New Scheme Based On AICTE Flexible Curricula Computer Science & Information Technology, VIII-Semester
The objective of this course is to familiarize students with the roles of a data scient ist and enable them to analyze data to derive meaningful in format ion from it.
Demonstrate proficiency wit h statistical analysis of data.
Build and assess data-based models.
Execute statistical analyses wit h professional statistical software.
Demonstrate skill in data management.
Apply data science concepts and methods to solve problems in real-world contexts and will co mmunicate these solutions effectively
Cases of Pig, Pig program structure, Pig Components, Pig Execut ion, Pig data models, Overview of Mahout, Mahout working.
Introduction to R, Basic Data Analyt ics Methods Using R, Communicating and Operationalizing an Analyt ics Project, Creating the Final Deliverables, Data Visualization Basics.
EMC Education Services, “Data Science and Big Data Analyt ics”, Wiley, 2015.
Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman, “Big Data for Dummies”,Wiley & Sons,2013.
VigneshPrajapati, “Big Data Analyt ics with R and Hadoop” ,Packt Publishing, 2013.
David Dietrich, Barry Heller, and Beibei Yang“Data Science and Big Data Analyt ics:Discovering, Analyzing, Visualizing and Presenting Data”, John Wiley & Sons, Inc.
Introduction to R tool for data analytics science
Basic Statistics and Visualization in R
K-means Clustering
Association Rules
Linear Regression
Logistic Regression
Naive Bayesian Classifier
Decision Trees
Simulate Principal component analysis
Simulate Singular Value Decomposition
New Scheme Based On AICTE Flexible Curricula Computer Science & Information Technology, VIII-Semester
Student should understand the value of Historical data and data mining in solving real world problems.
Student should become affluent with the basic Supervised and unsupervised learning algorithms commonly used in data mining.
Student develops the skill in using data mining for solving real-world problems.
Understand the functionality of the various data mining and data warehousing component
Appreciate the strengths and limitations of various data mining and data warehousing models
Explain the analyzing techniques of various data
Describe different methodologies used in data mining and data ware housing.
Compare different approaches of data ware housing and data mining with various technologies.
Data Warehousing: Introduction, Delivery Process, Data warehouse Architecture, Data Preprocessing: Data cleaning, Data Integration and transfor mation, Data reduction. Data warehouse Design: Datawarehouse schema, Partitioning strategy Data warehouse Implementation, Data Marts, Meta Data, Example of a Multidimensional Data model. Introduction to Pattern Warehousing.
OLAP Systems: Basic concepts, OLAP queries, Types of OLAP servers, OLAP operations etc. Data Warehouse Hardware and Operational Design: Security, Backup And Recovery,
Introduction to Data& Data Mining: D a t a Types, Quality of data, Data Preprocessing, Similarity measures, Summar y statistics, Data distributions, Basic data mining tasks, Data Mining V/s knowledge discovery in databases. Issues in Data mining. Introduction to Fuzzy sets and fuzzy logic.
Supervised Learning: Classification: Statistical-based algorithms, Distance-based algorithms, Decision tree-based algorithms, Neural network-based algorithms, Rule-based algorit hms, Probabilistic Classifiers
Clustering & Association Rule mining : Hierarchical algorithms, Partitional algorithms, Clustering large databases – BIRCH, DBSCAN, CURE algorithms. Association rules : Parallel and distributed algorithms such as Apriori and FP growth algorithms.
Pang – ningTan , Steinbach & Kumar, “Introduction to Data Mining”, Pearson Edu, 2019.
Jaiwei Han, Micheline Kamber, “Data Mining : Concepts and Techniques”, Morgan Kaufmann Publishers.
Margaret H. Dunha m, “Data Mining : Introductory and Advanced topics”, Pearson Edu.,
Anahory& Murray, “Data Warehousing in the Real World”, Pearson Edu.
Create an Employee Table with the help of Data Mining Tool WEKA.
Create a Weather Table with the help of Data Mining Tool WEKA.
Apply Pre-Processing techniques to the training data set of Weather Table
Apply Pre-Processing techniques to the training data set of Employee Table
Normalize Weather Table data using Knowledge Flow.
