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Computer Science &
Engineering
Scheme & Syllabus
Of
M. Tech.
I. K. Gujral Punjab Technical University Jalandhar
Jalandhar-Kapurthala Highway Kapurthala
144603, Punjab
I. K. Gujral Punjab Technical University, Kapurthala
INDEX
Sr. No.
Subject
Page No.
1.
Scheme all semesters
01 - 02
2.
Syllabus 1st Semester
03 – 07
3.
Syllabus 2nd Semester
08 – 13
4.
Syllabus 3rd Semester
14 – 15
5.
Syllabus 4th Semester
16
Sr. No.
Elective Subject
Page No.
1.
Large-Scale Management Technique (Elective 2)
17
2.
Machine Learning
18
3.
Big Data Analytics
19
4.
Data Visualization
20
5.
Linux Programming
(Elective - I)
Scheme & Syllabus (CSE) Batch 2014 & Onwards
21 – 22
I. K. Gujral Punjab Technical University, Kapurthala
FIRST SEMESTER
Contact Hours: 30 Hrs.
Course
Code
Course Title
Load
Allocation
Marks Distribution
L
T
P
Internal
External
Total Credits
Marks
CSB-202
Higher Mathematics
3
-
-
40
60
100
3
CSE-203
University Elective
2
-
-
40
60
100
2
CSB-204
Advanced Algorithm Analysis
3
-
-
40
60
100
3
CSB-208
Distributed Operating System
3
-
2
50
50
100
4
CSB-209
Advanced Computer Networks
3
-
2
50
50
100
4
CSBE-218
Elective – 1
3
-
2
50
50
100
4
17
-
6
270
330
600
20
TOTAL
SECOND SEMESTER
Contact Hours: 30 Hrs.
Course
Code
Course Title
Load
Allocation
L
T
P
Marks
Distribution
Total
Marks
Credits
Internal External
CSE-205
Advanced Computer
Architecture
3
-
2
50
50
100
4
CSB-206
Advanced Database System
3
-
2
50
50
100
4
CSB-207
Web Services
3
-
2
50
50
100
4
CSE-210
Advanced English
2
-
-
40
60
100
2
CSB-211
Fundamental of Big Data
Analytics
3
-
-
40
60
100
3
CSB-212
Fundamental of Cloud
Computing
3
-
-
40
60
100
3
CSB-213
Seminar
-
-
-
40
60
100
2
17
-
06
310
390
700
22
TOTAL
THIRD SEMESTER
Course
Code
Contact Hours: 29 Hrs.
Course Title
Load
Allocation
Marks Distribution
L
T
P
Internal
External
Total Credits
Marks
CSB-214
Dissertation Part-I
-
-
-
40
60
100
8
CSB-215
Information Retrieve & Data
Mining
3
-
2
50
50
100
4
Elective - 2
3
-
2
50
50
100
4
6
-
4
140
160
300
16
CSBE-216
TOTAL
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 1 of 22
I. K. Gujral Punjab Technical University, Kapurthala
FOURTH SEMESTER
Course
Code
CSB-214
Contact Hours: 30 Hrs.
Course Title
Load
Allocation
Marks Distribution
Total
Marks
Credits
L
T
P
Internal
External
Dissertation Part-II
-
-
-
40
60
100
10
Elective-3
3
-
2
50
50
100
4
Elective-4
3
-
2
50
50
100
4
TOTAL
6
-
4
140
160
300
18
PROGRAMME ELECTIVE
Course
Code
Contact Hours: 29 Hrs.
Course Title
Load
Allocation
Marks Distribution
Total
Marks
Credits
L
T
P
Internal
External
Large-Scale
Management Technique
(Elective 2)
3
-
2
50
50
100
4
CSBE-217
Machine Learning
3
-
2
50
50
100
3
CSBE-219
Big Data Analytics
3
-
2
50
50
100
4
CSBE-220
Data Visualization
3
-
2
50
50
100
4
CSBE-2018
Linux Programming
(Elective - I)
3
-
2
50
50
100
4
15
-
10
250
250
500
19
CSBE-216
TOTAL
UC
05
PC Offered
53
PE Needed
16
UE Needed
02
Total credits Offered
(UC+PC+PE)
76
UC – University Core
PC – Programme Core
PE – Programme Elective
UE-University Elective
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 2 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 202 HIGHER MATHEMATICS
Internal
Marks
40
External
Marks
60
Total
Marks
100
Credits
L
T
P
3
3
-
-
UNIT I PROBABILITY: Basics of probability, Independence and conditioning on events using Bayes rule,
Random variables, Discrete random variables: Bernoulli, Geometric, probability mass functions,
conditioning on random variables. Summary statistics: Expectations, variances, moment generating
functions, Continuous random variables: Uniform, Exponential, Gaussian, probability density functions,
jointly continuous random variables, conditioning on continuous or discrete random variables.
UNIT II SAMPLING THEORY: Sampling Theory, Random samples, Sampling with and without
replacement, Sampling distributions, Sampling distributions of means, Sampling distributions of
proportions, Sampling distributions of Differences and Sums, Standard Errors.
UNIT III STATISTICAL TECHNIQUES: Regression and correlation –Rank correlation– Partial and
multiple correlation- multiple regression, Analysis of correlation and covariance structures, including
principal components, factor analysis and canonical correlation, Classification and discrimination
techniques, Multivariate inference.
UNIT IV COMBINATORICS: Basics of Counting- The Pigeonhole Principle -Permutations and
Combinations -Binomial Coefficients -Generalized Permutations and Combinations -Generating
Permutations and Combinations, Recurrence relations.
UNIT V GRAPH THEORY: Introduction, Directed Graphs, Paths and circuits, Trees and fundamental
circuits, Cut sets and cut vertices, Matrix representations of Graphs, Incidence matrix – sub matrices –
circuit matrix – path matrix – adjacency matrix. Graph Theoretic Algorithms: Connectedness and
components – spanning tree – fundamental circuits – cut vertices – directed circuits – shortest path
algorithm .
