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GANPAT UNIVERSITYFACULTY OF ENGINEERING & TECHNOLOGY

Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT701 Subject Name Compiler DesignTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Regular expression, Finite Automata, Grammar, Context free grammarLearning Outcome:Upon successful completion of the course, the student should be able to:

Perform lexical analysis and various parsing techniques. Construct compiler

Theory syllabusUnit Content Hrs

1. Introduction: Introduction to translators- Assembler, Compiler, Interpreter, Difference between Compiler and Interpreter, Linker, Loader , one pass compiler, multi pass compiler, cross compiler , The components of Compiler, Stages of Compiler: Front end, Back end, Qualities of Good Compiler

08

2. Lexical Analysis: The Role of the Lexical Analyzer, Specification of Lexemes, Tokens and pattern. Recognition of Tokens.

04

3. Syntax Analysis: The Role of the Parser, Types of grammar, CFG, Left most derivation , Right most derivation, Parse Tree, Restriction on CFG, Ambiguous grammar, Top-Down Parsing, Issues of CFG, Recursive Decent Parser, Construction of Predictive Parsing Table , LL (1) Grammar, String Parsing using M-Table, Bottom-Up Parsing: Handle, Shift-reduce parser, LR parsers: LR (0), SLR (1), LALR (1), CLR (1), String parsing procedure using LR parser, R-R and S-R Conflicts.

12

4. Syntax-Directed Translation: Syntax Directed Definitions, construction of syntax tree, L-Attributed Definitions, Bottom up evaluation of S-attribute definition.

05

5. Intermediate Code Generation: Implementation of Three Address Code, Intermediate code for all constructs of programming languages (expressions, if-else, loops, switch case etc.)

04

6. Code Optimization: Introduction, Optimization of Basic Blocks, Loop Optimization Techniques 047. Code Generation: Introduction to Code Generation, Machine architecture currently in use,

Major issues of code generations04

8. Error Detection and Recovery: Functions of error handler, Classification of Errors: Run time error & Compile time error: Lexical , Syntax & Semantic phase error

04

Practical contentExperiments/Practical/Simulations would be carried out based on syllabusText Books1. Compilers: Principles, Techniques and Tools By Aho, Sethi, and Ullman, Addison-Wesley, 1986

Reference Books1. Concept of Compiler Design, By Adesh K. Pandey2. Compiler Design By O G Kakade, 4th Edition


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT702 Subject Name Artificial IntelligenceTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Basics of programming language and algorithms.Learning Outcome:Upon successful completion of the course, the student should be able to:

Identify problems those are amenable to solution by AI methods and which AI method may be suited to solving a given problem.

Implement various AI algorithmsTheory syllabusUnit Content Hrs

1. Introduction of AI 032. Problems and Problem Spaces & Search:

AI Problems, Underlying Assumptions, The Level Of the Model, Criteria for success, Defining the Problem as State Space Search, Production Systems, Problem Characteristics, Issues in the Design of Search Programs, Problems

04

3. Heuristic Search TechniquesGenerate & Test, Hill Climbing, Best First Search, Problem Reduction, Constraint Satisfaction, Means-Ends Analysis

07

4. Game PlayingMini-Max Search Procedure, Alpha-Beta Pruning

02

5. Expert System Architecture & Tools:Introduction, Rule Based System Architecture, Nonproduction System Architecture, Dealing with Uncertainty, Knowledge Acquisition & Validation, System Building Tools

04

6. Languages For AI ProblemsProlog

03

7. Probabilistic Reasoning:Fuzzy Logic

03

8. Artificial Neural NetworksBiological Motivation, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptron

06

9. Supervised LearningDecision Tree, Bayesian Decision Theory

04

10. Unsupervised LearningClustering, Nonparametric Methods

05

11. Evolutionary computingDifferent Genetic Algorithms And Its Applications, Genetic Programming

04

Practical contentExperiments/Practical/Simulations would be carried out based on syllabusText Books1. Artificial Intelligence, By Rich E. & Kevin Knight, Tata McGraw Hill.

Reference Books1. Machine Learning, By Tom M. Mitchell, Tata McGraw-Hill.