Normalize Employee Table data using Knowledge Flow.
Finding Association Rules for Buying data.
Finding Association Rules for Banking data.
Finding Association Rules for Employee data.
To Construct Decision Tree for Weather data and classify it.
New Scheme Based On AICTE Flexible Curricula Computer Science & Information Technology, VIII-Semester
The course has been designed to be an entry level in Bioinformatics. It is introductory in nature and will provide an overview of the concepts and practices in Bioinformatics. The course structure has been designed such that students will acquire skills required to become Assistant Programmer/Technical Assistant in Bioinformatics. It would also help students to acquire a good foundation to take up further studies.
To get introduced to the basic concepts of Bioinformatics and its significance in Biological data analysis.
Describe the history, scope and importance of Bioinformatics and role of internet in Bioinformatics.
Explain about the methods to characterize and manage the different types of Biological data.
Classify different types of Biological Databases.
Introduction to the basics of sequence alignment and analysis.
Gopal & Jones, BIOINFORMATICS with fundamentals of Genomics & Proteomics ,TMH Pub
Rastogi , Bioinformatics –Concepts , skills & Applications , CBS Pub
Claverie , Bioinformatics , Wiley pub
Stekel , Micrarray BioInformatics , Cambridge
To find information in online databases.
To retrieve the sequence of the Human keratin protein from UniProt database and to interpret the results.
To retrieve the sequence of the Human keratin protein from Genbank database and to interpret the results.
To find the similarity between sequences using BLAST.
To find the similarity between sequences using FASTA
To align more than two sequences and find out the similarity between those sequences using ClustalW.
This course aims at introducing the area of Information Retrieval and at examining the theoretical and practical issues involved in designing, implementing and evaluating Information Retrieval systems.
To identify basic theories and analysis tools as they apply to information retrieval.
To develop understanding of problems and potentials of current IR systems.
To learn and appreciate different retrieval algorithms and systems.
To apply various indexing, matching, organizing, and evaluating methods to IR problem.
To become aware of current experimental and theoretical IR research.
Introduction: Information ver s us data retrieval, the retrieval process, taxonomy of Information Retrieval Models.
Classic Information Retrieval Techniques: Boolean Model, Vector model, Probabilistic Model, comparison of classical models. Introduction to alternative algebraic models such as Latent semantic Indexing etc.
Keyword based Queries, User Relevance Feedback: Query Expansion and Rewriting, Document preprocessing and clustering, Indexing and Searching: Inverted Index construction, Introduction to Pattern matching.
Web Search: Crawling and Indexes, Search Engine architectures, Link Analysis and ranking algorithms such as HITS and Page Rank, Meta searches, Performance Evaluation of search engines using various measures, Introduction to search engine optimization.
Introduction to online IR Systems, Digital Library searches and web Personalization.
Ricardo Baeza-Yates and Berthier Ribeiro-Neto, “Modern Information Retrieval” Pearson Education
C. Manning, P. Raghvan and H. Schutze, “Introduction to Information Retrieval”, Cambridge University Press.
Amy N. Langville and Carl D. Meyer, “Google’s PageRank and Beyond: The Science of Search Engine Rankings”, Princeton Universit Press
Pierre Baldi, Paolo Frasconi and PadhraicSmythe, “Modelling the internet and the web: Probabilistic methods and Algorithms”, John Wiley
Students must experiment on various information retrieval systems like page rank etc
To understand the concept of Blockchain and its platforms- Bitcoin, Ethereum, Hyperledger and Multichain. The course provides an overview of the structure and mechanism of Blockchain.
Understand blockchain architecture and requisite crypto foundation.
Understand various consensus protocol and their usage for their specific application.
Understand and Resolve security concern in blockchain.
Explore blockchain advances and upcoming platforms.
Learn to write smart contracts.
Understand use cases.
Introduction and crypto foundation: Elliptic curve cryptography, ECDSA, Cryptographic hash function, SHA-256, Merkle trees, Cryptocurrencies.
Bitcoin, Bitcoin addresses, Bitcoin blockchain, block header, mining proof of work (PoW) algorithm, difficulty adjustment algorithm, mining pools, transactions, double spending attack, The 51% attacker, block format, transaction format, Smart contacts (escrow, micropayments, decentralized lotteries), payment channels.