Suggested Readings/ Books:
·
·
·
·
·
·
Ronald E. Walpole , Raymond H. Myers, Sharon L. Myers, Keying E. Ye , Probability and Statistics
for Engineers and Scientists, Pearson, 9th edition (2011)
Kenneth H. Rosen, Discrete Mathematics and its Applications, Random House (1988)
Narasing Deo, Graph theory with application to Engineering and Computer Science, Prentice Hall
India ( 2010 )
R.A.Johnson, Miller & Freund’s Probability and Statistics for Engineers, seventh edition, Pearson
Education, Delhi (2008).
J.P. Trembley and R.Manohar, “Discrete Mathematical Structures with Applications to Computer
Science”, Tata McGraw Hill – 13th reprint, 2001.
E.M.Reingold, J.Nievergelt, N.Deo, Combinatorial Algorithms: Theory And Practice, Prentice Hall,
N.J (1977)
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 3 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSE 203 UNIVERSITY ELECTIVE
Internal
Marks
40
External
Marks
60
Total
Marks
100
Credits
L
T
P
2
2
-
-
UNIT I ALGORITHMS COMPLEXITY AND ANALYSIS: Probabilistic Analysis, Amortized Analysis,
Competitive Analysis,Internal and External Sorting algorithms: Quick Sort, Heap Sort, Merge Sort,
Counting Sort, Bin Sort, Multi-way merge sort, Polyphase sorting, Search: Hashing.
UNIT II ADVANCED DATA STRUCTURES: AVL Trees, Red-Black Trees, Splay Trees, B-trees,
Fibonacci heaps, Data Structures for Disjoint Sets, Augmented Data Structures.
UNIT III GRAPHS & ALGORITHMS: Representation, Type of Graphs, Paths and Circuits: Euler
Graphs, Hamiltonian Paths & Circuits; Cut-sets, Connectivity and Separability, Planar Graphs,
Isomorphism, Graph Coloring, Covering and Partitioning, , Depth- and breadth-first traversals, Minimum
Spanning Tree: Prim’s and Kruskal’s algorithms, Shortest-path Algorithms: Dijkstra’s and Floyd’s
algorithm, Topological sort, Max flow: Ford-Fulkerson algorithm, max flow – min cut.
UNIT IV STRING MATCHING ALGORITHMS: Suffix arrays, Suffix trees, Rabin-Karp, Knuth-MorrisPratt, Boyer- Moore algorithm.
Suggested Readings/ Books:
·
Thomas Coremen, “Introduction to Algorithms”, Third edition, Prentice Hall of India (2009).
·
Kleinberg J., Tardos E., “Algorithm Design”, 1st Edition, Pearson, 2012.
·
Motwani R., Raghavan P., “Randomized Algorithms”, Cambridge University Press, 1995.
·
Vazirani, Vijay V., “Approximation Algorithms”, Springer, 2001.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 4 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 204 ADVANCED ALGORITHMIC ANALYSIS
Internal
Marks
40
External
Marks
60
Total
Marks
100
Credits
L
T
P
3
3
-
-
OBJECTIVE: To focus on design and analysis of algorithms in various domains that lays foundations for
designing efficient algorithms.
EXPECTED OUTCOME: On completion of this course the student would be able to
·
·
Apply the algorithms and design techniques to solve problems;
Have a sense of the complexities of various problems in different domains.
UNIT I INTRODUCTION: Overview of algorithmic design, asymptotic notation and its properties, Growth
of Functions, Time complexity and Analysis of algorithms, Recurrence Relations, Amortized analysis.
UNIT II LINEAR PROGRAMMING: Geometry, Farkas' Lemma, Strong Duality, Complexity, Interior-point
Algorithms, Ellipsoid Algorithm and Optimization vs. Separation, Extension to Conic Programming.
UNIT III NETWORK FLOWS: Maximum Flows, Min-cost Flows, Cycle Cancelling Algorithms, Strongly
Polynomial-time Analysis, Minimum Cuts without Flows.
UNIT IV P AND NP CLASSES: Class P, Polynomial time verification, reducibility, NP-Hard, NP
completeness, Cooks theorem, NP-complete problems- Circuitsat, 3Sat-CNF, Clique, vertex-cover and
subset sum.
Unit V Approximation Algorithms: Limits to Approximability, Basic Techniques and Vertex Cover,
Primal-dual Technique, Set cover problem,Multicommodity Cut via Embedding Metric Spaces,
Approximation Scheme for Euclidean TSP.
Suggested Readings/ Books:
· Cormen, Leiserson, Rivest and Stein , “Introduction to Algorithms”, 3rd edition,McGraw-Hill, 2009.
· E. Horowitz, and S. Sahni, “Fundamentals of Computer Algorithms”, 2nd edition , Computer Science
Press, 2008.
· Schrijver, A. “Theory of Linear and Integer Programming” Chichester: John Wiley & Sons, 1998.
· Roos, C., T. Terlaky, and J. -Ph. Vial. “Theory and Algorithms for Linear Optimization: An Interior
Point Approach” Chichester: John Wiley & Sons, 1997.
· Vazirani, V. “Approximation Algorithms” Berlin: Springer-Verlag, 2001.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 5 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSE 208 DISTRIBUTED OPERATING SYSTEMS
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: To provide the fundamentals for the distributed operating systems that serve foundation for
the advanced studies in the area of distributed systems.
EXPECTED OUTCOME: On completion of this course the student would be able to deal distributed
operating systems, global clocks, interprocess communication, file and memory management.
UNIT I INTRODUCTION: Fundamental issues in distributed systems, Distributed System Models and
Architectures, Classification of Failures in Distributed Systems, Basic Techniques for Handling Faults in
Distributed Systems.
UNIT II TIME AND GLOBAL STATES: Logical clocks and physical clocks, events, process states, global
states; Distributed Mutual Exclusion, Leader Election, Distributed Deadlock Detection, Remote Procedure
Calls, Broadcast Protocols.