2. Genetic Algorithms in Search, Optimization, and Machine Learning, D. E. Goldberg, Addison-Wesley.

3. Neural Networks: A Comprehensive Foundation, S. Haykin, PHI.

4. Introduction to Turbo Prolog, Townsend, Carl.


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT703 Subject Name Minor ProjectTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit - - 4 - 4 Theory - - -Hours - - 8 - 8 Practical 60 40 100Pre-requisites:Understanding of Tools & Technology and Database Management SystemLearning Outcome:After successful completion of this course, student will be able to

Develop an application using different technology Learn how to work in a team. Think and apply new ideas in developing a project.

Practical contentSr.No Content1 Students have to carry out an project development work under the guidance of faculty member using

the knowledge of subjects that he/she has learned up to 6 th semester. Students have to submit a report with code at the end of the semester


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT704 Subject Name Fundamentals of Image ProcessingTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Background of computer programming and algorithms.Learning Outcome:Upon successful completion of the course, the student should be able to:

Understand the fundamental concepts of image processing Perform image enhancement and restoration Apply the concepts of image segmentation


1. Introduction: Overview, examples of fields that use Digital Image Processing, fundamental steps in Digital Image Processing, components of an Image Processing System.

05

2. Digital Image Fundamentals: Elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, basic relationships between Pixels, linear and nonlinear operations.

07

3. Image Enhancement in the Spatial Domain: Background, some basic gray level transformations, histogram processing, enhancement using arithmetic/logic operations, basics of spatial filtering, smoothing spatial filters, sharpening spatial filters, combining spatial enhancement methods.

12

4. Introduction to Image Restoration: Image degradation/restoration model, noise models 055. Morphological Image Processing: Preliminaries, dilation and erosion, opening and closing, the

Hit-or-Miss transformation, some basic morphological algorithms.05

6. Image Segmentation: Detection of discontinuities, edge linking and boundary detection, thresholding, region-based segmentation.

06

7. Representation and Description: Representation, boundary descriptors, regional descriptors 05

Practical contentExperiments/Practical/Simulations would be carried out based on syllabusText Books1. Digital Image Processing By Rafael C. Gonzalez and Richard E. Woods.

Reference Books1. Digital Image processing By S. Sridhar, (Oxford University Press)2. Fundamentals of Image Processing By Anil K Jain


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT705 Subject Name Internet of ThingsTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Fundamentals of computer network, wireless sensor network, communication & internet technology, web technology, information securityLearning Outcome:After successful completion of this course, student will be able to

Use of Devices, Gateways and Data Management in IoT. Applications of IoT in Industrial Automation and Real World.


1. Overview and Introduction :Internet of Things (IoT) and Web of Things (WoT): What's WoT?, The Internet of Things Today, Time for Convergence, Towards the IoT Universe, Internet, of Things Vision, IoT Strategic Research and Innovation Directions, IoT Applications, Future Internet Technologies, Infrastructure, Networks and Communication, Processes, Data Management, Security, Privacy & Trust, Device Level Energy Issues, IoT Related Standardization, Recommendations on Research Topics.

10

2. M2M to IoT – A Basic Perspective:Introduction, Some Definitions, M2M Value Chains, IoT Value Chains, An emerging industrial structure for IoT, The international driven, global value chain and global information monopolies.

05

3. M2M to IoT-An Architectural Overview:Building architecture, Main design, principles and needed capabilities, An IoT architecture outline, standards considerations.

06

4. IoT Architecture -State of the Art:Introduction, State of the art, Architecture Reference Model- Introduction, Reference Model and architecture, IoT reference Model, IoT Reference Architecture- Introduction, Functional View, Information View, Deployment and Operational View, Other Relevant architectural views.

09

5. IoT Applications for Value Creations:Introduction, IoT applications for industry: Future Factory Concepts, Brownfield IoT, Smart Objects, Smart Applications, Four Aspects in your Business to Master IoT, Value, Creation from Big Data and Serialization, IoT for Retailing Industry, IoT For Oil and Gas, Industry, Opinions on IoT Application and Value for Industry, Home Management, eHealth.

