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Bachelor of Technology in
Computer Science and Engineering
(Big Data and Analytics)
Syllabus for Semester 5 & 6
2 299
INDEX
Sr.
No.
Particulars Page
No. 1 B. Tech CSE (Big Data and Analytics)
1.1 Program Structure of Third Year 3 1.2 Detailed Syllabus of Third Year 5
TEACHING SCHEME FOR B.TECH SEMESTER - V [CSE BDA] Effective from Academic year 2016-17
Subject
Code
Name of Subject
Teaching Scheme Credit Examination Scheme
Hrs.
L
T
P
Total
L/T
P
Total
Theory Practical
/TW Asse.
Grand
Total Int.
Asse.
Sem
End Hrs. Total
2CSE501 Software Engineering 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE502 Computer Networks 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE503 Algorithm Analysis & Design 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE50Ex Elective - I 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE50Ex Elective – II 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE50Ex Elective – III 3 0 2 5 3 1 4 40 60 3 100 50 100
TOTAL 18 0 12 30 18 6 24
Elective - I Elective – II Elective – III
2CSE50E13 Business Intelligence 2CSE50E15 Data Science & Analytics 2CSE50E17 Cloud Computing Essentials
2CSE50E14 Pattern Recognition 2CSE50E16 Soft Computing 2CSE50E18 Information Retrieval
TEACHING SCHEME FOR B.TECH SEMESTER - VI [CSE BDA] Effective from Academic year 2016-17
Subject
Code
Name of Subject
Teaching Scheme Credit Examination Scheme
Hrs.
L
T
P
Total
L/T
P
Total
Theory
Practical
/TW Asse.
Grand
Total Int.
Asse.
Sem
End Hrs. Total
2HS601 Entrepreneurship Development 3 0 0 3 3 0 3 40 60 3 100 0 100
2CSE601 Theory of Computation 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE602 Information Security 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE60Ex Elective – IV 3 0 4 7 3 2 5 40 60 3 100 50 150
2CSE60Ex Elective – V 3 0 2 5 3 1 4 40 60 3 100 50 150
2CSE60Ex Elective - VI 3 0 2 5 3 1 4 40 60 3 100 50 150
TOTAL 18 0 12 30 18 6 24
Elective – IV Elective – V Elective – VI
2CSE60E13 Big Data Analytics 2CSE60E15 Data Warehousing & Data
Mining
2CSE60E17 NoSQL Databases
2CSE60E14 Artificial Intelligence 2CSE60E16 Computer Graphics &
Visualization
2CSE60E18 Discrete Mathematics
300 3
4
301
SUMMER – III Duration Credit
Capstone Course 2 Weeks 1 *
* Credits are calculated separately not included in final total
Capstone Course in summer after semester- VI (credits not counted for graduation
requirement. It is a Pass/Fail course)
About Capstone Course
Computer science and Engineering candidate must take at least one course from an approved list of
capstone courses. The purpose of this requirement is to ensure that students have at least one course
that synthesizes and integrates skills and knowledge acquired throughout the CSE undergraduate
curriculum, and which includes a significant design experience, where teamwork and written and oral
communication are a key part of that design experience.
Capstone courses are distinguished by the following characteristics:
Requires synthesis and integration of knowledge and skills acquired across the curriculum to
solve a significant open-ended problem.
Provides a significant design experience in developing a solution, including the examination
of multiple design alternatives, with justification for the final path taken.
Uses teamwork.
Requires significant written, oral and visual deliverables, including a summative report and
presentation.
Includes multiple reflection activities, perhaps repeated periodically, e.g., individual reflective
writing assignments, design or code reviews, group or individual peer reviews. Includes an evaluation or assessment activity to gauge the merit the solution.
5
302
2CSE501: Software Engineering [3 0 2 3 1]
Learning Outcomes:
After successful completion of this course, student will be able to
• Understand various phases of software development lifecycle
• Requirement analysis and development using standard tools and methodologies
Understand and apply the key aspects of software engineering processes for the development of a
software system
• Develop a quality software project
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
Software Product, Software Processes, Study of different process models,
Project Management Concepts, Planning and Scheduling, Team organization
and people management.
