GANPAT UNIVERSITY FACULTY OF ENGINEERING & …...Perform lexical analysis and various parsing techniques. Construct compiler Theory syllabus Unit Content Hrs 1. Introduction: 1 Introduction

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY

TEACHING AND EXAMINATION SCHEME Programme Bachelor of Technology Branch/Spec. Computer Engineering

Semester VII

Effective from Academic Year 2017-18 Effective for the batch Admitted in July 2014

Subject Code

Subject Name

Teaching scheme Examination scheme (Marks)

Credit Hours (per week) Theory Practical

Lecture(DT) Practical(Lab.) Lecture(DT) Practical(Lab.) CE SEE Total CE SEE Total

L TU Total P TW Total L TU Total P TW Total

2CE701 Compiler Design 3 - 3 1 - 1 3 - 3 2 - 2 40 60 100 30 20 50

2CE702 Artificial Intelligence 3 - 3 1 - 1 3 - 3 2 - 2 40 60 100 30 20 50

2CE703 Minor Project - - - 4 - 4 - - - 8 - 8 - - - 60 40 100

Elective-IV 3 - 3 1 - 1 3 - 3 2 - 2 40 60 100 30 20 50

Elective-V 3 - 3 1 - 1 3 - 3 2 - 2 40 60 100 30 20 50

2CE712 Soft Skills - - - 2 - 2 - - - 4 4 0 0 0 60 40 100

Total 12 - 12 10 - 10 12 - 12 20 - 20

Elective-IV Elective-V

2CE704 Fundamentals of Image Processing 2CE708 Parallel Processing & Architecture

2CE705 Internet of Things 2CE709 Big data Analytics

2CE706 Mobile Computing 2CE710 Public Key Infrastructure

2CE707 Machine Learning 2CE711 Data Mining and Data Warehousing

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY Programme Bachelor of Technology Branch/Spec. Computer Engineering

Semester VII Version 2.0.0.0


Subject code 2CE701 Subject Name Compiler Design


(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total

L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100

Hours 3 0 2 - 5 Practical 30 20 50

Pre-requisites:

Regular expression, finite automata, grammar, context free grammar

Learning Outcome:

Upon successful completion of the course, the student should be able to:

Perform lexical analysis and various parsing techniques.

Construct compiler

Theory syllabus

Unit Content Hrs

1. 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

8

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

4

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

5

5. 3 Intermediate Code Generation: Implementation of Three Address Code, Intermediate code for all constructs of programming languages (expressions, if-else, loops, switch case etc.)

4

6. 4 Code Optimization: Introduction, Optimization of Basic Blocks, Loop Optimization Techniques 4

7. Code Generation: Introduction to Code Generation, Machine architecture currently in use, Major issues of code generations

4

8. 6 Error Detection and Recovery: Functions of error handler, Classification of Errors: Run time error & Compile time error: Lexical , Syntax & Semantic phase error

4

Practical content

Experiments/Simulation would be carried out based on the Syllabus

Text Books

1. Compilers: Principles, Techniques and Tools By Aho, Sethi, and Ullman, Addison-Wesley, 1986

Reference Books

1. 1 Concept of Compiler Design, By Adesh K. Pandey

2. Compiler Design By O G Kakade, 4th Edition

GANPAT UNIVERSITY




Subject code 2CE702 Subject Name Artificial Intelligence



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Basics of Programming Language and Algorithms

Learning Outcome:


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

Implement various AI Algorithms.

Theory syllabus

Unit Content Hrs

1. 1 Introduction of AI 3

2. 2 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

4

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

7

4. Game Playing Mini-Max Search Procedure, Alpha-Beta Pruning

2

5. 3 Expert System Architecture & Tools: Introduction, Rule Based System Architecture, Nonproduction System Architecture, Dealing with Uncertainty, Knowledge Acquisition & Validation, System Building Tools

4

6. 4 Languages For AI Problems Prolog

3

7. Probabilistic Reasoning: Fuzzy Logic

3

8. 6 Artificial Neural Networks Biological Motivation, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptron

6

9. Supervised Learning Decision Tree, Bayesian Decision Theory

4

10. Unsupervised Learning Clustering, Nonparametric Methods

5

11. Evolutionary computing Different Genetic Algorithms And Its Applications, Genetic Programming

4

Practical content


Text Books

1. Artificial Intelligence, By Rich E. & Kevin Knight, Tata McGraw Hill.

Reference Books

1. 1 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.