07

6. Internet of Things Privacy, Security and Governance:Introduction, Overview of Governance, Privacy and Security Issues, Contribution from FP7 Projects, Security, Privacy and Trust in IoT-Data-Platforms for Smart Cities, First Steps Towards a Secure Platform, Smartie Approach.

08

Practical contentExperiments/Practical/Simulations would be carried out based on syllabus

Text Books1. From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence: By Jan

Holler, Vlasios Tsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos, David Boyle, 1st Edition, Academic Press, 2014.

Reference Books1. Internet of Things (A Hands-on-Approach), by Vijay Madisetti and Arshdeep Bahga, 1st Edition, VPT,

2014.2. Getting Started with the Internet of Things: by Cuno Pfister, O‟Reilly Media, 20113. Rethinking the Internet of Things: A Scalable Approach to Connecting Everything, by Francis daCosta,

1st Edition, Apress Publications, 2013


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT706 Subject Name Mobile ComputingTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Computer NetworksLearning Outcome:Upon successful completion of the course, the student should be able to:

Understand mobile adhoc network, wireless network and cellular network


1. Introduction:Challenges in mobile computing, coping with uncertainties, resource poorness, bandwidth, etc. Cellular architecture, co-channel interference, frequency reuse, capacity increase by cell splitting. Evolution of mobile system: CDMA, FDMA, TDMA, GSM, Introduction to 3G and 4G Standards.

12

2. Mobility Management:Cellular architecture, Co-channel interference, Mobility: handoff, types of handoffs; location management, HLR-VLR scheme, hierarchical scheme, predictive location management schemes. Mobile IP, cellular IP.

10

3. Publishing & Accessing Data In Air:Pull and push based data delivery models, data dissemination by broadcast, broadcast disks, directory service in air, and energy efficient indexing scheme for push based data delivery.

06

4. Ad hoc Network Routing Protocols:Ad hoc network routing protocols, destination sequenced distance vector algorithm, cluster based gateway switch routing, global state routing, dynamic source routing, ad hoc on-demand routing, location aided routing, zonal routing algorithm.

12

5. Mobile Transaction and m-Commerce:

Models for mobile transaction, Recovery model for mobile transactions. Electronic payment and protocols for mobile commerce

05

Practical contentExperiments/Practical/Simulations would be carried out based on syllabusText Books1. Mobile Computing By Talukder and Yavagal, Tata McGraw Hill

Reference Books1. Mobile Communication By J.Schiller


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT707 Subject Name Machine LearningTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Background of mathematics and programming.Learning Outcome:Upon successful completion of the course, the student should be able to:

Understand the fundamental issues and challenges of machine learning. Understand strengths and weaknesses of many popular machine learning approaches.


1. Introduction of Machine Learning: Basic definitions, types of learning, Parameter Learning, hypothesis space and inductive bias, evaluation, cross-validation

05

2. Classification and Regression: Classification, Hypothesis Representation, Decision Boundary, Linear and Logistic Regression, Multiclass classification

05

3. Neural Network: Introduction, Application, Perceptron, Single layer Network, multi-layer network, backpropagation, introduction to deep neural network

06

4. Generative Models: Probability, Bayes Rule, Naïve Bayes, MAP and Bayesian learning 045. Evaluating a Learning Algorithm 046. Support Vector Machines and Kernel Methods 057. Unsupervised Learning: Introduction, Clustering, K-means Algorithm, Optimization Objective,

Random Initialization, Adaptive hierarchical clustering04

8. Supervised Learning: Decision Tree, Bayesian Decision Theory 049. Density Estimation: Problem Motivation, Gaussian Distribution 04

10. Evolutionary computing: Different Genetic Algorithms And Its Applications, Genetic Programming

04

Practical contentExperiments/Practical/Simulations would be carried out based on syllabusText Books1. Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David,

Published 2014 by Cambridge University Press.Reference Books

1. Classification and regression trees, Leo Breiman, Wadsworth International Group, 19842. Machine Learning, By Tom M. Mitchell, Tata McGraw-Hill3. Genetic Algorithms in Search, Optimization, and Machine Learning, D. E. Goldberg,

Addison-Wesley4. Neural Networks: A Comprehensive Foundation, S. Haykin, PHI


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT708 Subject Name Parallel Processing & ArchitectureTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Background of computer fundamentals and programming.Learning Outcome:Upon successful completion of the course, the student should be able to:

Describe different parallel processing architectures based on relationships between processingelements, instruction sequence, memory and interconnected network.