UNIT III INTER PROCESS COMMUNICATION: Inter Process Communication and Process
Synchronization – Inter Process Communication Ports – Implementation of Port – Port Table Initialization
– Port Creation – Sending a Message to Port – Receiving a message from Port – Port Deletion and
Reset. Process Synchronization – Classified Synchronized problem – Synchronization solution – Dead
lock prevention – Avoidance.
UNIT IV MEMORY AND FILE MANAGEMENT: Memory Management - Introduction – Partitioned Space
Allocation – Buffer Pools - Allocation a Buffer – Return a Buffer – Creating a Buffer Pool – Initializing the
Buffer Pool Table – Virtual Memory and Memory multiplying Hardware for Demand Paging – Address
Translation with a Page Table – Metadata in Page Table entry – Page replacement and Global Clock.
File Management - Operating Systems – Internal and File Management – The Intel Architecture – MSDOS internal – Windows XP – Internals –UNIX and UNIX internals.
UNIT V DISTRIBUTED OPERATING SYSTEMS: Distributed operating system concept – Architectures
of Distributed Systems, Distributed Mutual Exclusion, Distributed Deadlock detection, Agreement
protocols, Threads, processor Allocation, Allocation algorithms , Distributed File system design; Real
Time Operating Systems: Introduction to Real Time Operating Systems, Concepts of scheduling , Real
time Memory Management.
Suggested Readings/ Books:
· Davis, Davis William S, “Operating Systems: A Systematic View”, 6th edition, Pearson Education
India, 2007.
· Douglas Comer, “Operating System Design: The Xinu Approach, Linksys Version”,2nd edition, CRC
Press, 2011.
· Ann McIver McHoes, Ida M. Flynn, “Understanding Operating Systems”, 6th Edition, Cengage
Learning, 2010.
· Randy Chow and Theodore Johnson, “Distributed Operating Systems and Algorithms”, AddisonWesley, 1997.
· G. Coulouris, J. Dollimore, and and T. Kindberg, “Distributed Systems: Concepts and Designs”, 5th
edition, Addison Wesley, 2011.
· Mukesh Singhal, and N. G. Shivaratri, “Advanced Concepts in Operating Systems, Distributed,
Database, and Multiprocessor Operating Systems”, 1st edition, McGraw Hill, 1994.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 6 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 209 ADVANCED COMPUTER NETWORKS
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: To go beyond the basic level of understanding that is typically offered at an undergraduate
networking course.
EXPECTED OUTCOME: On completion of course students will be able to understand the fundamental
concepts in routing and addressing, transport protocols and congestion control, emerging distributed
applications, and wireless networking.
UNIT I NETWORKING STANDARDS AND SPECIFICATION: Networking standards and specifications,
Need for standardization, ISO and the IEEE standards, The IEEE 802 Project
UNIT II OVERVIEW OF OSI AND TCP/IP PROTOCOL SUITE: Layers in the OSI model, TCP/IP protocol
suite, Physical layer addressing, Network layer addressing, Client-Server model.
UNIT III ADDRESSING AND ROUTING: IP Addresses: Classful addressing, Subnetting/Supernetting,
Classless Addressing, Delivery and routing of IP packets, Interior and Exterior routing.
UNIT IV TCP/IP PROTOCOL SUITE: Socket Interface, Internet Protocol (IP), ICMP and ARP, Transport
Layer Protocols -TCP and UDP, Congestion control and Quality of Service, File Transfer protocols - FTP
and TFTP, SMTP, SNMP, BOOTP and DHCP, Domain Name System, Mobile IP. Routing protocols RIP, OSPF and BGP.
UNIT V AD HOC WIRELESS NETWORKS: Cellular and Ad hoc wireless networks, Applications of Ad
hoc wireless networks, issues in ad hoc wireless networks, issues in designing a routing protocol for ad
hoc wireless networks, Classification of routing protocols, Security in ad hoc wireless networks.
Suggested Readings/ Books:
· Behrouz A. Forouzan, “TCP/IP Protocol Suite”, 4th edition, Tata McGraw-Hill, 2010.
· W. Richard Stevens, “TCP/IP Illustrated, The Protocols”, 2nd edition, Pearson Education, 2011.
· C.Siva Ram Murthy, B.S. Manoj, “Ad hoc Networks-Architectures and protocols”, 3rd edition,
Pearson Education, 2007.
· Andrew S. Tenenbaum, “Computer Networks” 4th edition, Prentice Hall, 2011.
· D. E. Comer, “Internetworking with TCP/IP Principles, Protocols and Architecture”, Volume - I,
Pearson Education, 2009.
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Page 7 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 205 ADVANCED COMPUTER ARCHITECTURE
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: To focus towards the various design options in the area of architecture that lays platform to
develop and analyze high performance applications.
EXPECTED OUTCOME: On completion of this course the student would be able to
· Identify the need for multi-core architecture for specific applications by developing a suitable
complexity measure.
· Identify needs for homogeneous or heterogeneous multi-core architectures for a given
application.
· Develop methods to partition a given application program to run on a multi-core processor
· Use the Intel multi-core architecture for develop high performance code
· Optimize code using appropriate techniques.
UNIT I CONTROL UNIT DESIGN: Overview of IAS Computer, Data path implementation, Register
Transfer Notation (RTN), Abstract RTN, Concrete RTN, Control sequence for Simple RISC computer
(SRC); Control unit Design, Hardwired control unit Design and Micro programmed control unit Design
using control Sequences
UNIT II MEMORY MODULE DESIGN: Conceptual view of memory cell, Memory address map, Memory
connections to CPU, Cache memory- Cache memory management techniques, Types of cache’s : Look
through, look aside, write through , write around, unified Vs Split, multilevel, cache levels, Cache Misses,
performance issues: Mean memory access time, Execution time, Cache Coherence Protocols, Snoopy,
MSI, MESI, and MOESI.
UNIT III MULTI-CORE ARCHITECTURE: Parallel computing and why it failed, Multi-processor
architecture and its limitations, Need for multi-core architectures, Architecting with multi-cores,
Homogenous and heterogeneous cores, Shared recourses, shared busses, and optimal resource sharing
strategies. Performance evaluation of multi-core processors, Error management.
