V Sem Scheme Syllabus_2022-2026 (1) PDF

SEMESTER – V Sr. No. Course Code Courses L T P Credit 1 CS3CO40 Software Engineering 3 0 2 4 2 CS3CO43 Computer Networks 4 0 2 5 3 CS3ELxx Elective-2...

SEMESTER – V Sr. No. Course Code Courses L T P Credit 1 CS3CO40 Software Engineering 3 0 2 4 2 CS3CO43 Computer Networks 4 0 2 5 3 CS3ELxx Elective-2 3 0 2 4 4 CS3ELxx Elective-3 3 0 2 4 EN3HS04 Fundamentals of Management, Economics 3 0 0 3 5 & Accountancy 6 EN3NG09 Soft Skills-III 2 0 0 2 7 OE000xx Open Elective-1 3 0 0 3 Total 21 0 8 25 Total Contact Hours 29  Course Code Course Name Hours Per Week Software Engineering L T P Credits CS3CO40 3 0 2 4 Course Learning Objectives (CLOs): CLO01 Knowledge of basic SW engineering methods and practices, and their appropriate application. Along with general understanding of software process models such as the waterfall and evolutionary models CLO02 Understanding of software requirements and the SRS documents. Describe data models, object models, context models and behavioral models with CLO03 understanding of different software architectural styles. Understanding of software testing approaches such as unit testing and integration CLO04 testing. Describe software measurement and software risks. Understanding on quality control, software metrics and how to ensure good quality CLO05 software. Unit 1 Software Engineering – Definition, Process, Evolution and Myths, Generic Process Model, Framework, Process Models – Waterfall, Incremental, Evolutionary, Spiral, Component Based Model, Rational Unified Process Unit 2 Requirement Analysis, Stakeholders, Elicitation Techniques, Requirement Modelling - Use Cases, Activity Diagrams, Swimlane Diagrams, Data Modelling, Data Flow Diagram, Overview of Class Based Modelling, requirement Tracking. Unit 3 Principles of Software Design, Design Concepts – Abstraction, Architecture, Modularity, Relationships, Design Model, Component Design, User Interface Design, Configuration Management Unit 4 Software Quality, Approaches for Quality Assurance, Software Testing, Verification and Validation, Types of Testing, Risk Assessment, Risk Mitigation, Monitoring and Management Unit 5 Software Metrics, Process Metrics, Product Metrics, Function Oriented Metrics, Software Project Estimations, Function Point Based Metrics, COCOMO Models, Project Scheduling, Effort Distribution Textbooks:  1. Roger S. Pressman, Software Engineering: A Practitioner’s Approach, McGraw- Hill. 2. Ian Sommerville, Software Engineering, Pearson Education Inc., New Delhi References: 1. Fundamentals of Software Engineering by Rajib Mall, – PHI Course Outcomes (COs) After completion of this course the students shall be able to: Students will have thorough understanding of the basic structure and operation of CO01 software & various SDLC models. Students will be able to trace out requirements of a software to be build and also learn CO02 to prepare SRS. CO03 They will be able to draw the different types design models (UML Diagrams). CO04 Students will be able to understand the role & importance of SQA & software testing. CO05 They learnt different ways of maintenance in software and measuring project.  Course Code Course Name Hours Per Week CS3CO43 Computer Networks L T P Credits 4 0 2 5 Course Learning Objectives (CLOs): CLO01 Describe how computer networks are organized with the concept of layered approach. CLO02 Implement a simple LAN with hubs, bridges, and switches CLO03 Describe how packets on the Internet are delivered CLO04 Analyse the contents in each Data Link layer packet, based on the layer concept. CLO05 Design logical sub-address blocks with a given address block CLO06 Describe how routing protocols work and decide routing entries given a simple example of network topology Unit-1 MAC Sublayer: Static and Dynamic Chanel Allocation in LAN, MAC protocols-ALOHA and Slotted ALOHA, CSMA, CSMA/CD, CSMA/CA, Collision Free protocols, Limited Contention Protocols. Ethernet-Ethernet Cabling, Frame Format, Binary Exponential Back-off Algorithm, Ethernet Performance, Fast and Gigabit Ethernet, MAC address. Unit-2 Internetworking, Tunnelling, Fragmentation and Reassembly. IP protocol, IPv4 Addresses, Subnet Addressing, Subnet Mask, Supernetting CIDR, NAT, ICMP-header, message type, trace route, ARP & RARP, BOOTP and DHCP: Address allocation, configuration & packet format, OSPF and BGP, Comparative study of IPv4 & IPv6. Unit-3 Network Layer: Design issues, Routing algorithms: Dijkstra's algorithm, Bellman-ford algorithm, Link State Routing, Hierarchical Routing, Congestion Control Algorithms: General Principles of Congestion control, Prevention Policies, Congestion Control in Virtual-Circuit Subnets, Congestion Control in Datagram subnets. QOS-techniques for achieving