Identify algorithms, which require parallelization as part of system design or performanceenhancement.

Design and develop parallel algorithms for shared and distributed memory models.


1. Introduction: Von Neumann architecture, Why do we need high speed computing?, How do we increase the speed of computers?, Some interesting features of parallel computers.

03

2. Solving Problems in Parallel: Temporal parallelism, Data Parallelism, Combined temporal and data parallelism, Data parallelism with dynamic assignment, Data parallelism with quasi-dynamic assignment, Comparison of Temporal and Data Parallel Processing.

04

3. Instruction level Parallel Processing: Pipelining of Processing Elements, Delays in Pipeline Execution, Delay due to resource constraints, Delay due to data dependency, Pipeline delay due to branch instructions, Hardware modification to reduce delay due to branches, software modification to reduce delay due to branches, Difficulties in Pipelining,

04

4. Parallel Algorithms: Models of Computation, random access machine, parallel random access machine, Interconnection networks, combinational circuits, Analysis of Parallel Algorithms, Running time, Number of processors and cost,

03

5. Introduction to Parallel Processing: Architectural Classification schemes, Multiplicity of instruction – data stream, Serial versus parallel processing, Parallelism versus pipelining, Parallel Processing Applications.

03

6. Principles of Pipelining and Vector Processing: Principles of designing Pipeline Processors, Instruction prefetch and branch handling, Data buffering and busing structures, Internal forwarding and register tagging, Hazard detection and resolution.

04

7. Structures and Algorithms for Array Processors: SIMD Array Processors, SIMD computer organization, Masking and data routing mechanisms, Inter PE communications, SIMD Interconnection Networks, Static versus dynamic networks, Mesh connected Illiac network

04

8. Multiprocessor Architecture and Programming: Multiprocessor Operating Systems, Classification of Multiprocessor operating systems, Software requirements for Multiprocessors, Operating system requirements.

03

9. Processes, Shared Memory and Simple Parallel Programs: Introduction, Processes and 03

processors, Shared memory–1, Forking-Creating Processes, Shared memory-2, Processes are randomly scheduled – Contention

10. Basic Parallel Programming Techniques: Introduction, Loop splitting, Ideal speedup, Spin-locks, Contention and Self-scheduling, Histogram.

04

11. Barriers and Race Conditions: Introduction, The Barrier Calls, Expression splitting. 0412. Introduction to scheduling – Nested loops: Introduction, Variations on loop splitting, Variation

on self – scheduling, Indirect scheduling.03

13. Overcoming Data Dependencies: Introduction, Induction variable, Forward dependency, Block scheduling and forward dependency, Backward dependency, Splittable loops, Special scheduling – Assign based on condition.

03

Practical contentExperiments/simulation based on the syllabus.Text Books1. Computer Architecture and Parallel Processing -By Kai Hwang, McGraw Hill International Edition

Reference Books1. Parallel Computers – Architecture and Programming -By V. Rajaraman and C. Siva Ram Murthy,

PHI Publication2. Introduction to Parallel Programming -By Steven Brawer, Academic Press Inc3. Computer Architecture and Organization -By Hayes, Tata McGraw Hill4. Computer Organization and Architecture -By Stallings, Pearson Publication

Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT709 Subject Name Big data AnalyticsTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Fundamentals of mathematics and SQL.Learning Outcome:Upon successful completion of the course, the student should be able to:

Identify and distinguish big data analytics applications Describe big data analytics tools Explain big data analytics techniques Present cases involving big data analytics in solving practical problems


1. Introduction To Big DataIntroduction– distributed file system–Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, and Big data applications. Algorithms using map reduce.

07

2. Big Analytic Tools And TechniquesBig Data – Apache Hadoop & Hadoop EcoSystem, Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce -, Data Serialization, Introduction to Data Analysis with Spark.

