key security configurations. BTL 3: “Applying”
5 Cloud Security Fundamentals: Basic Principles of Cloud Security, Security Challenges, Shared Responsibility Model, Data Privacy, and Compliance Requirements. Understand the data privacy and compliance requirements for cloud security. BTL 2: “Understanding”.
6 Identity and Access Management (IAM) in Cloud: Authentication, Authorization, and Auditing, Multi-Factor Authentication (MFA), Access Control Models (RBAC, ABAC). Understand the concepts of authentication, authorization, and auditing in cloud environments. BTL 1: “Remembering”
7 Cloud Data Security and Encryption: Data Security Risks, Cryptographic Principles (Sym vs. Asy Encryption), Encryption in Transit and at Rest, Key Management Systems, Cloud Data Loss Prevention. Understand encryption techniques for data in transit and at rest, and implement key management systems for cloud data security.BTL 3: “Applying”
8 Cloud Network Security: Network Security Risks, Virtual Private Networks (VPNs), Virtual Private Clouds (VPCs), Firewalls, Load Balancers, Intrusion Detection and Prevention Systems. Analyze the network security risks in cloud environments. BTL 4: Analyzing “
9 Risk Management and Compliance in Cloud: Identifying Risks, Risk Assessment, Compliance Frameworks (GDPR, HIPAA, PCI-DSS), Third-Party Audits, Certifications. Identify the key risks in cloud environments and perform risk assessment techniques. BTL 3: “Applying”
10 Cloud Security Monitoring and Incident Response: Security Monitoring Tools, Incident Response Plans, Breach Detection & Mitigation, Cloud Forensics. Analyze the role of security monitoring tools in detecting cloud security incidents. BTL 4: Analyzing “
11 Application Security in Cloud: Secure Software Development Lifecycle (SDLC), Application Security Tools (WAF, DDoS Protection), Security Testing for Cloud Apps. Implement security tools like Web Application Firewalls and protection for securing cloud applications. BTL 3: “Applying”
12 Securing Hybrid and Multi-Cloud Environments: Multi-Cloud Security Challenges, Best Practices, Data Integration Risks, Cross-Cloud Communication Security. Apply best practices for securing hybrid and multi-cloud environment etc. BTL 3: “Applying”
13 Advanced Cloud Security Techniques: AI & ML in Cloud Security, Blockchain for Cloud Security, Zero Trust Architecture, and Cloud Automation. Implement cloud automation tools to enhance security in cloud environments. BTL 3: “Applying”
14 Cloud Security Best Practices & Future Trends: DevSecOps, Future Trends (AI, Quantum Computing), Cloud Security Certifications (ISO, SOC2, NIST).  Analyze the future trends in cloud security, including AI, quantum computing, and their implications. BTL 4: Analyzing “
Textbook References: –

1.      Cloud Computing Implementation, Management, and Security , “By John W.Ritting House and James F.Ransome,”Taylor and Francis Group.

2.      White Book of Security.

Other References:-

1.      Cloud Computing: Concepts, Technology & Architecture written by Zaigham Mahmood, Ricardo Puttini, and Thomas Erl.

2.      Cloud Computing: Methodology, Systems, and Applications by Lizhe Wang, Rajiv Ranjan.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

                

                 Descriptive Analytics for IoT       

Course Code:  LMC0442 Course Title:  Descriptive Analytics for IoT(4Credits)
Course Objectives: –

Ø  To understand the fundamental concepts of the Internet of Things, including its architecture, components, and applications.

Ø  To analyze and evaluate IoT communication protocols, focusing on their role in the IoT ecosystem.

Ø  To design and implement IoT systems, incorporating hardware, software, and networking components.

Ø  To examine security and privacy issues in IoT systems and propose methods for mitigating risks.