good QOS, Traffic Management, Integrated and Differentiated Services. RSVP Unit-4 Transport Layer: Design Issues, Transport Service Primitives, Socket Programming, TCP: Connection Management, Reliability of Data Transfers, TCP Flow Control, TCP Congestion Control, TCP Header Format, TCP Timer Management. UDP: Header Format, RPC, RTP, Session layer: Authentication, Authorization, Session layer protocol (PAP, SCP, H.245). Unit-5 Presentation layer: Data conversion, Character code translation, Presentation layer protocol. Application Layer: WWW Architectural Overview, URL-Static and Dynamic Web, FTP, SSH, Email- Architecture and Services, SMTP, DNS-Name System, Resource Records, Name Servers, Network Management (SNMP). Textbooks: 1. Computer Networks-V Edition, Andrew S. Tanenbaum-Pearson Education (Chapter No.4-7). 2. Data and Computer Communication-VIII Edition, William Stallings-Pearson Education (Part-3-6) 3. Data Communication and Networking- V Edition, Behrouz A.Fourouzan- Mc Graw Hill Publication (Part-3-6). 4. Communication Networks-Fundamental concepts and key Architecture, Alberto Leon- Garcia &Indra Widjaja-TMH (Unit1,2,7,8,10,12) Practical Understanding 1. Data Communication Principles for fixed and wireless networks-Aftab Ahmad, Kluwer Academic Publishers. 2. Data Communications Networking Devices: -Operation, Utilization, Lan and Wan Interworking-IV Edition, Gilbert Held-John Wiley and Sons. Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Analyse the requirements for a given organizational structure and select the most appropriate networking architecture and technologies CO02 Understanding of the use of various networking devices such as L-2 switch, L-3 Switch and Routers. CO03 Understanding of data link layer protocols, multi-channel access protocols and IEEE 802 standards for LAN CO04 Apply the routing and congestion in network layer with routing algorithms using simulators and classify IPV4 and IPV6 addressing scheme CO05 Describe the elements and protocols of transport layer. CO06 Understanding of network security and define various protocols such as FTP, HTTP, Telnet, DNS  Course Code Course Name Hours Per Week L T P Credits CS3EA10 Artificial Intelligence 3 0 2 4 Course Learning Objectives (CLOs): CLO01 Introduce Artificial Intelligence and various search algorithms CLO02 To teach the fundamentals of knowledge representation (logic-based, frame-based, semantic nets), inference and theorem proving CLO03 To teach reasoning and learning in AI CLO04 To teach the fundamentals of AI to solve real world problems. CLO05 To demonstrate Game Playing Strategies. CLO06 To introduce basics of Machine Learning and Deep Learning. Unit 1: Introduction to artificial intelligence, various types of production systems, Characteristics of production systems, Study and comparison of breadth first search and depth first search techniques. Unit 2: Optimization Problems: Hill-climbing search Simulated annealing likehillClimbing, BestfirstSearch.A*algorithm*algorithms etc, and various types of control strategies, Heuristic Functions, Constraint Satisfaction Problem. Unit 3: Knowledge Representation, structures, Predicate Logic, Resolution, Refutation, Deduction, Theorem proving, Inferencing, Semantic networks, Scripts, Schemas, Frames, Conceptual dependency. Unit 4: Uncertain Knowledge and Reasoning, forward and backward reasoning, monotonic and nonmonotonic reasoning, Probabilistic reasoning, Baye’s theorem, Decision Tree, Understanding, Common sense, Planning. Unit 5: Game playing techniques like minimax procedure, alpha-beta cut-offs etc, Study of the block world problem in robotics. Textbooks: 1. Elaine Rich, Kevin Knight and Nair, Artificial Intelligence, MH 2. S.Russel,PeterNorvig,ArtificialIntelligence:AModernApproach,Pearson. References: 1. Saroj Kausik, Artificial Intelligence, Cengage Learning4 2. Padhy, Artificial Intelligence and Intelligent Systems, Oxford UniversityPress, 3. Nils Nilsson, Artificial Intelligence: A New Synthesis, MorganKaufmann. Course Outcomes (COs): After completion of this course the students shall be able to: CO01 To understand strategies for solving various search problems in AI CO02 To get familiar with algorithms in AI. CO03 To understand the fundamentals of knowledge representation in AI CO04 To understand working knowledge of reasoning in the presence of incomplete and/or uncertain information CO05 To apply knowledge representation, reasoning, and natural language techniques to robotics problems. CO06 To understand the game theory and apply it in various applications  Course Code Course Name Hours Per Week CS3EL14 Internet of Things L T P Credits 3 0 2 4 Course Learning Objectives (CLOs): CLO01 Describe IOT, its applications CLO02 Make a small working model