4
2 Software Life Cycle Models
Build-and-Fix, Waterfall, Rapid Prototyping, Incremental, Spiral,
Comparison, ISO 9000 – CMM levels – Comparing ISO 9000 and CMM
6
3 Software Requirement and Analysis
Software requirements, extraction and specification, Feasibility Studies,
Requirements Modelling, Rapid Prototyping, OO Paradigms vs. Structured
Paradigm, Object Oriented Analysis ,CASE tools
8
4 Software Design Concepts
Object oriented design, Architectural design. Component level Design, User
Interface Design, Distributed Systems Architecture, Real Time Software
Design, User Interface Design, Pattern Based Design
6
5 Risk Management
Metrics and Measurement,
configuration management,
Reengineering
Estimation for
Maintenance,
software
Reverse
projects, software
Engineering and
6
6 Software Testing
Non-execution & Execution based testing, Automated Static Analysis Unit
testing, integration testing, black box and white box testing, regression
testing, performance testing, object oriented testing
5
7 Verification and validation of Software
Software Inspections and Audit, Automated Analysis, Critical systems
validation Software Quality Assurance, Quality Standards, Quality Planning
and Control, Various Quality models
6
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303
8 Overview of recent trends in Software Engineering, Security Engineering,
Agile Methods, Service Oriented Software Engineering, Aspect Oriented
Software Development
4
Text Books:
1. Software Engineering By Sommerville, Pearson Education9/E, 2010..
2. Software Engineering – A Practitioner‟s Approach By: Roger S Pressman, McGraw-Hill 7/E, 2010.
Reference Books:
1. Pankaj Jalote, Software Engineering – A Precise Approach Wiley
2. Software Engineering Fundamentals by Ali Behhforoz & Frederick Hudson OXFORD
3. Rajib Mall, Fundamentals of software Engineering, Prentice Hall of India.
4. Engineering Software as a Service An Agile Software Approach, Armando Fox and David Patterson
5. John M Nicolas, Project Management for Business, Engineering and Technology, Elsevier
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304
2CSE502: Computer Networks [3 0 2 3 1]
Learning Outcomes:
After successful completion of the course students should be able to
• Analyze the requirements for a given organizational structure and select the most appropriate
• networking architecture and technologies;
• Specify and identify deficiencies in existing protocols, and then go onto formulate new and better
protocols;
• analyze, specify and design the topological and routing strategies for an IP based networking
infrastructure
• Have a working knowledge of datagram and internet socket programming.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction to computer networks and Internet
Understanding of network and Internet, The network edge, The network core,
Understanding of Delay, Loss and Throughput in the packet switching
network, protocols layers and their service model, History of the computer
network
8
2 Application Layer
Principles of computer applications, Web and HTTP, E-mail, DNS, Socket
programming Topics with TCP and UDP
8
3 Transport Layer
Introduction and transport layer services, Multiplexing and Demultiplexing,
Connection less transport (UDP), Principles of reliable data transfer,
Connection oriented transport (TCP), Congestion control
10
4 Network Layer
Introduction, Virtual and Datagram networks, study of router, IP protocol and
addressing in the Internet, Routing algorithms, Broadcast and Multicast
routing
10
5 The Link layer and Local area networks
Introduction and link layer services, error-detection and correction techniques,
Multiple access protocols, addressing, Ethernet, switches
9
Reference Books:
1. Computer Networking- A Top-Down approach, 5th edition, Kurose and Ross, Pearson
2. Computer Networks- A Top-Down approach, Behrouz Forouzan, McGraw Hill
3. Computer Networks (4th edition), Andrew Tanenbaum, Prentice Hall
4. Computer Networking and the Internet (5th edition),Fred Halsall, Addison Wesley
5. Data Communications and Networking (4th edition), Behrouz Forouzan, McGraw Hill
6. TCP/IP Protocol Suite (3rd edition), Behrouz Forouzan, McGraw Hill
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305
2CSE503: Algorithm Analysis & Design [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Analyze the asymptotic performance of algorithms.
