Syllabus of B.tech. VIII SEM CSIT (RGPV)
Syllabus of B. Tech. VIII Sem CSIT (RGPV)
Syllabus of CSIT-801 Data Science
Source: (rgpv.ac.in)
UNIT-1 :
Data Science and Big Data Overview : Types of data, Sources of data, Data collection, Data storage and management,
Big Data Overview, Characterizat ion o f Big data, Drivers of Big Data,
Challenges, Big Data Use Cases, Defining Big Data Analyt ics and examples of its use cases,
Data Analytics Lifecycle : Discovery, Data Preparation, Model Planning, Model Building, Communicate Results, Operationalize.
UNIT-2 :
Advanced Analytical Theory and Methods : Clustering, K-means, Additional Clustering Algorithms, Association Rules, Apriori Algorithm,
Applications of Associat ion Rules, Regression, Linear Regression, Logistic Regression,
Classification, Decision Trees, Naive Bayes, Additional Classification Methods,
Text Analysis, Text Analysis Steps, Determining Sentiments.
UNIT-3 :
Advanced Analytics-Technology and Tools : Analyt ics for Unstructured Data Use Cases, MapReduce, Apache Hadoop,
Traditional database vs. Hadoop, Hadoop Core Components, HDFS,
Design of HDFS, HDFS Co mponents, HDFS Architecture,
Hadoop 2.0 Architecture, Hadoop-2.0 Resource Management, YARN.
UNIT-4 :
The Hadoop Ecosystem : Introduction to Hive, Hbase,
Hive Use Cases : Face book, Healthcare;
Hive Architecture, Hive Co mponents.
Integrating Data Sources, Dealing with Real-Time Data Streams, Complex Event Processing,
Overview of Pig, Difference between Hive and Pig,
Use Cases of Pig, Pig program structure, Pig Components, Pig Execution, Pig data models,
Overview of Mahout, Mahout working.
UNIT-5 :
Introduction to R : Basic Data Analytics Methods Using R,
Communicating and Operationalizing an Analytics Project,
Creating the Final Deliverables, Data Visualization Basics.
LIST OF EXPERIMENTS :
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
== END OF UNITS==
Syllabus of CSIT-802(A) Data Warehousing & Mining (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
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.
UNIT-2 :
OLAP Systems : Basic concepts, OLAP queries, Types of OLAP servers,
OLAP operations etc.
Data Warehouse Hardware and Operational Design: Security, Backup And Recovery
UNIT-3 :
Introduction to Data & Data Mining : Data 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.
UNIT-4 :
Supervised Learning : Classification: Statistical-based algorithms,
Distance-based algorithms, Decision tree-based algorithms,
Neural network-based algorithms, Rule-based algorit hms,
Probabilistic Classifiers
UNIT-5 :
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.
LIST OF EXPERIMENTS :
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.
== END OF UNITS==
Syllabus of CSIT-802(B) Bio Informatics (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction: Introduction to bioinformatics, objectives of bioinformatics,
Basic chemistry of nucleic acids, structure of DNA & RNA, Genes,
structure of bacterial chromosome, cloning methodology,
Data maintenance and Integrity Tasks
UNIT-2 :
Bioinformatics Databases & Image Processing : Types of databases, Nucleotide sequence databases,
Protein sequence databases, Protein structure databases, Normalization,
Data cleaning and transformation, Protein folding, protein function, protein purification and characterization,
Introduction to Java clients, CORBA,
Using MYSQL, Feature Extraction.
UNIT-3 :
Sequence Alignment and database searching : Introduction to sequence analysis, Models for sequence analysis,
Methods of optimal alignment, Tools for sequence alignment,
Dynamics Programming, Heuristic Methods,
Multiple sequences Alignment
UNIT-4 :
Gene Finding and Expression : Cracking the Genome, Biological decoder ring, finding genes through mathematics & learning,
Genes prediction tools, Gene Mapping,
Application of Mapping, Modes of Gene Expression data,
mining the Gene Expression Data
UNIT-5 :
Proteomics & Problem solving in Bioinformatics : Proteome analysis, tools for proteome analysis, Genetic networks, Network properties and analysis,
complete pathway simulation : E-cell,
Genomic analysis for DNA & Protein sequences ,
Strategies and options for similarity search ,
flowcharts for protein structure prediction
LIST OF EXPERIMENTS :
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.
== END OF UNITS==
Syllabus of CSIT-802(C) Web & Information Retrieval (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction : Information vers us dat a retrieval, the retrieval process,
taxonomy of Information Retrieval Models.
UNIT-2 :
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.
UNIT-3 :
Keyword based Queries,
User Relevance Feedback : Query Expansion and Rewriting,
Document preprocessing and clustering,
Indexing and Searching : Inverted Index construction,
Introduction to Pattern matching.
UNIT-4 :
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.
UNIT-5 :
Introduction to online IR Systems,
Digital Library searches and web Personalization
LIST OF EXPERIMENTS :
Students must experiment on various information retrieval systems like page rank etc
== END OF UNITS==
Syllabus of CSIT-802(D) Block Chain Technology (Departmental Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction and crypto foundation : Elliptic curve cryptography, ECDSA,
Cryptographic hash function, SHA-256, Merkle trees,
Cryptocurrencies.
UNIT-2 :
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.
UNIT-3 :
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
UNIT-4 :
Smart Contracts Different Blockchains and Consensus mechanisms
UNIT-5 :
Blockchain and Security R3, CORDA and Hyperledger System architecture,
ledger format, chain code,
transaction flow and ordering, private channels,
membership service providers, case studies.