of Project based on IOT CLO03 Different Communication Models of IoT and the API’s available CLO04 Analyze the different levels of IOT like FunctionalView/ Operational View. CLO05 Describe about the security issues in IOT and layer attack model in IOT CLO06 Describe how the IOT helps the human being by easing life. Unit-1 Introduction : Definition, Characteristics of IoT, IoT Architectural view, Physical design of IoT, IoT Protocols, Communication Models of IoT, IoT Communication APIs, IoT Enabling Technologies. Unit-2 IoT and M2M: Machine-to-Machine (M2M), Difference between M2M and IoT, SDN (Software Defined Networking) and NFV (Network Function Virtualization) for IoT, Data Storage in IoT, IoT Cloud Based Services. Unit –3 IoT Platform Design Methodology: Specifications of Purpose and Requirement, Process, Domain Model, Information Model, Service, IoT Level, Functional View, Operational View, Device and Component Integration, Application Development. Unit –4 Security issues in IoT: Introduction, Vulnerabilities, Security requirements and threat analysis, IoT security Tomography, layered attacker model, identity management and establishment, access control. Unit-5 Application areas of IoT: Home Automation, smart lighting, home intrusion detection, smart cities, smart parking, environment, weather monitoring system, agriculture. Text Books: 1. Arshdeep Bahga, Vijay Madisetti, “Internet of Things – A hands-on approach”, Universities Press.  2. Rajkamal,”Internet of Things”, Tata McGraw Hill publication References: 1. Olivier Hersent, David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications and Protocols”, Wiley 2. Donald Norris “The Internet of Things: Do-It-Yourself at Home Projects for Arduino, Raspberry Pi and BeagleBone Black”, McGraw Hill publication. Open Learning Source: 1. https://nptel.ac.in/courses/106105166/ 2. https://github.com/connectIOT/iottoolkit Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Analyse the requirements of an IOT and M2M. CO02 Understanding of the use of various sensors/ actuators and micro-controllers CO03 Understanding layered architecture of the IOT model. CO04 Apply the different views of the IOT Model. CO05 Describe the layered attack model of IOT and security issues. CO06 Understanding the application areas of IOT.  Course Code Course Name Hours Per Week L T P Credits CS3EL12 Cloud Computing 3 0 2 4 Course Learning Objectives (CLOs): CLO01 To provide students with fundamental and essentials of cloud CLO02 Describe Application which are used in daily life regarding cloud CLO03 To learn virtualization Techniques CLO04 To understand collaboration between user and service provider CLO05 To Explore area and working knowledge of cloud Provider Unit-1 Introduction to cloud computing, characteristics of cloud computing as per NIST, cloud reference model, application of cloud computing ECG analysis, protein structure prediction, cloud deployment models. Unit-2 Virtualization, virtualization advantages, Full virtualization, para- virtualization, hypervisors. Cloud interoperability, cloud service management, cloud analytics, Cloud broker, Capex, Opex, cloud architecture. Unit-3 Platform as a service, Infrastructure as a service, software as a service, Desktop as aservice, Backup as a service, DRaaS, Introduction to SLA, SLA lifecycle, SLA management,Business continuity plan. Unit-4 Cloud security fundamentals, vulnerability assessment, security architecture, identitymanagement and access control, data at rest, data in flight, data in motion, security in virtualization. Unit-5 Cloud application development platforms, Xen hypervisor, AWS, Google app engine,open stack. Text Books: 1. S. Chand,R.Buyya, C. Vecchiola, S.T. Selvi, “Mastering Cloud Computing,”McGraw Hill Education 2. Velte, A. Velte and R. Estenpeter, “Cloud Computing –A practical approach,McGraw Hill Education  References: 1. K. Chandrasekaran, “Essentials of Cloud Computing,” CRC Press 2. Thomas Erl, Zaigham Mahmood, RichardoPuttini, Cloud Computing: Concepts, Technology & Architecture, ServiceTech press 3. K Jayaswal, J Kallakurchi, Donald Houde, Deven Shah, Cloud ComputingBlack Book, Dreamtech Press. Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Analyze the requirements for a given organizational structure and select the mostappropriate cloud Application CO02 Working with virtualization and understanding between user and corporation. CO03 To Understand working knowledge of data and recovery process in virtual form CO04 To understand working of security aspect ,privacy issue and cloud developmentplatform List of Experiment: 1 Create Amazon Free Tier Account. 2 Create IAM Account in AWS. 3 Create your first EC2 instance. 4 Assigning Elastic IP address to Instance (Static IP address). 