Derive and solve recurrences describing the performance of divide-and-conquer algorithms.
Find optimal solution by applying various methods.
Apply pattern matching algorithms to find particular pattern.
Differentiate polynomial and non-polynomial problems.
Explain the major graph algorithms and their analyses. Employ graphs to model engineering
problems, when appropriate.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Elementary Algorithms
Problems & instances, efficiency of algorithms, average & worst case
analyses, elementary operation, reasons for analyzing efficiency
3
2 Asymptotic Notation
Big „oh‟ notation, other asymptotic notation, conditional asymptotic notation,
asymptotic notation with several parameters, operations on asymptotic
notation
4
3 Models of Computation
Random Access Machines, computational complexity of RAM programs, a
stored program model, abstractions of RAM - straight-line programs, Turing
Machines, relationship between Turing Machines and RAM.
4
4 Analysis of Algorithms
Analyzing control structures, barometer instructions, examples of their use,
average-case analysis, amortized analysis
4
5 Solving Recurrences
Intelligent guesswork, homogeneous recurrences, inhomogeneous
recurrences, change of variable, range transformations, asymptotic
recurrences, substitution method, iteration method, recurrence trees, master
method & master theorem
6
6 Divide and Conquer
Characteristics, the general template, applications: binary search, merge sort,
4
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306
quick sort, matrix multiplication, counting inversion
7 Greedy Algorithms
General characteristics of greedy algorithms and examples, applications:
Kruskal‟s and Prim‟s algorithms, shortest path problem, knapsack problem,
scheduling problem
6
8 Dynamic Programming
General characteristics and examples, principle of optimality, applications:
binomial coefficients, making change, knapsack problem, Floyd‟s algorithm,
chained matrix multiplication. Approach using recursion, memory functions
6
9 Graph Algorithms
Depth-first search, breadth-first search, topological ordering & sorting,
backtracking, application of backtracking: knapsack problem. Branch &
bound, application: the assignment problem, general considerations
4
10 Computational Complexity
Introduction, information-theoretic arguments: complexity and sorting,
complexity and algorithmic, introduction to NP completeness, the classes P
and NP, polynomial reductions, NP complete problems
4
Reference Books:
1. Fundamentals of Algorithmics by Brassard & Bratley, Prentice Hall of India
2. Introduction to Algorithms by Cormen, Leiserson, Rivest, Prentice Hall of India 3. Ellis Horowitz, Sartaj Sahni, Fundamentals of computer algorithms, Computer Science Press
10
307
ELECTIVE I
2CSE50E13: Business Intelligence [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Design and implement OLTP, OLAP and Warehouse concepts
Design and develop Data Warehouse using Various Schemas & Dimensional modelling
Use the ETL concepts, tools and techniques to perform Extraction, Transformation, and
Loading of data
Report the usable data by using various reporting concepts, techniques/tools, and use charts,
tables for reporting in BI
Use Analytics concepts like data mining, Exploratory and statistical techniques for predictive
analysis in Business Intelligence
Demonstrate application of concepts in BI
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 IMPORTANT CONCEPTS
Understanding the field of business intelligence in a global world -
Understanding the BI process and choosing –Place and tasks of the study of
private and public intelligence The practice of private and public intelligence:
the choice of means -Strategies of information gathering, The distinction
between intelligence, information and data, Information asymmetry and
competitive advantage
6
2 DIMENSIONAL MODELLING AND DW DESIGN
Star schema, Snow flake schema, and Fact Constellation schema, Grain of
dimensional model, transactions, Recurring Snapshots, Accumulating
Snapshots, Dimensions (SCD types, conformed dimensions)Clickstream