Ethereum: Overview of differences between Ethereum and bitcoin, block format, mining algorithm, proof-of-stake (PoS) algorithm, account management, contracts and transactions, Solidity language, decentralized application using Ethereum.
Smart Contracts Different Blockchains and Consensus mechanisms.
Blockchain and Security R3, CORDA and Hyperledger System architecture, ledger format, chain code, transaction flow and ordering, private channels, membership service providers, case studies.
Mastering Bitcoin:Unblocking Digital Cryptocurrencies, by Andreas Antonopoulos.
Mastering Ethereum, Antonopoulos, Andreas M. and Wood, O’Reilly Media,Inc.,2018
An Introduction to Bitcoin, V. Saravanan, Lecture Notes.
Bitcoin and Cryptocurrencies Technologies: A Comprehensive Introduction, Arvind Narayanan, Princeton University Press(July 19,2016)ISBN-10:0691171696.
To Create a first block in blockchain
To encrypt a block using Sha 256 Encryption Algorithm
To Mine a Block in Blockchain
To authenticate a mined block using consensus algorithm’
To implement proof of work
To secure a block using encryption
To create a simple cryptocurrency
To write a smart contract in solidity
Developing an overall understanding of digital marketing / online marketing platforms, mainly web analytics, social media tools, marketing through search engines, search engine optimization, mobile marketing, email marketing, Pay per click, digital display marketing, content marketing and Strategizing marketing.
Understand the concept of digital marketing and its real-world iterations
Articulate innovative insights of digital marketing enabling a competitive edge
Understand how to create and run digital media based campaigns
Identify and utilize various tools such as social media etc.
Understand how to do SEO Audit
Digital Marketing: Introduction, Moving from Traditional to Digital Marketing, Integrating Traditional and Digital Marketing, Reasons for Growth. Need for a comprehensive Digital Marketing Strategy. Concepts: Search Engine Optimization (SEO); Concept of Pay Per Click
Social Media Marketing: Introduction, Process - Goals, Channels, Implementation, Analyze Tools: Google and the Search Engine, Facebook, Twitter, YouTube and LinkedIn, Issues: Credibility, Fake News, Paid Influencers, Social Media and Hate/ Phobic campaigns, Analytics and linkage with Social Media, The Social Community.
Email Marketing: Introduction, email marketing process, design and content, delivery, discovery. Mobile Marketing: Introduction and concept, Process of mobile marketing: goals, setup, monitor, analyze; Enhancing Digital Experiences with Mobile Apps. Pros and Cons; Targeted advertising. Issues: Data Collection, Privacy, Data Mining, Money and Apps, Security, Spam. Growth Areas.
Managing Digital Marketing: Content Production; Video based marketing; Credibility and Digital Marketing; IoT; User Experience; Future of Digital Marketing.
SEO Analytics, Monitoring & Reporting : Google Search Console (GSC),Key Sections & Features of GSC; How to monitor SEO progress with Key Features of GSC: Overview, Performance, URL
Inspection, Coverage, Sitemaps, Speed, Mobile Usability, Backlinks, Referring Domains, Security & Manual Actions, How to do SEO Reporting
Dodson, Ian: The Art of Digital Marketing - The Definitive Guide to Creating Strategic, Targeted, and Measurable Online Campaigns. Wiley
Ryan, Damien: Understanding Digital Marketing - Marketing Strategies for Engaging the Digital Generation. Kogan Page Limited.
Gupta, Sunil: Driving Digital Strategy. Harvard Business Review Press
Tuten, Tracy L. and Solomon, Michael R.: Social Media Marketing. Sage
Bhatia, Puneet S.: Fundamentals of Digital Marketing. Pearson
Kotler, Philip: Marketing 4.0: Moving from Traditional to Digital. Wiley
The objective of this course is to provide the students an introduction to quantum computation. Much of the background material related to the algebra of complex vector spaces and quantum mechanics is covered within the course.