5 Configure AWS S3 Bucket. 6 Create VPC- Virtual Private Cloud with Internet Gateway and Route Table. 7 Create AWS Elastic Load Balancer. 8 Create a lambda Function and launch a new instance. 9 Launching RDS instance in AWS. 10 Case Study of Open Stack , Hypervisor and Google app Engine.  Course Code Course Name Hours Per Week L T P Credits CS3EL13 Data Science 3 0 2 4 Course Learning Objectives (CLOs): CLO01: Understand the definition, history, and components of Data Science, and its importance in the business world. CLO02: Apply probability theory and statistical concepts for analysing data, including random variables, distributions, and statistical inference. CLO03: Gain proficiency in Exploratory Data Analysis (EDA) techniques and the Data Science process. CLO04: Develop skills in data visualization principles, tools, and creating visualizations for complex datasets. CLO05: Utilize Python as a data science tool, including libraries like SciPy, scikit-learn, PyBrain, Pylearn, and Matplotlib. Unit I: Introduction to Data Science, Definition and description of Data Science, history and development of Data Science, terminologies related with Data Science, basic framework and architecture, importance of Data Science in today’s business world, primary components of Data Science, users of Data Science and its hierarchy, overview of different Data Science techniques. Unit II: Sample spaces, events, Conditional probability, and independence. Random variables. Discrete and continuous random variables, densities and distributions, Normal distribution and its properties, Introduction to Markov chains, random walks, Descriptive, Predictive, and prescriptive statistics, Statistical Inference, Populations and samples, Statistical modelling, Unit III: Exploratory Data Analysis and the Data Science Process - Basic tools (plots, graphs, and summary statistics) of EDA - Philosophy of EDA - The Data Science Process - Case Study Unit IV: Data Visualization: Basic principles, ideas and tools for data visualization, Examples of inspiring (industry) projects, Exercise: create your own visualization of a complex dataset. Unit V: NoSQL, use of Python as a data science tool, Python libraries: SciPy and sci-kitLearn, PyBrain, Pylearn, Matplotlib, challenges and scope of Data Science project management. Textbooks: 1. Joel Grus, “Data Science from Scratch: First Principles with Python”. 2. Principles of Data Science by Sinan Oz Demir, PACKT.  References: 1. Lillian Pierson,“Data Science for Dummies”. 2. Foster Provost, Tom Fawcett, “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking”. Course Outcomes (COs): After completion of this course the students shall be able to: CO01: Demonstrate knowledge and understanding of the key concepts and components of Data Science. CO02: Apply probability and statistical techniques to analyse and interpret data. CO03: Perform Exploratory Data Analysis (EDA) and follow the Data Science process for effective data analysis. CO04: Create meaningful and informative data visualizations using appropriate tools and principles. CO05: Utilize Python and relevant libraries for data science tasks, showcasing proficiency in project management and addressing challenges in Data Science projects. List of Experiments 1. Installation, configure and run R Complier. 2. a) Write a Program to Calculate Mean of a given dataset using R. b) Write a Program to Calculate Mode of a given dataset using R. c) Write a Program to Calculate Median of a given dataset using R. 3. a) Perform cleaning of a given data set (EDA) using R. b) Perform transformation of a given data set (EDA) using R. 4. Perform Data Visualization using Pie Chart Plotting Framework using R. 5. Perform Data Visualization using Bar Chart Plotting Framework using R. 6. Perform Data Visualization using Boxplot Plotting Framework using R. 7. Perform Data Visualization using Histogram Plotting Framework using R. 8. Perform Data Visualization using Line Graph Plotting Framework using R. 9. Perform Data Visualization using Scatterplot Plotting Framework using R. 10. Perform reading data using Pandas library of Python. 11.. Perform any operation of Numpy library of Python. 12.. Perform data Visualization using Matplotlib library of Python. 13. Case study to realize storage of big data using H base, Mongo DB.  