Source Data (Google Analytics as a Clickstream Data Source), Facts
(additive, semi-additive, non-additive), Hierarchy in dimensions, parent child
relationships, Many-Many Dimensional relationship, Multi Valued
Dimensions and Dimension Attributes
8
3 ETL
Data Quality, Data profiling, Data enrichment, data duplication, ETL
Architecture and what is ETL, Extraction concept and Change data capture,
Transformation concept, lookups, time lag, formats, consistency, Loading
concept, Initial and Incremental loading, late arriving facts, What is Staging,
Data marts, Cubes, Scheduling and dependency matrix
8
4 REPORTING
Metadata Layer, Presentation Layer, Data Layer, Use of different layers and
6
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308
overall Reporting architecture, Various report elements such as Charts, Tables, prompts Data aggregation: Table based, Materialized views, Query
rewrite, OLAP, MOLAP, Dashboards, Ad-hoc reports, interactivity in
analysis (drill down, drill up), Security: report level, data level (row,
column),Scheduling
5 ANALYTICS
Analytics concepts and use in Business Intelligence, Exploratory and
statistical techniques:- Cluster analysis, Data visualization, Predictive analysis
:- Regression, Time series, Data Mining :- Hierarchical clustering, Decision
tree Text analytics :- Text mining, In-Memory Analytics and In-DB
Analytics, Case study: Google Analytics
9
6 RECENT TRENDS
Big data like HIVE, PIG and DW appliances like Netezza, Teradata, Smart
Change data capture using log based techniques, Real time BI, Operational
BI, Embedded BI, Agile BI, BI on cloud, BI applications (Case study on BI
tools like: QlikView, Pentaho, Tableau, MyReport, Spotfire, OR any other BI
tool)
8
Lab Work:
Unit 1: Understand the benefits of IBM Cognos Insight, Drag and drop files to import data, Filter
data and discover associations using Explore Points, Perform a Guided Import from a file, Perform a
Guided Import from a relational data source, Refresh data
Unit 2: Analyze data from different perspectives, Insert totals, Calculate data, Explore chart types,
Explore chart options, Determine the optimal chart type to use for your analysis, Add content by
using widgets, Organize your workspace with tabs and action buttons, Improve appearance by
applying themes
Unit 3: Understand data entry colors and fonts, Control appearance and behavior using formatting,
Annotate and calculate data in cells, Send or upload files to other users, Publish a workspace, Export
and print data, End to End Workshop
Text Books:
1. Reema Thareja, “Data Warehouse”, Publisher: Oxford University Press
2. Jiawei Han, Micheline Kamber, Jian Pei “Data Mining: concepts and techniques”, 2nd Edition, Publisher:
Elsevier/Morgan Kaufmann
3. Ralph Kimball, Margy Ross, “The Data Warehouse Toolkit”, 3rd edition, Publisher: Wiley
Reference Books: 1. William Inmon, “Building the Data Warehouse”, Wiley publication 4th edition 2. Efrem G. Mallach, “Decision Support And Data Warehouse Systems”, 1st Edition Publisher: Tata
McGraw-Hill Education,. ISBN-10: 0072899816
3. Efraim Turban, Ramesh Sharda, Dursun Delen, David King, “Business Intelligence”, ISBN-10:
013610066X Publisher: Prentice Hall.ISBN-13: 9780136100669
4. Dorian Pyle, “Business Modeling and Data Mining”, Elsevier Publication MK
12
309
2CSE50E14: Pattern Recognition [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Understands the fundamental pattern recognition and machine learning theories
Able to design and implement certain important pattern recognition techniques
Able to apply the pattern recognition theories to applications of interest.
Distinguish supervised learning methods from the unsupervised ones.
Able to apply supervised learning methods (model-based maximum likelihood, k-nearest
neighbours) to the classifier design.
Able to apply k-means clustering algorithm.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
Paradigms for pattern recognition, Statistical and Syntactic pattern
recognition, Soft and Hard computing schemes for pattern recognition.