UNIT IV MULTITHREADING CONCEPTS: Evolution of Multi-Core Technology, basic concepts of
threading and parallel computing, Concurrency, Parallelism,threading design concepts for developing an
application, Correctness Concepts: Critical Region, Mutual exclusion, Synchronization, Race Conditions,
Performance Concepts: Simple Speedup, Computing Speedup, Efficiency , Granularity , Load Balance,
Tools Foundation – Intel® Compiler and Intel® VTune™ Performance Analyzer.
UNIT V MULTI-CORE PROGRAMMING: Introduction to OpenMP , OpenMP Directives, Parallel
constructs, Work-sharing constructs, Data environment constructs, Synchronization constructs, Extensive
API library for finer control, benchmarking multi-core architecture: Bench marking of processors.
Comparison of processor performance for specific application domains.
Suggested Readings/ Books:
· John L. Hennessy and David A. Patterson “Quantative Approach –Computer Architecture” 5th
edition, Morgan Kaufmann, 2011.
· Shameem Akhter and Jason Roberts, “Multi-Core Programming”, 1st edition, Intel Press, 2006.
· Vincent .P. Heuring, Harry F. Jordan “ Computer System design and Architecture” 2nd edition,
Pearson, 2003.
· David B. Kirk , Wen-mei W. Hwu, “Programming Massively Parallel Processors: A Hands-on
Approach (Applications of GPU Computing Series)”, 1st edition, Morgan Kaufmann, 2010.
· Apman, Gabriele Jost, Ruud van van der Pas, “Using OpenMP: Portable Shared Memory
Parallel Programming (Scientific and Engineering Computation)”, 1st edition, MIT Press, 2007.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 8 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 206 ADVANCED DATABASE SYSTEMS
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: To expose the students to the latest industry relevant topics in modern database
management systems.
EXPECTED OUTCOME: To enable the students to design their own parallel and distributed databases
and to expose to the various warehousing tools.
UNIT I DATABASE DESIGN AND TUNING: Introduction to physical database design – Guideline for
index selection- Overview of database tuning – Conceptual schema tuning – Queries and view tuning.
UNIT II PARALLEL AND DISTRIBUTED DATABASE: Parallel database systems: Architecture of
parallel databases, parallel Query evaluation, parallelizing joins and parallel - query optimization.
Distributed database systems: Distributed database architecture, Properties of distributed database,
Types
UNIT III DEDUCTIVE DATABASES: Introduction, Prolog/datalog notation, Interpretation of rules, Basic
inference mechanisms for logic programs, Datalog programs and their evaluation, deductive database
system, deductive object oriented databases, applications.
UNIT IV DATA WAREHOUSING: Data warehousing: Characteristics of Data warehouse, Data
preprocessing, Data warehouse architecture, Multi dimensional data model, Schema design, OLAP
Operation and Data mart, Concepts of Data mining.
UNIT V DATABASE TECHNOLOGIES: Object Database Systems, Multimedia databases, Mobile
databases, Spatial Database, Temporal database, Data bases on the World Wide Web, Geographic
Information system, Genome data management, Digital Libraries.
Suggested Readings/ Books:
· Raghu Ramakrishnan and Johannes Gehrke, “Database Management Systems”, 3rd Edition,
McGraw Hill,2007.
· S.K.Singh, “Database Systems: Concepts, Design & Applications”, 1st edition, Prentice Hall, 2009.
· Ramez Elmasri and B.Navathe, “Fundamentals of database systems”, 4th edition, Addison Wesley,
2008.
· Jiawei Han and Micheline Kamber, “Data Mining-Concepts and Techniques”, 2nd edition, Morgan
Kaufmann publishers, 2011.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 9 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSE 207 WEB SERVICES
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: To provide fundamentals on SOA, SOAP UDDI and XML that lays foundations for the
advanced studies in the area of web services.
EXPECTED OUTCOME: After completion of this course the students will be perform project in the area
of XML.
UNIT I SOA: (SERVICE ORIENTED ARCHITECTURE): Introduction to Services - Bind, Pubish, Find Framework for SOA – Web Services Architecture, Interoperability – RESTful (Representational State
Transfer) Services, WS-Interoperability.
UNIT II XML & WEB SERVICE STANDARDS: Basics of XML –XML standards - SOAP - Messaging,
Encoding, Faults, Data types, WS-Routing, WSDL Specification - UDDI Business Registry - UDDI data
Models, Types, Inquiry and Publisher APIs.
UNIT III FROM WEB SERVICES TO SEMANTIC WEB SERVICES: Introduction to semantic web
services -Resource Description Framework:RDF - Basic elements, Classes and Properties - RDF query,
RDF tools, RDF-Semantics.
UNIT IV ONTOLOGY BASICS, WEB ONTOLOGY LANGUAGE: OWL, sub languages- OWL: Lite, DL,
Full. Instance, Classes, Properties, DataType Properties, Object Properties, Operators - OWL-S: An
upper ontology to describe web services, Building blocks, Validating OWL- S documents.
UNIT V REAL WORLD EXAMPLES & APPLICATIONS: Protégé-OWL, Case Study, Swoogle,
Architecture and usage of meta-data, FOAF(Friend Of A Friend), Semantic markup, RSS, feeds,
semantic web search engines, Web Crawler, mashups with Examples.
Suggested Readings/ Books:
· Sanjiva Weerawarana, Francisco Curbera, Frank Leymann, Tony Storey, Donals F. Ferguson, “Web
Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable
Messaging and More”, 2nd edition, Prentice Hall PRT, 2005.
· Liyang Yu, “Introduction to the Semantic Web and Semantic Web Services”, 1st edition, Chapman &
Hall/CRC, 2007.
· John Hebeler, Matthew Fisher, Ryan Blace, Andrew Perez-Lopez, Mike Dean, “Semantic web
programming", 3rd edition, Wiley Publishing Inc, 2009.