Hours per Week Total Course Code Course Name L T P Credits CS3EL17 NoSQL Database 3 0 2 4 Course Learning Objectives (CLOs): CLO01 Comprehensive NoSQL Understanding: By the end of this course, students should have a comprehensive understanding of NoSQL databases, including their history, fundamental features, scalability considerations, and the flexibility they offer in handling different types of data. CLO02 Comparison Skills: Students will be able to compare and contrast NoSQL databases with traditional relational databases (RDBMS), evaluating their respective strengths, weaknesses, and suitability for various use cases. CLO03 Classification and Taxonomy Proficiency: Students will gain proficiency in classifying and categorizing NoSQL databases based on their characteristics, allowing them to select the appropriate database type for specific data management needs. CLO04 Technical and Business Evaluation: Upon completion of the course, students will be equipped with the skills to technically evaluate NoSQL databases, considering factors like search features, scalability, and data safety. Additionally, they will be able to perform business evaluations, assessing the strategic importance of NoSQL in different contexts. CLO05 Practical Application of NoSQL: By the end of the course, students will be able to apply NoSQL databases effectively. They will have practical knowledge of key-value, document, column-oriented, and graph databases, including their features, consistency models, and real-world use cases. Students will also be able to make informed decisions about deploying and securing NoSQL databases in various scenarios. Unit I: Introduction to NoSQL Understanding NoSQL Databases, History of NoSQL, Features of NoSQL, Scalability, Cost, Flexibility, NoSQL Business Drivers, Classification and Comparison of NoSQL Databases, Consistency – Availability - Partitioning (CAP), Limitations of Relational Databases, Comparing NoSQL with RDBMS Managing Different Data Types, Columnar, Key-Value Stores, Triple and Graph Stores, Document, Search Engines, Hybrid NoSQL Databases, Applying Consistency Methods, ACID, BASE, Polyglot persistence Unit II: Evaluating NoSQL The Technical Evaluation, Choosing NoSQL, Search Features, Scaling NoSQL, Keeping Data Safe, Visualizing NoSQL, Extending Data Layer, Business Evaluation, Deploying Skills, Deciding Open Source versus commercial software, Business critical features, Security Unit III: Key-Value & Document Based Databases Introduction to Key-Value Databases, Key Value Store, Essential Features, Consistency, Transactions, Partitioning, Scaling, Replicating Data, Versioning Data, how to construct a Key, Using Keys to Locate Values, Hash Functions, Store data in Values, Use Cases. Introduction to Document Databases, Supporting Unstructured Documents, Document Databases Vs. Key-Value Stores, Basic Operation on Document database, Partition, Sharding, Features, Consistency, Transactions, Availability, Scaling, Use Cases. Unit IV: Column-Oriented & Graph Based Databases Introduction to Column Family Database, Features, Architectures, Differences and Similarities to Key Value and Document Database, Consistency, Transactions, Scaling, Use Cases Introduction to Graph Databases, Advantages, Features, Consistency, Transactions, Availability, Scaling, Graph & Network Modelling, Properties of Graphs and Noes, Types of Graph, Undirected and directed Graph, Flow Network, Bipartite Graph, Multigraph, Weighted Graph Unit V: Search Engine Common Feature of Search Engine, Dissecting a Search Engine, Search versus query, Web crawlers, Indexing, Searching, indexing Data Stores, Altering, Using Reverse queries, Use Cases, Types of Search Engine, Elastic Search Text Books: 1. Adam Fowler, NoSQL for Dummies, John Wiley & Sons, Inc. 2. Dan Sullivan, NoSQL for Mere Mortals, Pearson Education, Inc. References: 1. Pramod J. Sadalage & Martin Fowler, NoSQL Distilled, Pearson Education, Inc. 2. Dan McCreary& Ann Kelly, Making Sense of NoSQL, Manning Shelter Island Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Comprehensive NoSQL Understanding: Graduates of this course will have a deep understanding of NoSQL databases, including their history, features, scalability, and business drivers. They will recognize the limitations of relational databases and effectively compare NoSQL with RDBMS. CO02 Effective Database Management: Students will develop proficiency in managing different data types, including columnar, key-value, triple, graph, document, and hybrid NoSQL databases. They will apply various consistency methods (ACID, BASE) and understand polyglot persistence. CO03 Strategic NoSQL Evaluation: Graduates will be equipped with the skills to evaluate NoSQL databases from both technical and business perspectives. They will make informed decisions regarding the choice of NoSQL solutions, considering factors like search features, scalability, security, and open-source vs. commercial software. CO04 Proficiency in Key-Value and Document Databases: Students will be proficient in working with key-value and document databases, understanding key features, data consistency, transactions, partitioning, and replication. They will apply this knowledge to real-world use cases. CO05 Graph and Columnar Database Proficiency: Graduates will gain a strong understanding of column-oriented and graph databases, including their features, architectures, and use cases. They will also develop expertise in graph modeling, graph properties, and the handling of different types of graphs.  