Analyze the behavior of basic quantum algorithms Implement simple quantum algorithms and 2. Information channels in the quantum circuit model
3. Simulate a simple quantum error-correcting code
4. Prove basic facts about quantum information channels
-Jozsa Algorithm, Quantum Fourier Transform, Phase Estimation, Shor’s Algorithm ,Quantum Searching and Grover’s Algorithm
Quantum Computing Explained: David McMahon, Wiley Interscience (IEEE Computer Science).
Quantum Computing without Magic Devices : Zdzislaw Meglicki; PHI .
Quantum Computation and Quantum Information: M.A. Nielsen & Isaac L. Chuang, Cambridge University Press .
Quantum Computing and communications: An Engineering Approach: Sandor Imre and Ferenc Balazs, Wiley.
The objective of this course is to emphasize the importance of cyber laws and digital forensics, and to prepare students to conduct a digital investigation in an organized and systematic way.
Become aware of various cyber crimes and cyber laws
Underline the need of digital forensic and role of digital evidences
Understand different types of digital evidences that can be presented to support investigations
List the methods to generate legal evidence and supporting investigation reports
Use various digital forensic tools
Introduction to cybercrime, definition, cyber crime and information security, classification of cybercrimes, cybercrime: the legal perspectives, an Indian perspective, cybercrime and the Indian ITA 2000, a global perspective on cybercrime, Cyber offences: How criminals plan them, Tools and methods used in cyber crime, Need of cyber law, The Indian IT act, challenges to Indian law and cybercrime scenario in India, digital signature and Indian IT act,
Law and framework for information security, law for intellectual property rights (IPR), patent law, copy right law, Indian copyright act, privacy issue and law in Hong Kong, Japan, and Australia, data protection act in Europe, health insurance portability and accountability act of 1996(HIPAA),Gramm-leach-Bliley act of 1999(GLAB),Sarbanes-Oxley(SOX), legal issue in data mining.
Digital forensics Science, The need for computer forensics, Understanding computer forensics, computer forensics versus other related disciplines, A brief History of computer Forensics, Cyber forensics and digital evidence, Digital forensics lifecycle, chain of custody concept, Network forensics, Approaching a computer forensics investigation, setting up a computer forensics laboratory, Forensics and social networking sites, computer forensics from compliance perspective, challenges in computer forensics, forensics auditing, anti forensics.
Current Computer Forensics Tools, Evaluating Computer Forensics Tool Needs, Types of Computer Forensics Tools, Tasks Performed by Computer Forensics Tools, Tool Comparisons, Other Considerations for Tools, Computer Forensics Software Tools, Command-Line Forensics
Tools, UNIX/Linux Forensics Tools, Other GUI Forensics Tools, Computer Forensics Hardware Tools, Forensic Workstations
Forensics of hand held devices, Investigating Network Intrusions and Cyber Crime, Network Forensics and investigating logs, investigating network Traffic, Investigating Web attacks, Router Forensics. Cyber forensics tools and case studies
The Indian Cyber law with Cyber glossary, Suresh T. Vishwanathan, New Delhi, Bhart Law House, 2000.
Law of Cyber Crimes and Information Technology Law, S.V. JogaRao, 2007.
Cory Altheide, Harlan Carvey, Digital Forensics with Open Source Tools, Syngress imprint of Elsevier.
Bill Nelson, Amelia Phillips, Christopher Steuart, “Guide to Computer Forensics and Investigations”, Fourth Edition, Course Technology.
Angus M. Marshall, “Digital forensics: Digital evidence in criminal investigation”, John –Wiley and Sons, 2008.
Nina Godbole and Sunit Belapure–Cyber Security, Wiley India Publication.
To understand the basic concepts associated with the design and Functioning and applications of Robots. To study about the drives and sensors used in Robots. To learn about analyzing robot kinematics and robot programming
Learn about knowledge for the design of robotics. 2 Learn about force and torque sensing
Understand different sensors and vision of machine
Understand robot kinematics and robot programming
Apply basics on an application of Robots
M.P.Groover, “Industrial Robotics – Technology, Programming and Applications”, McGraw- Hill, 2001.
Saha S. , Introduction to Robotics , TMH.
Ghoshal Ashitava, Robotics, Fundamental Concepts and Analysis, Oxford.
Yu Kozyhev, Industrial Robots Handbook, MIR Publications.