GANPAT UNIVERSITY




Subject code 2CE703 Subject Name Minor Project



L TU P TW

Credit - - 4 - 4 Theory - - -

Hours - - 8 - 8 Practical 60 40 100

Pre-requisites:

Understanding of Tools & Technology and Database Management System

Learning 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 content

1 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 6th semester. Students have to submit a report with code at the end of the semester

GANPAT UNIVERSITY




Subject code 2CE704 Subject Name Fundamentals of Image Processing



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Back ground of computer programming and Algorithms

Learning Outcome:


Understand the fundamental concepts of image processing

Perform image enhancement and restoration

Apply the concepts of image segmentation

Theory syllabus

Unit Content Hrs

1. 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. 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 05

5. 3 Morphological Image Processing: Preliminaries, dilation and erosion, opening and closing, the Hit-or-Miss transformation, some basic morphological algorithms.

05

6. 4 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 content


Text Books

1. Digital Image Processing By Rafael C. Gonzalez and Richard E. Woods.

Reference Books

1. 1 Digital Image processing By S. Sridhar, (Oxford University Press)

2. Fundamental of Image Processing by Anil K. Jain

GANPAT UNIVERSITY




Subject code 2CE705 Subject Name Internet of Things



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Fundamentals of computer network, wireless sensor network, communication & internet technology, web technology, information security

Learning 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.

Theory syllabus

Unit Content Hrs

1. 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. 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. 3 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. 4 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 content

Experiments/Practical’s/Simulations would be carried out based on syllabus

Text Books

1. 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 Books

1. 1 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, 2011

3. Rethinking the Internet of Things: A Scalable Approach to Connecting Everything, by Francis daCosta, 1st Edition, Apress Publications, 2013

GANPAT UNIVERSITY




Subject code 2CE706 Subject Name Mobile Computing



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Computer Networks

Learning Outcome:


Understand mobile adhoc network, wireless network and cellular network

Theory syllabus

Unit Content Hrs

1. 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. 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. 3 Mobile Transaction and m-Commerce: Models for mobile transaction, Recovery model for mobile transactions. Electronic payment and protocols for mobile commerce

05

Practical content


Text Books

1. Mobile Computing By Talukder and Yavagal, Tata McGraw Hill

Text Books

1. Mobile Communication By J.Schiller

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY Programme Bachelor of Technology Branch/Spec. Information Technology



Subject code 2CE707 Subject Name Machine Learning



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Back ground of Mathematics and Programming

Learning Outcome:


Understand the fundamental issues and challenges of machine learning.

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

Theory syllabus

Unit Content Hrs

1. 1 Introduction of Machine Learning

Basic definitions, types of learning, Parameter Learning, hypothesis space and inductive bias, evaluation, cross-validation

05

2. 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

04

5. 3 Evaluating a Learning Algorithm 04

6. 4 Support Vector Machines and Kernel Methods 05

7. Unsupervised Learning: Introduction, Clustering, K-means Algorithm, Optimization Objective,

Random Initialization, Adaptive hierarchical clustering

04

8. 6 Supervised Learning: Decision Tree, Bayesian Decision Theory 04

9. Density Estimation: Problem Motivation, Gaussian Distribution 04

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

04

Practical content


Text Books

1. Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David, Published 2014 by Cambridge University Press.

Reference Books

1. 1 Classification and regression trees, Leo Breiman, Wadsworth International Group, 1984

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

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

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

GANPAT UNIVERSITY




Subject code 2CE708 Subject Name Parallel Processing & Architecture



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Back ground of Computer Fundamentals and Programming. Learning Outcome:


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

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

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

Theory syllabus

Unit Content Hrs

1. 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. 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. 3 Introduction to Parallel Processing: Architectural Classification schemes, Multiplicity of instruction – data stream, Serial versus parallel processing, Parallelism versus pipelining, Parallel Processing Applications

03

6. 4 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 Iliac network

04

8. 6 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 processors, Shared memory–1, Forking-Creating Processes, Shared memory-2, Processes are randomly scheduled – Contention

03

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. 04

12. 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, Split table loops, Special scheduling – Assign based on condition.

03

Practical content

Experiments/simulation based on the syllabus.