Course Course Name Hours Per Week Code Natural Language Processing L T P Credits CS3EA12 3 0 2 4 Unit-1 Introduction: Human languages, Main approach of NLP, Knowledge in speech and language processing, Ambiguity, Models and algorithms, Formal language and Natural Language, Regular Expression and automata. Unit-2 Text Pre-processing, Tokenization, Feature Extraction from text, Morphology: Inflectional and Derivational, Finite state morphological parsing, Finite state transducer Part of Speech Tagging: Rule based, Stochastic POS, Transformation based tagging. Unit-3 Speech Processing: Speech and phonetics, Vocal organ, Phonological rules and Transducer, Probabilistic models: Spelling error, Bayesian method to spelling, Minimum edit distance, Bayesian method of pronunciation variation. Unit-4 N-Grams: Simple N-Gram, perplexity, Smoothing, Backoff, Entropy, Parsing: Statistical Parsing, Probabilistic parsing, TreeBank. Unit-5 Application: Sentiment analysis, Spelling correction, Word sense disambiguation, Machine translation, Text Classification, Question answering system. Text Book: 1. Daniel Jurafsky and James H. Martin, “Speech and Language Processing”, Pearson Education. 2. James Allen, “Natural Language Understanding”, Pearson Education. Reference book: 1. Christopher D. Manning and Hinrich Schutze, “Foundation of statistical Natural Language Processing”, MIT Press. 2. Mary Dee Harris “Introduction to Natural language Processing” ,Reston.  Course Code Course Name Hours Per Week L T P Hrs. Credits OE00018 Python Essentials 3 0 0 3 3 Course Learning Objectives (CLOs): CLO01 To understand why Python is a useful scripting language for developers. CLO02 To learn how to use lists, tuples, dictionaries, indexing and slicing to access data in Python programs. CLO03 To learn how to read and write files in Python. CLO04 To learn how to design object‐oriented programs with Python classes. CLO05 To learn how to use exception handling in Python applications for error handling Unit-1 Basic Introduction Introduction to Python, History, Features, command interpreter and development environment-IDLE, Application of Python, Python 2/3 differences, Basic program structure-quotation and indentation, Operator, Basic data types and In-built objects. Unit-2 Function and Sequence Functions: definition and use, Arguments, Block structure, scope, Recursion, Argument passing, Conditionals and Boolean expressions, Lambda Function, inbuild functions (str(),globals(),locals(),vars(),eval(),exec(),execfile(),repr(),ascii()) Sequences: Strings, Tuples, Lists Iteration, looping and control flow, String methods and formatting. Unit-3 File Operation & OOPS concepts Reading config files in python, Writing log files in python, Understanding read functions, read(), readline() and readlines(), Understanding write functions, write() and writelines(), Manipulating file pointer using seek. Unit-4 OOPS Concepts Object Oriented concepts- Encapsulation, Polymorphism, Classes, Class instances, Constructors & Destructors__init__, __del__, Multiple inheritance, Operator overloading Properties, Special methods, Emulating built-in types. Unit-5 Mutable data types, Exception and Standard modules Dictionaries, Sets and Mutability, Exceptions, List and Dict Comprehensions, Standard Modules-math, random Packages. Text Book: 1. Dr.R.Nageswara Rao, Core Python Programming, dreamtech press. 2. Paul Barry, Head First Python, O’REILLY. References: 1. Mark Luiz, Learning Python, O’REILLY. 2. Jamie Chan, Learn Python in One Day, LCF Publishing. Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Describe the Numbers, Math functions, Strings, List, Tuples and Dictionaries in Python CO02 Express different decision-making statements and Function CO03 Interpret Object oriented programming in Python CO04 Understand and summarize different File handling operations CO05 Student will be able to distinguish between mutable and immutable data types. CO01 Students are able to work with standard libraries and pre define module. List of Experiments (if applicable) WAP to find product of two numbers using command line arguments? WAP to Given the string 'hello', give an index command that returns 'e’. WAP to Reverse the string 'hello' using slicing. WAP to Given the string ‘hello’, give two methods of producing the letter 'o' using indexing. WAP to Ask the user for a string and print out whether this string is a palindrome