Statistical Pattern Recognition- Patterns and classes, Supervised, Semi-
supervised, and Unsupervised classification
4
2 Representation
Vector space representation of patterns and classes, patterns and classes as
strings, Tree-based representations, Frequent itemsets for representing classes
and clusters, Patterns and classes as logical formulas
5
3 Proximity Measures
Dissimilarity measures, metrics, similarity measures, Edit distance, Hausdorff
metric between point sets, Kernel functions, Contextual and conceptual
similarity between points
5
4 Dimensionality Reduction
Feature selection: Branch and bound, Sequential feature selection, Feature
extraction: Fisher's linear discriminant, Principal components as features;
Nearest Neighbor Classifiers- Nearest neighbor classifier, Soft nearest
neighbor classifiers, Efficient algorithms for nearest neighbor classification,
K-nearest neighbor classifier, minimal distance classifier, condensed nearest
neighbor classifier and its modifications
7
5 Bayes Classifier
Bayes classifier, naïve Bayes classifier, Belief net; Decision Trees- Axis-
parallel and oblique decision trees, Learning decision trees, Information gain
and Impurity measures
7
6 Linear Discriminant Functions
Characterization of the decision boundary, Weight vector and bias, Learning
6
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the discriminant function, Perceptrons; Support Vector Machines- Maximizing the margin, Training support vector machines, Kernel functions
7 Clustering
Clustering process, Clustering algorithms, Clustering large datasets
6
8 Combination of Classifiers
AdaBoost for classification, Combination of homogeneous classifiers,
Schemes for combining classifiers
5
Text Books: 1. Susheela D evi and M. Narasimha Murty, Pattern Recognition: An Introduction, Universities Press,
Hyderabad, 2011.
Reference Books: 1. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, John Wiley and Sons, 2000. 2. M. Narasimha Murty and V. Susheela Devi, Pattern Recognition, NPTEL Web Course, 2011
(http://nptel.iitm.ac.in/courses.php?disciplineId=106).
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311
ELECTIVE II
2CSE50E15: Data Science & Analytics [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Learn the fundamentals of data analytics and the data science pipeline
Learn how to scope the resources required for a data science project
Apply statistical methods, regression techniques, and machine learning algorithms to make
sense out of data sets both large and small
Demonstrate knowledge of statistical data analysis techniques utilized in business decision
making.
Apply principles of Data Science to the analysis of business problems.
Use data mining software to solve real-world problems.
Employ cutting edge tools and technologies to analyze Big Data.
Apply algorithms to build machine intelligence.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Descriptive Statistics
Introduction to the course, Descriptive Statistics, Probability Distributions
5
2 Inferential Statistics
Inferential Statistics through hypothesis tests, Permutation & Randomization
Test
5
3 Regression & ANOVA
Regression, ANOVA (Analysis of Variance)
6
4 Machine Learning Introduction and Concepts
Differentiating algorithmic and model based frameworks, Regression:
Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest
Neighbours, Regression & Classification
7
5 Supervised Learning with Regression and Classification techniques
Bias-Variance Dichotomy, Model Validation Approaches, Logistic
Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis,
Regression and Classification Trees, Support Vector Machines, Ensemble
Methods: Random Forest, Neural Networks, Deep learning
7
6 Unsupervised Learning and Challenges for Big Data Analytics
Clustering, Associative Rule Mining, Challenges for big data analytics
5
7 Prescriptive analytics
Creating data for analytics through designed experiments, creating data for
6
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analytics through Active learning, creating data for analytics through Reinforcement learning
8 Visualization
Graph Visualization, Data Summaries, Model Checking & Comparison
4
Reference Books:
1. Hastie, Trevor, et al. The elements of statistical learning. Vol. 2. No. 1. New York: springer, 2009.
2. Montgomery, Douglas C., and George C. Runger. Applied statistics and probability for engineers. John
Wiley & Sons, 2010
3. Bekkerman et al. Scaling up Machine Learning
4. Tom White “Hadoop: The Definitive Guide” Third Edition, O‟reilly Media, 2012.
5. AnandRajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge University Press,
2012.