12

3. HDFS, HIVE AND HIVEQL, HBASE HDFS-Overview, Installation and Shell, Java API; Hive Architecture and Installation, Comparison with Traditional Database, HiveQL Querying Data, Sorting And Aggregating, Map Reduce Scripts, Joins & Sub queries, HBase concepts, Advanced Usage, Schema Design, Advance Indexing, PIG, Zookeeper , how it helps in monitoring a cluster, HBase uses Zookeeper and how to Build Applications with Zookeeper.

12

4. NoSQLWhat is it?, Where It is Used Types of NoSQL databases, Why NoSQL?, Advantages of NoSQL, Use of NoSQL in Industry, SQL vs NoSQL, NewSQL

08

5. Case Studies 06Practical contentExperiments/simulation based on the syllabus.Text Books1. Big Data and Analytics by Seema Acharya,Subhashini Chellappan,Paperback,2015.

Reference Books1. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press,

2011. Ron Bekkerman, Mikhail Bilenko and John Langford, Scaling up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, 2011.

2. Tom White, Hadoop: The Definitive Guide, O‟Reilly Media, Third Edition, 2012.3. Bill Franks, Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with

Advanced Analytics, Wiley, 2012.4. Michael Minelli, Michele Chambers, and Ambiga Dhiraj, Big Data, Big Analytics: Emerging Business

Intelligence and Analytic Trends for Today's Businesses, Wiley, 2013.5. Frank J. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, Wiley, 2012.


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT710 Subject Name Public Key InfrastructureTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Cryptography, network security.Learning Outcome:Upon successful completion of the course, the student should be able to:

Understand public key infrastructures Understand centralized and decentralized infrastructure Understand concept of digital certificates Learn various secured services provided by internet


1. Cryptography Basics 032. Public Key Infrastructure: Introduction, Components of PKI, CA Functions, Digital Certificate,

Certificate Management, Private Key Management, Public Key Cryptography Standards06

3. PKI Architecture: Introduction, Types of PKI Architecture: Single CA Architecture (Basic Trust List Model, Certificate Path Construction in a Single CA and Basic Trust List Architecture ), Enterprise PKI Architecture, Hierarchical PKI Architecture (Certificate Path Construction in a Hierarchical PKI Architecture), Mesh PKI Architecture(Certificate Path Construction in a Mesh PKI Architecture), Hybrid PKI Architecture (Certificate Path Construction in a Bridge PKI Architecture)

06

4. Internet Security Protocols :Introduction, Basic Concepts, Secure Socket Layer ,Secure Electronic Transaction, SSL Vs. SET, 3-D Secure Protocol, Email Security: PEM,PGP,S/MIME

07

5. User-Authentication Mechanisms: Authentication Basics , Passwords, Authentication Tokens, Certificate Based Authentication, Biometric Authentication, Kerberos, Entity Authentication mechanism: Challenge response Protocol, Zero knowledge protocol.

06

6. Digital Signature, Message Digest Algorithm – MD5 057. Symmetric Key Algorithm: Advanced Encryption Standard (AES) 038. Asymmetric Key Algorithm: Knapsack Algorithm, Elgamal Crypto System 049. Key Management: Introduction, Key Distribution Center (KDC), A Simple Protocol using KDC,

Needham–Schroeder Symmetric Key Protocol, Otway-Rees Protocol05

Practical contentExperiments/simulation based on the syllabus.Text Books1. Public Key Infrastructure Implementation and Design By Suranjan Choudhury, Kartik Bhatnagar, and

Wasim HaqueReference Books

1. Cryptography and Network Security By Atul Kahate (3rd Edition)2. Cryptography and Network Security By Forouzan


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT711 Subject Name Data Mining and Data WarehousingTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit 3 0 1 - 4 Theory 40 60 100Hours 3 0 2 - 5 Practical 30 20 50Pre-requisites:Basics of database and data warehouseLearning Outcome:Upon successful completion of the course, the student should be able to:

Understand the difference between database and data warehouse Learn how to pre-process data before applying data mining techniques Able to Choose the data-mining task (classification, regression, clustering, Association Rules etc.). Understand mathematical foundations of data mining tools Interpret the results produced by data mining