· Grigoris Antoniou and Frank van Harmelen, “A Semantic Web Primer”, 2nd edition, MIT Press, 2008.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 10 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSE-210 ADVANCED ENGLISH
Internal
Marks
40
External
Marks
60
Total
Marks
100
Credits
L
T
P
2
2
-
-
UNIT I A BRIEF ORIENTATION ON THE THEORY OF BUSINESS COMMUNICATION:
Definition of Communication; its nature & process; forms & channels of communication; profile
of an Effective Communicator
BUILDING UP AND ENRICHMENT OF VOCABULARY: Learning Derivatives, Prefixes and
Suffixes; Homonyms & Homophones; Pairs/Group of words; Synonyms & Antonyms; One word
substitution; Foreign words & Phrases.
BASIC SENTENCE FAULTS: Revising and practicing a prescribed set of grammar items; parts
of speech, compound & complex sentence constructions, active/ passive, direct/indirect
speech, using grammar actively while processing or producing language.
UNIT II APPLICATION OF BUSINESS COMMUNICATION:
a) SPEAKING:
Oral communication- Everyday Interactions, Group Discussions, Public speaking;
Conversation Skills; Business Etiquette; Presentation Skills-combating stage fright,
preparing power point presentation Non-verbal communication in Oral & Power Point
Presentations; Telephonic Skills; Preparation for job interview- practice through mock
interview.
b) MECHANICS OF WRITING:
Descriptive and argumentative essays, Writing business letters, emails; memos,
Drafting Reports-training reports, project reports, varied business reports; Scientific &
Technical Writing-writing abstracts & summaries, research papers; Career Documents;
Preparing a selling resume, covering letters, CVs etc.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 11 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 211 FUNDAMENTALS OF BIG DATA ANALYTICS
Internal
Marks
40
External
Marks
60
Total
Marks
100
Credits
L
T
P
3
3
-
-
OBJECTIVES: This course provides a broad introduction to big data at a foundation level .It also
highlights the state of the practice of data analytics and its Lifecycle to address business challenges that
leverage big data. It provides a brief introduction of big data technology and tools, including MapReduce
and Hadoop.
EXPECTED OUTCOME: The students will be able to huge volumes of data untapped by the BI
programs. They come to know about the Analytics Life Cycle. They get knowledge of open source
software framework that supports the processing of large data sets.
UNIT I INTRODUCTION: Big Data Overview - State of the practice in analytics - The role of the Data
Scientist - Big Data Analytics in Industry Verticals.
UNIT II ANALYTICS LIFECYCLE: Key roles for a successful analytic project - Main phases of the
lifecycle - Developing core deliverables for stakeholders.
UNIT III BIG DATA – TECHNOLOGY AND TOOLS: Introduction to MapReduce/Hadoop for analyzing
unstructured data - Hadoop ecosystem of tools - In-database Analytics - MADlib and Advanced SQL
Techniques, NoSQL, MDX.
UNIT IV ANALYTICS AND STATISTICAL MODELING FOR BIG DATA – THEORY AND METHODS:
Case Study: Big data analytics using -Naïve Bayesian Classifier - K-Means Clustering - Association
Rules Decision Trees -Linear and Logistic Regression -Time Series Analysis -Text Analytics.
UNIT V INTRODUCTION TO R: Introduction to R - Analyzing and exploring data with R - Statistics for
model building and evaluation.
Suggested Readings/ Books:
· Noreen Burlingame ,”Little Book of Big Data” Ed. 2012
· Tom White, “Hadoop , the definitive guide”, O'Reilly Media
· Alex Holmes, “Hadoop in practice”, Manning Publications
·
Donald Miner, “Map Reduce Design Patterns: Building Effective Algorithms and Analytics for
Hadoop and Other Systems,
· Nathan Marz , “Big Data: Principles and best practices of scalable real-time data systems”, Manning
Publications
· Big Data Now: Current Perspectives, O’Reilly Radar [kindle Edition]
· Paul Zikopoulos et al., “Harness the Power of Big Data The IBM Big Data Platform”
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 12 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSB 212 FUNDAMENTALS OF CLOUD COMPUTING
Internal
Marks
40
External
Marks
60
Total
Marks
100
Credits
L
T
P
3
3
-
-
UNIT I INTRODUCTION: Cloud Computing Overview – Characteristics - challenges, Benefits, limitations
– Evolution of IT to Cloud Computing - Cloud computing architecture - Cloud deployment model: Public
clouds – Private clouds – Community clouds - Hybrid clouds - Cloud Reference Model – Business Case Evaluating cloud business impact and economics – Cloud based Solutions for Business applications.
UNIT II SOFTWARE AS A SERVICE (SaaS): Introduction to SaaS – Evolution of SaaS - characteristics Benefits – Lifecycle – Business model – Application architecture – Multitenant – Different levels of
Multitenancy - Evaluating SaaS – Application scalability – SaaS Integration Services – SaaS Integration
Products & Platform - Case study – Deploying open-source SaaS application.
UNIT III PLATFORM AS A SERVICE (PaaS): Introduction to PaaS – Characteristics – Benefits –
Disadvantages - Types – Service model - Cloud platform & Management Computation – Integrated
lifecycle platform – Web application framework - Enabling Technologies as Platform - Emerging Cloud
Computing Trends and Innovations – Case study – Deploying application in PaaS engine.
UNIT IV INFRASTRUCTURE AS A SERVICE (IaaS): Introduction to IaaS - Virtualization – Server –
Storage – Network – data storage – Local Cloud and Thin Clients - Load balancing – Improving
performance through Load balancing - scalability – Managing cloud resource – cloud capacity
management – Virtual machine provisioning – Migration service - Case study – Deploying application in
IaaS engine.
UNIT V CLOUD SERVICE PROVIDERS: SaaS service providers: Google Docs - Salesforce.com –
iCloud - open source software, and commercial cloud service providers. PaaS service providers: Google
App Engine - Microsoft Azure – Force.com – Citrix. IaaS service providers: Amazon EC2 - GoGrid –
Rackspace – Terremark.
Suggested Readings/ Books:
· John Rhoton , “ Cloud Computing Explained : Implementation Handbook for Enterprises”,
· Barrie Sosinsky, “Cloud Computing Bible”, Wiley publication.