New Scheme Based On AICTE Flexible Curricula Computer Science & Information Technology, VIII-Semester
The objective of this course is to familiarize students with the roles of a data scient ist and enable them to analyze data to derive meaningful in format ion from it.
Demonstrate proficiency wit h statistical analysis of data.
Build and assess data-based models.
Execute statistical analyses wit h professional statistical software.
Demonstrate skill in data management.
Apply data science concepts and methods to solve problems in real-world contexts and will co mmunicate these solutions effectively
Cases of Pig, Pig program structure, Pig Components, Pig Execut ion, Pig data models, Overview of Mahout, Mahout working.
Introduction to R, Basic Data Analyt ics Methods Using R, Communicating and Operationalizing an Analyt ics Project, Creating the Final Deliverables, Data Visualization Basics.
EMC Education Services, “Data Science and Big Data Analyt ics”, Wiley, 2015.
Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman, “Big Data for Dummies”,Wiley & Sons,2013.
VigneshPrajapati, “Big Data Analyt ics with R and Hadoop” ,Packt Publishing, 2013.
David Dietrich, Barry Heller, and Beibei Yang“Data Science and Big Data Analyt ics:Discovering, Analyzing, Visualizing and Presenting Data”, John Wiley & Sons, Inc.
Introduction to R tool for data analytics science
Basic Statistics and Visualization in R
K-means Clustering
Association Rules
Linear Regression
Logistic Regression
Naive Bayesian Classifier
Decision Trees
Simulate Principal component analysis
Simulate Singular Value Decomposition
New Scheme Based On AICTE Flexible Curricula Computer Science & Information Technology, VIII-Semester
Student should understand the value of Historical data and data mining in solving real world problems.
Student should become affluent with the basic Supervised and unsupervised learning algorithms commonly used in data mining.
Student develops the skill in using data mining for solving real-world problems.
Understand the functionality of the various data mining and data warehousing component
Appreciate the strengths and limitations of various data mining and data warehousing models
Explain the analyzing techniques of various data
Describe different methodologies used in data mining and data ware housing.
Compare different approaches of data ware housing and data mining with various technologies.
Data Warehousing: Introduction, Delivery Process, Data warehouse Architecture, Data Preprocessing: Data cleaning, Data Integration and transfor mation, Data reduction. Data warehouse Design: Datawarehouse schema, Partitioning strategy Data warehouse Implementation, Data Marts, Meta Data, Example of a Multidimensional Data model. Introduction to Pattern Warehousing.
OLAP Systems: Basic concepts, OLAP queries, Types of OLAP servers, OLAP operations etc. Data Warehouse Hardware and Operational Design: Security, Backup And Recovery,
Introduction to Data& Data Mining: D a t a Types, Quality of data, Data Preprocessing, Similarity measures, Summar y statistics, Data distributions, Basic data mining tasks, Data Mining V/s knowledge discovery in databases. Issues in Data mining. Introduction to Fuzzy sets and fuzzy logic.
Supervised Learning: Classification: Statistical-based algorithms, Distance-based algorithms, Decision tree-based algorithms, Neural network-based algorithms, Rule-based algorit hms, Probabilistic Classifiers
Clustering & Association Rule mining : Hierarchical algorithms, Partitional algorithms, Clustering large databases – BIRCH, DBSCAN, CURE algorithms. Association rules : Parallel and distributed algorithms such as Apriori and FP growth algorithms.
Pang – ningTan , Steinbach & Kumar, “Introduction to Data Mining”, Pearson Edu, 2019.
Jaiwei Han, Micheline Kamber, “Data Mining : Concepts and Techniques”, Morgan Kaufmann Publishers.
Margaret H. Dunha m, “Data Mining : Introductory and Advanced topics”, Pearson Edu.,
Anahory& Murray, “Data Warehousing in the Real World”, Pearson Edu.
Create an Employee Table with the help of Data Mining Tool WEKA.
Create a Weather Table with the help of Data Mining Tool WEKA.
Apply Pre-Processing techniques to the training data set of Weather Table
Apply Pre-Processing techniques to the training data set of Employee Table
Normalize Weather Table data using Knowledge Flow.