1. Introduction: Basics of data mining, related concepts, Data mining techniques. 062. Data Pre-Processing: Data Pre-processing, Handling noise & outliers - Integration,

Discretization, Smoothing, Transformation, 3-4-5 rule etc.06

3. Association Rule Mining: Association Rule Mining,- Support & Confidence heuristic, Apriori algorithm, Enhancement to Apriori (DIC, Hashing, Partitioning, Sampling etc) , FP-Tree & FP-Growth algorithm, Criticize to Support & Confidence, Interest & Lift Extended association rule mining (Hierarchical, Multimedia, Text, Quantitative etc)

06

4. Clustering: Cluster Analysis, Types of Data in Cluster Analysis, Interval-Scaled Variables, Categorical, Ordinal, and Ratio-Scaled Variables, Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical Methods, Density-Based Methods

10

5. Classification: Decision Tree Classification, Info-Gain criteria to select best split, Naive Bayes Classifier, Precision & Recall evaluating performance of classifier accuracy , Confusion matrix for evaluating performance of classifier accuracy, Correlation analysis , Chi-square statistic

06

6. Web Mining: Introduction to Web Mining, Web content mining, Web usage mining, Web Structure mining, Web log structure and issues regarding web logs

05

7. Data Warehousing: OLAP & OLTP, Data warehouse & Data mart, OLAM architecture, Extraction, Transform & Loading (ETL) concept for generic, two-tier, three -tier architecture, Data warehousing schema - Star, Snowflake, Fact Constellation (Galaxy) - Data Cube , Operations on Data cube (slicing, roll up, roll down, drill up etc)

06

Practical contentExperiments/simulation based on the syllabus.Text Books1. Han, Kamber ,“Data Mining Concepts and Techniques”, Morgan Kaufmann

Reference Books1. Paulraj Ponniah, “Data Warehousing Fundamentals”, John Wiley.2. M.H. Dunham, “Data Mining Introductory and Advanced Topics”, Pearson Education.3. Ralph Kimball, “The Data Warehouse Lifecycle toolkit”, John Wiley.


Programme Bachelor of Technology Branch/Spec. Information TechnologySemester VII Version 2.0.0.0Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014Subject code 2IT712 Subject Name Soft SkillsTeaching scheme Examination scheme (Marks)(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TWCredit - - 2 - 2 Theory 0 0 0Hours - - 4 - 4 Practical 60 40 100Pre-requisites:NilLearning Outcome:Upon successful completion of the course, the student should be able to:

Develop interpersonal skills and be effective goal oriented team player To be professional with idealistic, practical and moral values. To develop communication and problem solving skills. To re-engineer attitude and understand its influence on behaviour.

Practical contentSr.No Content Hrs

1 Self-Analysis: SWOT analysis, who am I, attributes, importance of self-confidence, self-esteem. 062 Attitude: Factors influencing attitude, challenges and lessons from attitude, etiquette. 063 Motivation: Factors of motivation, self-talk, intrinsic & extrinsic motivators. 054 Goal setting: Wish list, smart goal, blue print for success, short term, long term, life time goals 055 Time management: value of time, diagnosing time management, weekly planner to do list,

prioritizing work05

6 Gratitude: Understanding the relationship between Leadership Networking & Team work. Assessing Interpersonal Skills Situation description of Interpersonal Skill.Team Work: Necessity of Team Work Personally, Socially and Educationally

11

7 Leadership: Skills for a good leader, assessment of leadership skills 058 Stress management: Causes of stress and its impact, how to manage & distress, circle of control,

stress busters.06

9 Emotional Intelligence: What is emotional intelligence, emotional quotient, why emotional intelligence matters, emotion scales, managing emotions.

06

10 Conflict resolution: Conflicts in human relations – reasons case studies, approaches to conflict resolution.

05

Text Books

Reference Books1 SOFT SKILLS, 2015, Career Development Centre, Green Pearl Publications2 Covey Sean, Seven Habit of Highly Effective Teens, New York, Fireside Publishers, 1998.3 Carnegie Dale, How to win Friends and Influence People, New York: Simon & Schuster, 1998.

ganpat · web viewgenerate & test, hill climbing, best first search, problem reduction,...

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