· Kris Jamsa, “Cloud Computing”, Jones & Barlett Learning.
· Jothy Rosenberg, Arthur Mateos, “The Cloud At your Service”, Manning Publication, 2011.
· Anthony Velte, Toby Velte, “Cloud Computing: A Practical Approach”, McGraw Hill.
· Rajkumar Buyya, Cloud Computing Principles and Paradigm, Wiley publication
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 13 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSE 215 INFORMATION RETRIEVAL AND DATA MINING
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: The course is aimed at an entry level study of information retrieval and data mining
techniques. It is about how to find relevant information and subsequently extract meaningful patterns out
of it. While the basic theories and mathematical models of information retrieval and data mining are
covered, the course is primarily focused on practical algorithms of textual document indexing, relevance
ranking, web usage mining, text analytics, as well as their performance evaluations.
EXPECTED OUTCOME: On completion of this course students are expected to master both the
theoretical and practical aspects of information retrieval and data mining. More specifically, the student
will understand:
1. The basic concepts and processes of information retrieval systems and data mining techniques.
2. The common algorithms and techniques for information retrieval
3. The quantitative evaluation methods for the IR systems and data mining techniques.
4. The popular probabilistic retrieval methods and ranking principle.
5. The techniques and algorithms existing in practical retrieval and data mining systems such as
those in web search engines and the Amazon book/ Last.FM recommender systems.
6. The challenges and existing techniques for the emerging topics of MapReduce, portfolio retrieval
and online advertising.
UNIT I OVERVIEW OF INFORMATION RETRIEVAL AND DATA MINING : Introduction to information
retrieval and Data Mining – Data Mining Functionalities, Steps in Data Mining Process – Architecture of a
Typical Data Mining Systems . Understand the conceptual models of an information retrieval and
knowledge discovery system. Indexing techniques for textual information items- inverted indices,
tokenization, stemming and stop words.
UNIT II MINING ASSOCIATION RULES: Mining Association Rules in Large Databases, Mining Frequent
Patterns - basic concepts - Efficient and scalable frequent item set mining methods, Apriori algorithm,
FP-Growth algorithm, Associations - mining various kinds of association rules.
UNIT III PREDICTIVE MODELING AND CLUSTERING: Classification and Prediction-Issues
Classification by Decision Tree Induction–Bayesian Classification – Other Classification Methods –
Prediction–Clusters Analysis – Basics of cluster analysis -Types of Data in Cluster Analysis –
Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical Methods.
UNIT IV RETRIEVAL METHODS AND EVALUATION: Retrieval models- Boolean, Vector space, Binary
independence, Language modeling. Probability ranking principle. Other commonly-used techniques
include relevance feedback, pseudo relevance feedback, and query expansion. Retrieval Performance
Evaluation measures Average precision, NDCG, etc. "Cranfield paradigm.
UNIT V PERSONALISATION AND EMERGING AREAS: Basic techniques for collaborative filtering and
recommender systems - memory-based approaches, probabilistic latent semantic analysis (PLSA), and
personalized web search- click-through data. Peer-to-peer information retrieval, Learning to Rank
Portfolio retrieval and Risk Management.
Suggested Readings/ Books:
·
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, “Introduction to Information
Retrieval”, Cambridge University Press. 2008.
·
Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Addison-Wesley,
2006
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 14 of 22
I. K. Gujral Punjab Technical University, Kapurthala
·
Ian H. Witten, Alistair Moffat and Timothy C, “Gigabytes”, Morgan Kaufmann, (2nd Ed.) (1999), San
Francisco, California.
·
Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer (2006).
·
Jiawei Han and Micheline Kambers, “Data Mining –Concepts and Techniques”, 2 edition, Morgan
Kaufman Publications, 2011.
David Hand, Heikki Mannila and Prdhraic Smyth, “Principles of Data Mining”, 3rd edition, Morgan
Kaufman Publications, 2009.
nd
M. Kantardzic, “Data Mining: Concepts, Models, Methods, and Algorithms”, 2 edition, Wiley-IEEE
Press, 2011.
·
·
Scheme & Syllabus (CSE) Batch 2015 & Onwards
nd
Page 15 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSBE 216 LARGE-SCALE DATA MANAGEMENT TECHNIQUES(ELECTIVE –II)
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVES: Data Management to handle large-scale data arising from Internet and Enterprise -based
applications.
EXPECTED OUTCOME: Will be able to develop solutions for both building data-intensive scalable
applications over the Internet/Web as well as for large-scale data analytics.
UNIT I DATA MANAGEMENT SOLUTIONS FOR ENTERPRISE APPLICATIONS: Formal Model of
Correctness, the Transaction Model, Database Concurrency Control Protocols, Transaction Failures and
Recovery, and Database Recovery Protocols.
UNIT II DATA MANAGEMENT SOLUTION FOR INTERNET APPLICATIONS: Google's Application
Stack: Chubby Lock Service, Big Table Data Store, and Google File System; Yahoo's key-value store:
PNUTS; Amazon's key-value store: Dynamo; Correctness Semantics of key-value store and its impact on
application development.
UNIT III DATA ANALYSIS PLATFORMS FOR ENTERPRISE DATA ANALYTICS: Online Analytical
Processing, Data Warehouse Architectures, and the Data Cube Model.
UNIT IV LARGE-SCALE DATA ANALYTICS IN THE INTERNET CONTEXT: Governance,
Programming paradigms: PigLatin and Hive, and parallel databases versus MapReduce.
UNIT V APPLICATIONS: MASSIVE DATA SETS: Applications: Billing in the Large, Detecting Fraud in
the Real World, Massive Datasets in Astronomy, Data Management in Environmental Information
Systems, Massive Data Sets issues in Earth Observing, Massive Data Set Issues in Air Pollution
Modelling, Mining Biomolecular Data Using Background Knowledge and Artificial Neural Networks.
Suggested Readings/ Books:
·
·
·
·
·
·
·
Gerhard WEIKUM and Gottfried VOSSEN, “ Transactional Information Systems: Theory and the
practice of concurrency control and recovery”, Morgan Kaufmann Publishers,
Lars George , “HBase: The Definitive Guide”, O'Reilly Media, Inc.