Normalize Employee Table data using Knowledge Flow.
Finding Association Rules for Buying data.
Finding Association Rules for Banking data.
Finding Association Rules for Employee data.
To Construct Decision Tree for Weather data and classify it.
New Scheme Based On AICTE Flexible Curricula Computer Science & Information Technology, VIII-Semester
The course has been designed to be an entry level in Bioinformatics. It is introductory in nature and will provide an overview of the concepts and practices in Bioinformatics. The course structure has been designed such that students will acquire skills required to become Assistant Programmer/Technical Assistant in Bioinformatics. It would also help students to acquire a good foundation to take up further studies.
To get introduced to the basic concepts of Bioinformatics and its significance in Biological data analysis.
Describe the history, scope and importance of Bioinformatics and role of internet in Bioinformatics.
Explain about the methods to characterize and manage the different types of Biological data.
Classify different types of Biological Databases.
Introduction to the basics of sequence alignment and analysis.
Gopal & Jones, BIOINFORMATICS with fundamentals of Genomics & Proteomics ,TMH Pub
Rastogi , Bioinformatics –Concepts , skills & Applications , CBS Pub
Claverie , Bioinformatics , Wiley pub
Stekel , Micrarray BioInformatics , Cambridge
To find information in online databases.
To retrieve the sequence of the Human keratin protein from UniProt database and to interpret the results.
To retrieve the sequence of the Human keratin protein from Genbank database and to interpret the results.
To find the similarity between sequences using BLAST.
To find the similarity between sequences using FASTA
To align more than two sequences and find out the similarity between those sequences using ClustalW.
This course aims at introducing the area of Information Retrieval and at examining the theoretical and practical issues involved in designing, implementing and evaluating Information Retrieval systems.
To identify basic theories and analysis tools as they apply to information retrieval.
To develop understanding of problems and potentials of current IR systems.
To learn and appreciate different retrieval algorithms and systems.
To apply various indexing, matching, organizing, and evaluating methods to IR problem.
To become aware of current experimental and theoretical IR research.
Introduction: Information ver s us data retrieval, the retrieval process, taxonomy of Information Retrieval Models.
Classic Information Retrieval Techniques: Boolean Model, Vector model, Probabilistic Model, comparison of classical models. Introduction to alternative algebraic models such as Latent semantic Indexing etc.
Keyword based Queries, User Relevance Feedback: Query Expansion and Rewriting, Document preprocessing and clustering, Indexing and Searching: Inverted Index construction, Introduction to Pattern matching.
Web Search: Crawling and Indexes, Search Engine architectures, Link Analysis and ranking algorithms such as HITS and Page Rank, Meta searches, Performance Evaluation of search engines using various measures, Introduction to search engine optimization.
Introduction to online IR Systems, Digital Library searches and web Personalization.
Ricardo Baeza-Yates and Berthier Ribeiro-Neto, “Modern Information Retrieval” Pearson Education
C. Manning, P. Raghvan and H. Schutze, “Introduction to Information Retrieval”, Cambridge University Press.
Amy N. Langville and Carl D. Meyer, “Google’s PageRank and Beyond: The Science of Search Engine Rankings”, Princeton Universit Press
Pierre Baldi, Paolo Frasconi and PadhraicSmythe, “Modelling the internet and the web: Probabilistic methods and Algorithms”, John Wiley
Students must experiment on various information retrieval systems like page rank etc
To understand the concept of Blockchain and its platforms- Bitcoin, Ethereum, Hyperledger and Multichain. The course provides an overview of the structure and mechanism of Blockchain.
Understand blockchain architecture and requisite crypto foundation.
Understand various consensus protocol and their usage for their specific application.
Understand and Resolve security concern in blockchain.
Explore blockchain advances and upcoming platforms.
Learn to write smart contracts.
Understand use cases.
Introduction and crypto foundation: Elliptic curve cryptography, ECDSA, Cryptographic hash function, SHA-256, Merkle trees, Cryptocurrencies.
Bitcoin, Bitcoin addresses, Bitcoin blockchain, block header, mining proof of work (PoW) algorithm, difficulty adjustment algorithm, mining pools, transactions, double spending attack, The 51% attacker, block format, transaction format, Smart contacts (escrow, micropayments, decentralized lotteries), payment channels.