Ø  To explore emerging IoT trends and technologies, and assess their potential impact on industries and society.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to IoT : Definition and evolution of IoT, IoT application domains, IoT architecture: Sensors, communication, processing, and actuation Recall the definition, evolution, application domains of IoT, and  IoT architecture.BTL1: “Remember”
2 IoT Architecture and Design Principles:  Layers of IoT architecture: Perception, network, edge, and application layers, Design principles and considerations for IoT systems. Understand the layers of IoT architecture and describe design principles for IoT systems.BTL2: “Understand”
3 IoT Communication Protocols:  Basics,   Wi-Fi, Zigbee, Bluetooth, MQTT, CoAP, Protocols for different IoT use cases. Identify and compare various IoT communication protocols. BTL3: “Apply”
4 IoT Devices and Sensors:  Types of IoT devices (sensors, actuators, controllers), Types of sensors used in IoT systems (temperature, motion, pressure, etc.), Integration of sensors with IoT systems Identify different types of IoT device, sensors used in IoT systems, and integrate them into IoT systems.BTL3: “Apply”
5 Data Processing and Management in IoT: Big data in IoT, Data acquisition, storage, and processing techniques, Cloud computing in IoT systems Understand big data in IoT, including data acquisition, storage etc in IoT data management.BTL2: “Understand”
6 IoT Supporting Services: Computing Using a Cloud, Principles of Cloud Computing, The Relationship Between the IoT and Cloud Computing Platform for IoT Applications/Services, Virtualization, Cloud Deployment Models , Cloud Platform Architecture Explain the principles of cloud computing and its role in supporting IoT applications and services, and describe virtualization, cloud deployment models, and architecture for IoT.BTL2: “Understand”
7 Networking and IoT Communication Technologies:  Classification of IoT protocols, Network Layer Routing Protocols &  Encapsulation Protocols, Session Layer Protocols, IoT Management Protocol, Role of networking, scalability, efficiency and Challenges in networking Identify and analyze IoT networking technologies and protocols (routing, session, encapsulation) and understand their impact on scalability and efficiency in IoT systems.BTL4: “Analyze”
8 Cloud and Edge Computing in IoT: Cloud computing architecture in IoT, Benefits and challenges of edge computing in IoT, IoT cloud platforms: AWS IoT, Azure IoT, Google Cloud IoT Understand the architecture, benefits of cloud computing for IoT systems.BTL3: “Apply”
9 Security in IoT:  IoT security challenges: Authentication, confidentiality, and integrity, Security protocols for IoT devices, IoT security standards (e.g., ISO/IEC) Identify security challenges  and apply security protocols and standards to protect IoT devices.BTL3: “Apply”
10 Privacy Issues in IoT: Privacy risks in IoT ecosystems, Data protection and anonymization techniques, Legal and ethical considerations of IoT data collection. Understand privacy risks in IoT ecosystems considering legal and ethical implications of IoT data collection.BTL2: “Understand”
11 IoT Standards and Frameworks:  Role of standards in IoT development, Overview of IoT standards (IEEE, ITU, IETF), Industry frameworks and IoT ecosystems. Describe the role of standards in IoT development and compare various IoT standards (IEEE, ITU, IETF) and industry frameworks.BTL3: “Apply”
12 Real-World Applications of IoT:  IoT in healthcare, agriculture, smart cities, and industrial applications, Integration of IoT in existing infrastructures Identify and explain the integration of IoT in real-world applications and describe how IoT integrates into existing infrastructures.BTL4: “Analyze”
13 Future Trends in IoT: Emerging trends in IoT: AI, machine learning, block chain integration, Challenges and opportunities in the future of IoT. Analyze emerging trends in IoT, including the integration of AI, machine learning, block chain etcBTL4: “Analyze”.
14  Challenges in IoT and Case Studies: Security Concerns and Challenges, Real time applications of IoT , Home automation, Automatic lighting , Home intrusion detection, Cities,  Smart parking , Environment- Weather monitoring system, Agriculture –Smart irrigation Identify security concerns and challenges in IoT, and evaluate real-world case studies (e.g., smart homes, smart parking, weather monitoring) to assess IoT application outcomes.BTL5: “Evaluate”
Textbook References: –

1.      Internet of Things by Simone Cirani, Gianluigi Ferrari, Marco Picone, Luca Veltri

2.      Internet of Things Architecture and Design Principles by Dr. Raj Kamal

Other References:-

  1. Dieter Uckelmann, Mark Harrison, Florian Michahelles, Architecting the Internet of Things
  2. Arshdeep Bahga and Vijay Madisetti, Internet of Things: A Hands-on Approach

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)

 

 

 

 

 

 

 

 

 

Full Stack Development

Course Code:  LMC0443 Course Title: Full Stack Development (4 Credits)
Course Objectives: –

Ø  To gain a thorough understanding of front-end, back-end, and database technologies.

Ø  To develop proficiency in front-end technologies like HTML, CSS, and JavaScript.

Ø  To learn to work with relational databases.

Ø  To implement effective database design and perform CRUD operations.

Ø  To understanding frameworks involves recognizing their role in simplifying development, exploring their features and benefits.