6. Vincent Granville, Developing Analytic Talent: Becoming a Data Scientist, wiley, 2014.
7. Jeffrey Stanton & Robert De Graaf, Introduction To Data Science, Version 2.0, 2013.
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313
2CSE50E16: Soft Computing [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to:
Identify and describe soft computing techniques and their roles in building intelligent
machines
Recognize the feasibility of applying a soft computing methodology for a particular problem
Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems
Apply genetic algorithms to combinatorial optimization problems
Apply neural networks to pattern classification and regression problems
Effectively use existing software tools to solve real problems using a soft computing
approach
Evaluate and compare solutions by various soft computing approaches for a given problem.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
What is soft computing? Differences between soft computing and hard
computing, Soft Computing constittuents, Methods in soft computing,
Applications of Soft Computing
6
2 Introduction to Genetic Algorithms
Introduction to Genetic Algorithms (GA), Representation, Operators in GA,
Fitness function, population, building block hypothesis and schema theorem.;
Genetic algorithms operators- methods of selection, crossover and mutation,
simple GA(SGA), other types of GA, generation gap, steady state GA,
Applications of GA
8
3 Neural Networks
Concept, biological neural syste,. Evolution of neural network, McCulloch-
Pitts neuron model, activation functions, feedforward networks, feedback
networks, learning rules – Hebbian, Delta, Percepron learning and Windrow-
Hoff, winner-take-all
7
4 Supervised learning
Perceptron learning, single l layer/multilayer perceptron, linear separability,
hidden layers, back popagation algorithm, Radial Basis Function network;
Unsupervised learning - Kohonen, SOM, Counter-propagation, ART,
Reinforcement learning, adaptive resonance architecture, applications of
neural networks to pattern recognition systems such as character recognition,
face recognition, application of neural networks in image processing
8
5 Fuzzy systems
Basic definition and terminology, set-theoretic operations, Fuzzy Sets,
8
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314
Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions, Fuzzy Rules & Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems,
Fuzzy Decision Making; Neuro-fuzzy modeling- Adaptive Neuro-Fuzzy
Inference Systems, Coactive Neuro-Fuzzy Modeling, Classification and
Regression Trees, Data Clustering Algorithms, Rulebase Structure
Identification and Neuro-Fuzzy Control , Applications of neuro-fuzzy
modeling
6 Swarm Intelligence
What is swarm intelligence? Various animal behavior which have been used
as examples, ant colony optimization, swarm intelligence in bees, flocks of
birds, shoals of fish, ant-based routing, particle swarm optimization
8
Text Books: 1. S.N. Shivanandam, Principle of soft computing, Wiley. ISBN13: 9788126527410 (2011) 2. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”, Prentice-Hall of
India, 2003.
3. George J. Klir and Bo Yuan, “Fuzzy Sets and Fuzzy Logic-Theory and Applications”, Prentice Hall, 1995.
4. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and Programming
Techniques”, Pearson Edn., 2003.
Reference Books: 1. Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998. 2. David E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison Wesley,
1997.
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315
ELECTIVE III
2CSE50E17: Cloud Computing Essentials [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Understand the computing paradigm and cloud computing
Understand the architecture of cloud computing
Understand and use the service models and deployments
Work on any real cloud service
Understand the service management and security of cloud
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 INTRODUCTION
Overview of computing paradigms, Recent trends in computing, evolution of
cloud computing, Overview of cloud computing, Cloud computing-Concepts,
properties, characteristics, Role of open standards.