or not. (A palindrome is a string that reads the same forwards and backwards.) WAP to create a byte type array, read and display the elements of the array. WAP to accept a numeric digit from keyboard and display in words. WAP to display a group of messages when the condition is true? WAP to accept a number from keyboard and test whether a number is even or odd. WAP to test whether a given number is in between 1 and 10. WAP to display even numbers between m and n WAP to display characters of a string using for loops WAP to display odd numbers from 1 to 10 using range (). WAP to display and sum of a list of numbers using loop. WAP to display the stars in an equilateral triangular form using a loop. WAP to display numbers from 1 to 100 in a proper format WAP to search for an element in the list of elements. WAP to display prime number series. WAP to generate Fibonacci number series. Write a Python program to combine each line from first file with the corresponding line in second file Write a Python program to copy the contents of a file to another file WAP to define Student class and create an object to it. Also, we will call the method and display the student’s details. WAP to create a static method that counts the number of instances created for a class. WAP to create a Bank class where deposits and withdraw can be handled by using instance methods. WAP showing single inheritance in which two sub classes are derived from a single base class. WAP to implement multiple inheritance using two base classes.  WAP to show method overloading to find sum of two or three numbers. WAP to Create a 3×3 numpy array of all True’s WAP to Replace all odd numbers in arr with -1 a. Input ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) WAP to Convert a 1D array to a 2D array with 2 rows a. Input: np. arrange (10) WAP to Get the common items between a and b Input: a = np. array ([1,2,3,2,3,4,3,4,5,6]) b = np. array ([7,2,10,2,7,4,9,4,9,8]) Desired Output: array ([2, 4])  Hours per Week Course Code Course Name L T P Credits OE00051 R-Programming 3 0 0 3 Course Learning Objectives (CLOs): CLO01 To understand importance and advantages of R Programming and IDE for programming. CLO02 To understand and implement program on various data Structures in R. CLO03 To understand basic programming fundamentals like Objects, Classes, Functions in R, debugging tools etc CLO04 Work with the Data Sets of various formats, Training algorithms and plotting. CLO05 To become proficient in writing a fundamental program and perform Data Analytics with R wit use of R strings, date etc. Unit 1 - R basics Introduction: Basic features of R, advantages of using R, Limitations, R resources, Arithmetic and objects, Math, Variables, and Strings, Vectors and Factors, Vector operations. Unit 2 - Data structures in R Data types, Arrays, Tables, Matrices: operations, Lists: operations, Data frames: creation, factors, reading. Unit 3 - R programming fundamentals Conditions and loops, Functions in R, Objects and Classes, Recursion, Debugging Unit 4 - Working with data in R Reading CSV and Excel Files, Reading text files, Writing and saving data objects to file in R, Reading in larger, Datasets, Exporting data. Interface to outside world. Unit 5 – String & Dates in R, Graphics String operations in R, Regular Expressions, Dates in R, Time in R, Graphics: one dimension plot, legends, function plot, box plot. Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Implement basics of R Programming using built-in functions. CO02 Understand fundamentals and Data Structures used in R Programming. CO03 apply fundamentals and Data Structures, functions, debugging tools in writing R-script CO04 Work with the Data Sets of various formats, Training algorithms and plotting. CO05 R-Programming languages for different applications like Machine Learning, Data Science etc. Course Learning Objectives (CLOs): CLO01 Students with understand the fundamental concepts of Block Chain CLO02 They will able to understand the difference between Crypto currency and Blockchain. CLO03 They will able to understand of various Consensus algorithms CLO04 Students will apply their technical knowledge and skills to develop and implement Blockchain CLO05 Students will learn about various Applications and methods used for Blockchain Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Student will understand the basic terminology used in Blockchain and Bitcoin. CO02 Students will be able to explore Blockchain and classification of various cryptocurrency. CO03 Students will learn about various Consensus algorithms. CO04 Students will able to understand basic Blockchain Architecture. CO05 Students will able to use and understand application of Blockchain.  