Ethereum: Overview of differences between Ethereum and bitcoin, block format, mining algorithm, proof-of-stake (PoS) algorithm, account management, contracts and transactions, Solidity language, decentralized application using Ethereum.
Smart Contracts Different Blockchains and Consensus mechanisms.
Blockchain and Security R3, CORDA and Hyperledger System architecture, ledger format, chain code, transaction flow and ordering, private channels, membership service providers, case studies.
Mastering Bitcoin:Unblocking Digital Cryptocurrencies, by Andreas Antonopoulos.
Mastering Ethereum, Antonopoulos, Andreas M. and Wood, O’Reilly Media,Inc.,2018
An Introduction to Bitcoin, V. Saravanan, Lecture Notes.
Bitcoin and Cryptocurrencies Technologies: A Comprehensive Introduction, Arvind Narayanan, Princeton University Press(July 19,2016)ISBN-10:0691171696.
To Create a first block in blockchain
To encrypt a block using Sha 256 Encryption Algorithm
To Mine a Block in Blockchain
To authenticate a mined block using consensus algorithm’
To implement proof of work
To secure a block using encryption
To create a simple cryptocurrency
To write a smart contract in solidity
Developing an overall understanding of digital marketing / online marketing platforms, mainly web analytics, social media tools, marketing through search engines, search engine optimization, mobile marketing, email marketing, Pay per click, digital display marketing, content marketing and Strategizing marketing.
Understand the concept of digital marketing and its real-world iterations
Articulate innovative insights of digital marketing enabling a competitive edge
Understand how to create and run digital media based campaigns
Identify and utilize various tools such as social media etc.
Understand how to do SEO Audit
Digital Marketing: Introduction, Moving from Traditional to Digital Marketing, Integrating Traditional and Digital Marketing, Reasons for Growth. Need for a comprehensive Digital Marketing Strategy. Concepts: Search Engine Optimization (SEO); Concept of Pay Per Click
Social Media Marketing: Introduction, Process - Goals, Channels, Implementation, Analyze Tools: Google and the Search Engine, Facebook, Twitter, YouTube and LinkedIn, Issues: Credibility, Fake News, Paid Influencers, Social Media and Hate/ Phobic campaigns, Analytics and linkage with Social Media, The Social Community.
Email Marketing: Introduction, email marketing process, design and content, delivery, discovery. Mobile Marketing: Introduction and concept, Process of mobile marketing: goals, setup, monitor, analyze; Enhancing Digital Experiences with Mobile Apps. Pros and Cons; Targeted advertising. Issues: Data Collection, Privacy, Data Mining, Money and Apps, Security, Spam. Growth Areas.
Managing Digital Marketing: Content Production; Video based marketing; Credibility and Digital Marketing; IoT; User Experience; Future of Digital Marketing.
SEO Analytics, Monitoring & Reporting : Google Search Console (GSC),Key Sections & Features of GSC; How to monitor SEO progress with Key Features of GSC: Overview, Performance, URL
Inspection, Coverage, Sitemaps, Speed, Mobile Usability, Backlinks, Referring Domains, Security & Manual Actions, How to do SEO Reporting
Dodson, Ian: The Art of Digital Marketing - The Definitive Guide to Creating Strategic, Targeted, and Measurable Online Campaigns. Wiley
Ryan, Damien: Understanding Digital Marketing - Marketing Strategies for Engaging the Digital Generation. Kogan Page Limited.
Gupta, Sunil: Driving Digital Strategy. Harvard Business Review Press
Tuten, Tracy L. and Solomon, Michael R.: Social Media Marketing. Sage
Bhatia, Puneet S.: Fundamentals of Digital Marketing. Pearson
Kotler, Philip: Marketing 4.0: Moving from Traditional to Digital. Wiley
The objective of this course is to provide the students an introduction to quantum computation. Much of the background material related to the algebra of complex vector spaces and quantum mechanics is covered within the course.