Even Hewitt , “Cassandra: The Definitive Guide”, O'Reilly Media, Inc
Alex Holmes, “Hadoop in practice”, Manning Publications
James Abello, Panos M. Pardalos, Mauricio G.C. Resende, “Handbook of Massive Data Sets”,
Kluwer Academic Publishers.
Alan Gates , “Programming Pig Dataflow Scripting with Hadoop”, O'Reilly Media, Inc.
Donald Miner, Adam Shook, “MapReduce Design Patterns Building Effective Algorithms and
Analytics for Hadoop and Other Systems”, O'Reilly Media, Inc.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 16 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSBE 217 MACHINE LEARNING
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: This course provides a broad introduction to machine learning and statistical pattern
recognition. Supervised learning (generative/discriminative learning, parametric/non-parametric learning,
neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction,
kernel methods); learning theory, reinforcement learning and adaptive control. The course will also
discuss recent applications of machine learning, such as to robotic control, data mining, autonomous
navigation, bioinformatics, speech recognition, and text and web data processing.
EXPECTED OUTCOME: The objective of this course is to give students basic knowledge about the key
algorithms and theory that form the foundation of machine learning and computational intelligence so that
they will be able to understand the principles, advantages, limitations and possible applications of
machine learning Identify and apply the appropriate machine learning technique to classification, pattern
recognition, optimization and decision making.
UNIT I INTRODUCTION: Learning Problems – Perspectives and Issues – Concept Learning – Version
Spaces and Candidate Eliminations – Inductive bias – Decision Tree learning – Representation –
Algorithm – Heuristic Space Search.
UNIT II SUPERVISED LEARNING: Supervised learning setup, LMS.Logistic regression, Perceptron.
Exponential family, Generative learning algorithms. Gaussian discriminant analysis. Support vector
machines, Model selection and feature selection, Ensemble methods: Bagging, boosting, Evaluating and
debugging learning algorithms.
UNIT III UNSUPERVISED LEARNING: Locally weighted Regression – Radial Bases Functions – Case
Based Learning. EM. Mixture of Gaussians. Factor analysis. PCA (Principal components analysis) ICA
(Independent components analysis).
UNIT IV BAYESIAN AND COMPUTATIONAL LEARNING: Bayes Theorem – Concept Learning –
Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs
Algorithm – Naive Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probability Learning –
Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.
UNIT V ADVANCED LEARNING: Learning Sets of Rules – Sequential Covering Algorithm – Learning
Rule Set – First Order Rules – Sets of First Order Rules – Induction on Inverted Deduction – Inverting
Resolution – Analytical Learning – Perfect Domain Theories – Explanation Base Learning – FOCL
Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning
Suggested Readings/ Books:
· Introduction to Machine Learning - Ethem Alpaydin, MIT Press, Oct 2004; Prentice Hall of India, 2005
· Tom Mitchell, Machine Learning, McGraw Hill, 1997.
·
Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer (2006).
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 17 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSBE 219 BIG DATA ANALYTICS
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVE: This course covers tools and techniques required for big data analytics. The course
focuses on concepts, principles, and techniques applicable to any technology environment and industry
and establishes a baseline that can be enhanced by further formal training and additional real-world
experience.
EXPECTED OUTCOME
1. Define learning and knowledge analytics
2. Map the developments of technologies and practices that influence learning and knowledge
analytics as well as developments and trends peripheral to the field.
3. Evaluate prominent analytics methods and tools and determine appropriate contexts where the
methods would be most effective.
4. Describe how “big data” and data-driven decision making differ from traditional decision making
and the potential future implications of this transition.
5. Describe and evaluate developing trends in learning and knowledge analytics and develop
models for their potential impact on teaching, learning, and organizational knowledge.
UNIT I OVERVIEW OF HADOOP: Introduction to learning and knowledge analytics- Rise of “Big Data” Big Data From Technology Perspective- Hadoop- Components of Hadoop-Application Development in
hadoop- The Distributed File System -Hadoop Cluster Architecture-Batch Processing-Low Latency
NoSQL.
UNIT II MAPREDUCE ALGORITHM DESIGN: MapReduce Basics - Functional Programming Roots Mappers and Reducers -The Execution Framework - Partitioners and Combiners- MapReduce Algorithm
Design- Local Aggregation- Pairs and Stripes- Computing Relative Frequencies - Secondary SortingRelational Joins
UNIT III REAL TIME ANALYTICS AND SEARCH : In-line queries-In-memory data, data on HDFS,
HBase or any other structure on Hadoop clusters. Impala with large scale search engine like SolrCloud.
Real-Time Queries in Hadoop
Cloudera Impala: A Modern SQL Engine for Hadoop-scalable parallel database technology available to
the Hadoop community
UNIT IV INDEXING FOR TEXT RETRIEVAL : Inverted Indexing for Text Retrieval- Web CrawlingInverted - Inverted Indexing: Baseline Implementation - Inverted Indexing: Revised Implementation- Index
Compression
UNIT V ANALYTICS FOR BIG DATA IN MOTION : Infosphere Stream Basics- How stream worksStreams Processing Language-Stream Tool Kits
Apache Flume NG - Microsoft StreamInsight as tools for complex event processing (CEP) applications.
Case Studies Big Data in E-Commerce and IT Energy Consumption, Social and Health Science
Suggested Readings/ Books:
· Paul C. Zikopoulos,Chris Eaton,Dirk deRoos,Thomas Deutsch ,George Lapis, “Understanding Big
Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill,2012.
· Lin and Chris Dyer ,”Data-Intensive Text Processing with MapReduce Jimmy “, Morgan & Claypool
Synthesis,2010.
· Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with
Advanced Analytics”, John Wiley& Sons,2012.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 18 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CSBE 220 DATA VISUALIZATION
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
OBJECTIVES: This course covers the basic theories of data visualization, such as data types, chart
types, visual variables, visualization techniques, color theory, cognitive theory, and data patterns
EXPECTED OUTCOME: Understand the principles of creating and evaluating effective data
visualizations
·
·
·
Can use software tools to create various data visualizations
Familiar with the visualization techniques in major application areas
Acquire the skill to apply visualization techniques to a problem and associated data set
UNIT I PRINCIPLES AND THEORIES OF VISUALIZATION: Theories related to visual information
processing - Color theory - Data types - Visual variables -Chart types: statistical graphs, maps, trees and
networks
UNIT II ASPECTS OF DATA PATTERNS: Acquisition of data, Discipline-independent classification of
information sources, Data base issues – In memory database - storage and retrieval of data - Query
languages - Reliability of data – Patterns and predicting data, continuously and discontinuously variable
data, plotting data and suitability for different types of data.
UNIT III VISUALIZATION TECHNIQUES: Scalar and point techniques - Vector visualization techniques Multidimensional techniques – glyphs, Graph-theoretic graphics - Linked Views for Visual Exploration Multivariate Visualization by Density Estimation, Volume Visualization – Rendering - Attribute Mapping Visualizing Cluster Analysis - Visualizing Contingency Tables - Matrix Visualization - Visualization in
Bayesian Data Analysis - Evaluation of data visualization
UNIT IV APPLICATIONS: Visualization for Genetic Network Reconstruction, Reconstruction,
Visualization and Analysis of Medical Images, Exploratory Graphics of a Financial Dataset, Visualization
Tools for Insurance Risk Processes, Visualization of Social Networks datasets, Visualizing Darwin’s
database – A case study.
UNIT V TOOLS AND LANGUAGES: Programming Statistical Data Visualization in the Java Language,
Web-Based Statistical Graphics using XML Technologies, Google Map API, Google Chart, Tableau Heat Map Generation
Suggested Readings/ Books:
· Ben Fry, Visualizing Data: Exploring and Explaining Data with Processing Environment, O'Reilly
Media, 2008
· C.H. Chen, W.K. Hardle, A.Unwin,Handbook of Data Visualization, Springer, Ed(XIV), 2008
· Avril Coghlan, A Little Book of R For Multivariate Analysis, 2013
· Avril Coghlan, A Little Book of R For Biomedical Statistics, 2013
· Paul Murrell, R Graphics, Computer Science and Data Analysis Series
· John Verzani, simpler – Using R for Introductory statistics
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 19 of 22
I. K. Gujral Punjab Technical University, Kapurthala
CBSE 2018 LINUX PROGRAMMING (ELECTIVE –I)
Internal
Marks
50
External
Marks
50
Total
Marks
100
Credits
L
T
P
4
3
-
2
UNIT I INTRODUCTION TO LINUX OPERATING SYSTEM: Introduction and Types of Operating
Systems, Linux Operating System, Features, Architecture Of Linux OS and Shell Interface, Linux
System Calls, Linux Shared Memory Management, Device and Disk Management in Linux, Swap
space and its management. File System and Directory Structure in Linux. Multi-Processing, load sharing
and Multi-Threading in Linux, Types of Users in Linux, Capabilities of Super Users and equivalents.
UNIT II INSTALLING LINUX AS A SERVER: Linux and Linux Distributions ;Major differences
between various Operating Systems (on the basis of: Single Users vs Multiusers vs Network Users;
Separation of the GUI and the Kernel; Domains; Active Directory;).
UNIT III INSTALLING LINUX IN A SERVER CONFIGUARTION : Before Installation; Hardware; Server
Design ;Dual-Booting Issues; Modes of Installation; Installing Fedora Linux; Creating a Boot Disk;
Starting the Installation; GNOME AND KDE : The History of X Windows; The Downside; Enter
GNOME; About GNOME ;Starting X Windows and GNOME; GNOME Basics; The GNOME Configuration
Tool.
UNIT IV INSTALLING SOFTWARE: The Fedora Package Manager; Installing a New Package using
dpkg and RPM; Querying a Package; Uninstalling a Package using dpkg and RPM; Compiling
Software; Getting and Unpacking the Package; Looking for Documentation; Configuring the
Package; Compiling Your Package; Installing the Package, Driver Support for various devices in linux.
UNIT V MANAGING USERS: Home Directories ;Passwords; Shells; Stratup Scripts; Mail; User
Databases; The / etc /passwd File; The / etc / shadow File; The / etc /group File; User
Management Tools; Command-Line User Management; User LinuxConf to Manipulate Users and
Groups; SetUID and SetGID Program
UNIT VI THE COMMAND LINE : An Introduction to BASH, KORN, C, A Shell etc. ; BASH commands:
Job Control; Environment Variables; Pipes; Redirection; Command-Line Shortcuts; Documentation Tools;
The man Command; the text info System; File Listings; Owner ships and permissions; Listing Files; File
and Directory Types; Change Ownership; Change Group; Change Mode ; File Management and
Manipulation; Process Manipulation; Miscellaneous Tools; Various Editors Available like: Vi and its
modes, Pico, Joe and emacs, , Su Command.
UNIT VII BOOTING
AND SHUTTING DOWN: LILO and GRUB; Configuring LILO; Additional
LILO options; Adding a New Kernel to Boot ; Running LILO; The Steps of Booting; Enabling and
disabling Services
FILE SYSTEMS: The Makeup File Systems; Managing File Systems; Adding and Partitioning a
Disk; Network File Systems; Quota Management;
CORE SYSTEM SERVICES: The init Service; The inetd and xinetd Processess; The syslogd Daemon;
The cron Program
PRINTING: The Basic of lpd; Installing LPRng; Configuring /etc/printcap; The /ETC/lpd.perms
File; Clients of lpd, Interfacing Printer through Operating System.
Suggested Readings/ Books:
· Linux Administration : A Beginner's Guide by Steve Shah , Wale Soyinka, ISBN 0072262591 (0-07226259-1), McGraw-Hill Education.
· Unix Shell Programming, Yashavant P. Kanetkar.
· UNIX Concepts and Applications by Sumitabha Das.
· Operating System Concepts 8th edition, by Galvin.
Scheme & Syllabus (CSE) Batch 2015 & Onwards
Page 20 of 22