Hours per Week Total Course Code Course Name L T P Credits EN3NG05 Soft Skills-III 2 0 0 2 Course Learning Objectives (CLOs): CLO01 Improving professional communication CLO02 Knowing traits of personality and working on it CLO03 Developing writing skills CLO04 In Improving interpersonal skills including Leadership qualities CLO05 Improving interview and group discussion skills and hence employability UNIT I Introducing Introduction – persons, places, objects, projects. Elevator pitch, self- introduction. UNIT II Professional writing skills Job application, resume, email etiquettes, netiquettes. UNIT III GD and Interviews GD – Dos and Don’ts, importance, conduction, Mock GDs. Interviews – dressing, FAQs, mock interviews. UNIT IV Interpersonal skills I: Basic personality traits, emotional intelligence, adaptability, time management, goal setting, teamwork. UNIT V Interpersonal skills II: Leadership, problem solving, negotiation skills, stress management. Text Books: 1. Rizvi, Ashraf M. Effective Technical Communication Tata Mc Graw-Hill Publishing Company Limited 2. K Alex, Soft Skills: Know yourself and know the world, S Chand & Company Ltd. New Delhi. References: 1. L Bove Courtland, John V Thill and Mukesh Chaturvedi Business Communication Today Dorling Kindersley (India) Pt. Ltd. 2. Ranjan Bhanu, Communication Skills,Dhanpati Rai & Co. (Pvt) Ltd Delhi. Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Interact confidently at formal occasions CO02 Understand their personality and improve it CO03 Work on their writing skills CO04 Improve interpersonal skills CO05 Face interview confidently and will be able to know the qualities of participants taking part in GD  Hours per Week Course Code Course Name L T P Credits Fundamentals of Management, EN3HS04 3 0 0 3 Economics and Accountancy Course Learning Objectives (CLOs): CLO01 To introduce with the Fundamental knowledge of Management. CLO02 To give knowledge about the Marketing and Human Resource Management. CLO03 To provide basic information of Applied Economics. CLO04 To get acquainted with the knowledge of Financial Accounting. CLO05 To give sufficient knowledge of Financial Management. Unit-1 Concepts of Management Definition, characteristics and importance of management; Management: Science or Art, Difference between Management and Administration, Levels of management, Functions of Management, Managerial Roles, Managerial skills and competencies; Decision Making: Definition, process and types; Decision making under certainty, uncertainty and risk; Cross cultural issues in management and challenges Unit-3 Fundamentals of Marketing and Human Resource Management Introduction to Marketing: Definition, importance, function and scope of marketing, Core Concepts of marketing, Marketing concepts and orientations, Marketing environment, Marketing-mix, Holistic marketing concept, Customer Relationship Management (CRM). Introduction to Human Resource Management (HRM): Nature, Scope, Objectives and Functions; Role of HR manager, Process and need for Human Resource Planning, Human resource policies, Changing role of Human Resource in India, Globalization and its impact onHuman Resource. Unit-3 Fundamentals of Economics Introduction to Economics: Definition, nature, scope and significance; Difference between microand macro economics; Time value of money, Law of diminishing marginal utility; Theory of Demand and Supply, Price elasticity of demand; Meaning and types of costs, Law of variable proportions; Types of market structure; National income and related aggregates; Meaning and types of Inflation; Meaning and phases of business cycle. Unit-4 Basic Accounting Principles Accounting Principles and Procedure, Double entry system, Journal, Ledger, Trail Balance, Cash Book; Preparation of Trading, Profit and Loss Account; Balance sheet; Cost Accounting: Introduction, Classification of costs, Methods and Techniques of costing, Cost sheet and preparation of cost sheet; Breakeven Analysis: Meaning and its application. Unit -5 Fundamentals of Financial Management Introduction of Business Finance: Meaning, Definition of Financial Management, Goals of Financial Management (Profit Maximization and Wealth Maximization), Modern approaches toFinancial Management — (Investment Decision, Financing Decision and Dividend Policy Decisions). Course Outcomes (COs): After completion of this course the students shall be able to: CO01 Students will be able to understand Basics of Management Theory. CO02 Student will be gaining knowledge of Marketing & Human Resource Management. CO03 Students will be able to understand basic information for Economics. CO04 Students will be able to get acquainted with the Financial Accounting System. CO05 Students will be able gain sufficient knowledge of Financial Management