Analyze the behavior of basic quantum algorithms Implement simple quantum algorithms and 2. Information channels in the quantum circuit model
3. Simulate a simple quantum error-correcting code
4. Prove basic facts about quantum information channels
-Jozsa Algorithm, Quantum Fourier Transform, Phase Estimation, Shor’s Algorithm ,Quantum Searching and Grover’s Algorithm
Quantum Computing Explained: David McMahon, Wiley Interscience (IEEE Computer Science).
Quantum Computing without Magic Devices : Zdzislaw Meglicki; PHI .
Quantum Computation and Quantum Information: M.A. Nielsen & Isaac L. Chuang, Cambridge University Press .
Quantum Computing and communications: An Engineering Approach: Sandor Imre and Ferenc Balazs, Wiley.
The objective of this course is to emphasize the importance of cyber laws and digital forensics, and to prepare students to conduct a digital investigation in an organized and systematic way.
Become aware of various cyber crimes and cyber laws
Underline the need of digital forensic and role of digital evidences
Understand different types of digital evidences that can be presented to support investigations
List the methods to generate legal evidence and supporting investigation reports
Use various digital forensic tools
Introduction to cybercrime, definition, cyber crime and information security, classification of cybercrimes, cybercrime: the legal perspectives, an Indian perspective, cybercrime and the Indian ITA 2000, a global perspective on cybercrime, Cyber offences: How criminals plan them, Tools and methods used in cyber crime, Need of cyber law, The Indian IT act, challenges to Indian law and cybercrime scenario in India, digital signature and Indian IT act,
Law and framework for information security, law for intellectual property rights (IPR), patent law, copy right law, Indian copyright act, privacy issue and law in Hong Kong, Japan, and Australia, data protection act in Europe, health insurance portability and accountability act of 1996(HIPAA),Gramm-leach-Bliley act of 1999(GLAB),Sarbanes-Oxley(SOX), legal issue in data mining.
Digital forensics Science, The need for computer forensics, Understanding computer forensics, computer forensics versus other related disciplines, A brief History of computer Forensics, Cyber forensics and digital evidence, Digital forensics lifecycle, chain of custody concept, Network forensics, Approaching a computer forensics investigation, setting up a computer forensics laboratory, Forensics and social networking sites, computer forensics from compliance perspective, challenges in computer forensics, forensics auditing, anti forensics.
Current Computer Forensics Tools, Evaluating Computer Forensics Tool Needs, Types of Computer Forensics Tools, Tasks Performed by Computer Forensics Tools, Tool Comparisons, Other Considerations for Tools, Computer Forensics Software Tools, Command-Line Forensics
Tools, UNIX/Linux Forensics Tools, Other GUI Forensics Tools, Computer Forensics Hardware Tools, Forensic Workstations
Forensics of hand held devices, Investigating Network Intrusions and Cyber Crime, Network Forensics and investigating logs, investigating network Traffic, Investigating Web attacks, Router Forensics. Cyber forensics tools and case studies
The Indian Cyber law with Cyber glossary, Suresh T. Vishwanathan, New Delhi, Bhart Law House, 2000.
Law of Cyber Crimes and Information Technology Law, S.V. JogaRao, 2007.
Cory Altheide, Harlan Carvey, Digital Forensics with Open Source Tools, Syngress imprint of Elsevier.
Bill Nelson, Amelia Phillips, Christopher Steuart, “Guide to Computer Forensics and Investigations”, Fourth Edition, Course Technology.
Angus M. Marshall, “Digital forensics: Digital evidence in criminal investigation”, John –Wiley and Sons, 2008.
Nina Godbole and Sunit Belapure–Cyber Security, Wiley India Publication.
To understand the basic concepts associated with the design and Functioning and applications of Robots. To study about the drives and sensors used in Robots. To learn about analyzing robot kinematics and robot programming
Learn about knowledge for the design of robotics. 2 Learn about force and torque sensing
Understand different sensors and vision of machine
Understand robot kinematics and robot programming
Apply basics on an application of Robots
M.P.Groover, “Industrial Robotics – Technology, Programming and Applications”, McGraw- Hill, 2001.
Saha S. , Introduction to Robotics , TMH.
Ghoshal Ashitava, Robotics, Fundamental Concepts and Analysis, Oxford.
Yu Kozyhev, Industrial Robots Handbook, MIR Publications.