Text Books

1. Computer Architecture and Parallel Processing -By Kai Hwang, McGraw Hill International Edition

Reference Books

1. 1 Parallel Computers – Architecture and Programming -By V. Rajaraman and C. Siva Ram Murthy, PHI Publication

2. Introduction to Parallel Programming -By Steven Brawer, Academic Press Inc.

3. Computer Architecture and Organization -By Hayes, Tata McGraw Hill

4. \ Computer Organization and Architecture -By Stallings, Pearson Publication

GANPAT UNIVERSITY




Subject code 2CE709 Subject Name Big data Analytics



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Fundamentals of Mathematics and SQL

Learning Outcome:


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

Theory syllabus

Unit Content Hrs

1. 1 Introduction To Big Data Introduction– 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. 2 Big Analytic Tools And Techniques Big 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. NoSQL What 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. 3 Case Studies 06

Practical content


Text Books

1. Big Data and Analytics by Seema Acharya,Subhashini Chellappan,Paperback,2015.

Reference Books

1. 1 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.

GANPAT UNIVERSITY




Subject code 2CE710 Subject Name Public Key Infrastructure



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Cryptography, Network Security

Learning Outcome:


Understand public key infrastructures

Understand centralized and decentralized infrastructure

Understand concept of digital certificates

Learn various secured services provided by internet Theory syllabus

Unit Content Hrs

1. 1 Cryptography Basics 03

2. 2 Public Key Infrastructure: Introduction, Components of PKI, CA Functions, Digital Certificate, Certificate Management, Private Key Management, Public Key Cryptography Standards

06

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. 3 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. 4 Digital Signature, Message Digest Algorithm – MD5 05

7. Symmetric Key Algorithm: Advanced Encryption Standard (AES) 03

8. 6 Asymmetric Key Algorithm: Knapsack Algorithm, Elgamal Crypto System 04

9. Key Management: Introduction, Key Distribution Center (KDC), A Simple Protocol using KDC, Needham–Schroeder Symmetric Key Protocol, Otway-Rees Protocol

05

Practical content


Text Books

1. Public Key Infrastructure Implementation and Design By Suranjan Choudhury, Kartik Bhatnagar, and Wasim Haque

Reference Books

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

2. Cryptography and Network Security By Forouzan

GANPAT UNIVERSITY




Subject code 2CE711 Subject Name Data Mining and Data Warehousing



L TU P TW

Credit 3 0 1 - 4 Theory 40 60 100


Pre-requisites:

Basics of database and data warehouse

Learning Outcome:


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

Theory syllabus

Unit Content Hrs

1. 1 Introduction: Basics of data mining, related concepts, Data mining techniques. 06

2. 2 Data Pre-Processing: Handling noise & outliers - Integration, Discretization, Smoothing, Transformation, 3-4-5 rule etc.

06

3. 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. 3 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. 4 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. 6 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 content


Text Books

1. Han, Kamber ,“Data Mining Concepts and Techniques”, Morgan Kaufmann

Reference Books

1. 1 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.

GANPAT UNIVERSITY




Subject code 2CE712 Subject Name Soft Skills



L TU P TW

Credit - - 2 - 2 Theory 0 0 0

Hours 0 0 4 0 4 Practical 60 40 100

Pre-requisites:

Nil

Learning Outcome:


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 Contents

Sr. No.

Content Hrs

1. 1 Self-Analysis: SWOT analysis, who am I, attributes, importance of self-confidence, self-esteem. 06

2. 2 Attitude: Factors influencing attitude, challenges and lessons from attitude, etiquette. 06

3. Motivation: Factors of motivation, self-talk, intrinsic & extrinsic motivators. 05

4. Goal setting: Wish list, smart goal, blue print for success, short term, long term, life time goals 05

5. 3 Time management: value of time, diagnosing time management, weekly planner to do list, prioritizing work

05

6. 4 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 05

8. 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 Books

1. 1 Covey Sean, Seven Habit of Highly Effective Teens, New York, Fireside Publishers, 1998.

2. Carnegie Dale, How to win Friends and Influence People, New York: Simon & Schuster, 1998.

3. SOFT SKILLS, 2015, Career Development Centre, Green Pearl Publications

Documents

GANPAT UNIVERSITY FACULTY OF ENGINEERING & …...Perform lexical analysis and various parsing techniques. Construct compiler Theory syllabus Unit Content Hrs 1. Introduction: 1 Introduction