Course Contents

Unit No. Unit Description Learning Outcome
1 Introduction to Full Stack Development: Overview of Frontend and Backend developer, Languages & Role of Front-end & Back-end development, Frontend & Backend frameworks. Recall the roles, languages, and frameworks for frontend and backend development.BTL1: “Remember”
2 HTML Fundamentals: HTML basics, Structure of a Web Page, Tags, Elements, attributes, Colors and image syntax, Formatting Elements etc. Understand a simple HTML Tags, its structure and elements.BTL2: “Understand”
3 CSS Fundamentals: CSS Basics, Syntax, Types of CSS: Inline, Internal and External CSS, Selectors, Properties, Box model, Website layout, Adv. and disadv. & Applications. Apply CSS to style web pages using selectors, properties, and the box model. BTL3: “Apply
4 JavaScript: JS basics, Syntax, Output, Comments, Variables and datatypes, Operators, Statements, Loops, Objects, Functions, Arrays, Adv. and disadv. & Applications. Understand JavaScript basics and implement variables, loops, and functions.BTL2: “Understand”
5 Bootstrap: A frontend framework: Containers, Grid System, Text/Typography, Colors, Tables, Images, Alerts, Buttons, Cards, Adv. and disadv. & Applications. Use Bootstrap to create responsive web pages with its grid system and UI components.BTL3: “Apply”
6 Python: Basics, Indentation, Comments, Variables and data type, Strings, Operators, List, Set, Tuples, Dictionary, Loops, Functions, Arrays, Classes, Inheritance & Polymorphism. Understand Python basics, including data types, loops, and object-oriented concepts.
BTL2: “Understand”
7 PHP: Basics, Syntax, Case Sensitivity, Comments, Variables, Echo/print, Datatypes, Numbers, Math, Operators, Loops, Function, Arrays, Forms, Date and time, File handling. Understand basic server-side functionality using PHP syntax and functions.BTL2: “Understand”
8 C#: Syntax, Comments, Variables, Datatypes, Math, Operators, Loops, Arrays, Methods, Classes & members, Inheritance, Polymorphism, Adv. and disadv. & Applications. Use C# syntax, methods, and object-oriented concepts like inheritance and polymorphism.
BTL3: “Apply”
9 Django: Backend framework–Django: Introduction, Installation, Create project, Create app, URLs, Templates, Models, Data insertion, updation and deletion. Create a basic Django project with templates, models, and data manipulation.BTL3: “Apply
10 Express.js: Introduction, Setup, Middleware, Request and response. Routing, Templating, JavaScript in EJS. Set up an Express.js app with routing, middleware, and EJS templating. BTL3: “Apply”
11 MySQL Databases: Basic commands, Operators, Keywords, Updation and deletion, Math, Join, Union, Statements, Constraints, Keys, Functions, Adv. and disadv. & Applications. Use MySQL commands to manage databases, including CRUD operations and joins.BTL3: “Apply”
12 MongoDB: A NOSQL database: Datatypes, MongoDB: A NoSQL database, Data modelling, Operators, Aggregation pipeline operators, Aggregation Stages, Query modifiers. Understand MongoDB concepts, including data modeling and basic query operations.BTL2: “Understand”
13 Advanced MongoDB Operations: Aggregation and geospatial commands, Query & Write Operation Commands, Query Plan Cache and Authentication Commands, User Management Commands, Role Management Commands, Replication AND Session commands, CRUD: document, MongoDB Text Search, Shell, MongoDB Atlas and Compass, Applications.  Analyze advanced MongoDB operations, including aggregation, geospatial queries, write operations, user and role management, replication, and document CRUD operations using MongoDB Atlas and Compass.BTL4: “Analyze”
14 Full stack security: Threat Modelling for Full-Stack Applications, Risks in Full Stack Application Environments, Mitigating Risks in Full Stack Application Environments, Role of Cryptography in Full Stack Security, Common Vulnerabilities and Best Practices, Database Security and Data Protection. Evaluate security risks in full-stack applications, understand the role of cryptography, apply best practices for mitigating risks, and secure databases and data in full-stack development.BTL3: “Apply”,BTL5: “Evaluate”
Textbook References: –

1.      Full Stack Web Development For Beginners by Riaz Ahmed.

2.      Bootstrap in 24 Hours, Sams Teach Yourself by Jennifer Kyrnin.

Other References:-

1.      Programming basics with C# by Dr. Svetlin Nakov.

2.      Programming basics with Javascript by Dr. Svetlin Nakov.

(Bloom’s Taxonomy: BT level 1: Remembering; BT level 2: Understanding; BT level 3: Applying; BT level 4: Analyzing; BT level 5: Evaluating; BT level 6: Creating)