LIST OF EXPERIMENTS :
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
== END OF UNITS==
Syllabus of CSIT-803 (A) Digital Marketing and SEO (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 :
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
UNIT-2 :
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.
UNIT-3 :
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.
UNIT-4 :
Managing Digital Marketing: Content Production;
Video based marketing;
Credibility and Digital Marketing;
IoT;
User Experience;
Future of Digital Marketing.
UNIT-5 :
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
== END OF UNITS==
Syllabus of CSIT-803(B) Quantum Computing (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Introduction to quantum mechanics : Postulates of quantum mechanics,
Qubit and quantum states, Vector Spaces,
Single Qubit Gates, multiple Qubit Gates, Controlled Gates,
Composite Gates, Matrices and operators.
UNIT- 2 :
Density operators : Density Operator for a Pure State, Density Operator for a Mixed State,
Properties of a Density Operator, Characterizing Mixed States,
Completely Mixed States, Partial Trace and Reduced Density Operator.
Quantum measurement theory : Distinguishing Quantum States and Measurement,Projective Measurements,
Measurements on Composite Systems, Generalized Measurements,
Positive Operator Valued Measures.
UNIT-3 :
Entanglement : Quantum state entanglement, Bell’s Theorem, The Pauli Representation,
Using Bell States For Density Operator Representation,
Quantum gates and circuits : Single Qubit Gates, The Z Y Decomposition, Basic Quantum Circuit Diagrams, Controlled Gates,
Application of Entanglement in teleportation and supper dense coding.,
Distributed quantum communication
Quantum Computer : Guiding Principles, Conditions for Quantum Computation, Harmonic Oscillator Quantum Computer,
Optical Photon Quantum Computer – Optical cavity Quantum electrodynamics, Ion traps, Nuclear Magnetic resonance.
UNIT-4 :
Quantum Algorithm : Hadamard Gates, The Phase Gate, Matrix Representation of Serial and Parallel Operations,
Quantum Interference, Quantum Parallelism and Function Evaluation,
Deutsch -Jozsa Algorithm, Quantum Fourier Transform,
Phase Estimation, Shor’s Algorithm ,
Quantum Searching and Grover’s Algorithm
UNIT-5 :
Quantum Error Correction : Introduction, Shor code, Theory of Quantum Error Correction,
Constructing Quantum Codes, Stabilizer codes, Fault Tolerant Quantum Computation,
Entropy and information –Shannon Entropy,
Basic properties of Entropy,Von Neumann,
Strong Sub Additivity, Data Compression,
Entanglement as a physical resource.
== END OF UNITS==
Syllabus of CSIT- 803 (C) Cyber Laws and Forensics (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 :
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,
UNIT-2 :
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.
UNIT-3 :
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.
UNIT-4 :
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
UNIT-5 :
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
== END OF UNITS=
Syllabus of CSIT-803 (D) ROBOTICS (Open Elective)
Source: (rgpv.ac.in)
UNIT-1 :
Fundamentals of Robot : Robot – Definition – Robot Anatomy
Co-ordinate Systems, Work Envelope, types and classification
Specifications – Pitch, Yaw, Roll, Joint Notations, Speed of Motion,
Pay Load – Robot Parts and Functions
Need for Robots – Different Applications
UNIT- 2 :
Robot Drive Systems and End Effectors : Pneumatic Drives , Hydraulic Drives, Mechanical Drives, Electrical Drives,
D.C. Servo Motors, Stepper Motor, A.C. Servo Motors – Salient Features,
Applications and Comparison of Drives End Effectors – Grippers – Mechanical Grippers,
Pneumatic and Hydraulic Grippers, Magnetic Grippers, Vacuum Grippers;
Two Fingered and Three Fingered Grippers;
Internal Grippers and External Grippers;
Selection and Design Considerations.
UNIT-3 :
Sensors and Machine Vision : Requirements of a sensor, Principles and
Applications of the following types of sensors– Position of sensors (Piezo Electric Sensor, LVDT, Resolvers,
Optical Encoders, Pneumatic Position Sensors),
Range Sensors (Triangulation Principle, Structured, Lighting Approach, Time of Flight Range Finders, Laser Range Meters),
Proximity Sensors (Inductive, Hall Effect, Capacitive, Ultrasonic and Optical Proximity Sensors),Touch Sensors, (Binary Sensors, Analogue Sensors),
Wrist Sensors, Compliance Sensors, Slip Sensors. Camera, Frame Grabber,
Sensing and Digitizing Image Data – Signal Conversion, Image Storage, Lighting Techniques.
Image Processing and Analysis
Data Reduction : Edge detection, Feature Extraction and Object Recognition -Algorithms.
Applications– Inspection, Identification, Visual Serving and Navigation
UNIT-4 :
Robot Kinematics and Robot Programming : Forward Kinematics, Inverse Kinematics and Differences;
Forward Kinematics and Reverse Kinematics of Manipulators with Two, Three Degrees of Freedom (In 2Dimensional),
Four Degrees of Freedom (In 3 Dimensional) – Deviations and Problems.
Teach Pendant Programming, Lead through programming,
Robot programming Languages – VAL Programming – Motion Commands,
Sensor Commands, End effecter commands, and Simple programs
UNIT-5 :
Implementation and Robot Economics : RGV, AGV;
Implementation of Robots in Industries – Various Steps;
Safety Considerations for Robot Operations;
Economic Analysis of Robots – Pay back Method, EUAC Method, Rate of Return Method.
== END OF UNITS==