6
2 CLOUD COMPUTING ARCHITECTURE
Cloud computing architecture, Cloud service delivery models (XAAS), Cloud
Deployment models
5
3 INFRASTRUCTURE AS A SERVICE
Introduction, Hypervisors, Resource virtualization, Examples, How to
implement IAAS
7
4 PLATFORM AS A SERVICE
Introduction, Cloud Platform and Management, Examples, How to implement
PAAS
7
5 SOFTWARE AS A SERVICE
Introduction, Web services, Web 2.0, Web OS, Examples, How to implement
SAAS
7
6 SERVICE MANAGEMENT IN CLOUD COMPUTING
Service Orchestration -Cloud computing and Service Management, Service
Level Agreements (SLAs), Billing & Accounting, Comparing scaling
hardware, economics of scaling, managing data. Cloud performance, Existing
project experience
5
7 CLOUD SECURITY
Infrastructure security, Data Security, Storage Identity and Access
Management, Access Control, Trust and Reputation, Authentication in Cloud
4
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computing
8 CASE STUDY ON OPEN SOURCE AND REAL CLOUD SERVICS
Eucalyptus, VMware Cloud, IBM Bluemix, Google Cloud services, Amazon
Web services
4
PRACTICALS:
Practicals will be based on the coverage of the above topics using any real cloud service (IBM
Bluemix, Google cloud service or AWS).
Reference Books:
1. Barrie Sosinsky: "Cloud Computing Bible", Wiley-India, 2010
2. Rajkumar Buyya, James Broberg, Andrzej M. Goscinski: "Cloud Computing: Principles and Paradigms",
Wiley, 2011
3. Nikos Antonopoulos, Lee Gillam: "Cloud Computing: Principles, Systems and Applications", Springer,
2012
4. Ronald L. Krutz, Russell Dean Vines: "Cloud Security: A Comprehensive Guide to Secure Cloud
Computing", Wiley-India, 2010
5. Tim Mather, Subra Kumara swamy, Shahed Latif, Cloud Security and Privacy: An Enterprise Perspective
on Risks and Compliance, O'Reilly Media, 2009.
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317
2CSE50E18: Information Retrieval [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Explain the concepts of indexing, vocabulary, normalization and dictionary in Information
Retrieval
Define a boolean model and a vector space model, and explain the differences between them
Explain the differences between classification and clustering
Discuss the differences between different classification and clustering methods
Choose a suitable classification or clustering method depending on the problem constraints at
hand
Implement classification in a boolean model and a vector space model
Implement a basic clustering method
Give account of a basic spectral method
Evaluate information retrieval algorithms, and give an account of the difficulties of evaluation
Explain the basics of XML and Web search.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
Basics of Information Retrieval and Introduction to Search Engines; Boolean
Retrieval-: Boolean queries, Building simple indexes, Processing Boolean
queries
5
2 Term Vocabulary and Posting Lists
Choosing document units, Selection of terms, Stop word elimination,
Stemming and lemmatization, Skip lists, Positional postings and Phrase
queries; Dictionaries and Tolerant Retrieval: Data structures for dictionaries,
Wildcard queries, Permuterm and K-gram indexes, Spelling correction,
Phonetic correction
6
3 Index Construction
Single pass scheme, Distributed indexing, Map Reduce, Dynamic indexing;
Index Compression - Statistical properties of terms, Zipf's law, Heap's law,
Dictionary compression, Postings file compression, Variable byte codes,
Gamma codes
6
4 Vector Space Model
Parametric and zone indexes, Learning weights, Term frequency and
weighting, Tf-Idf weighting, Vector space model for scoring, variant tf-idf
6
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functions
5 Computing Scores in a Complete Search System
Efficient scoring and ranking, Inexact retrieval, Champion lists, Impact
ordering, Cluster pruning, Tiered indexes, Query term proximity, Vector
space scoring and query operations
6
6 Evaluation in Information Retrieval
Standard test collections, unranked retrieval sets, Ranked retrieval results,
Assessing relevance, User utility, Precision and Recall, Relevance feedback,
Rocchio algorithm, Probabilistic relevance feedback, Evaluation of relevance
feedback
5
7 Probabilistic Information Retrieval
Review of basic probability theory, Probability ranking principle, Binary
independence model, Probability estimates, probabilistic approaches to
relevance feedback. Text Classification- Rocchio classifier, KNearest
neighbor classifier, Linear and nonlinear classifiers, Bias-variance tradeoff,
Naïve Bayes and Support Vector machine based classifiers
6
8 Text Clustering
Clustering in information retrieval, Evaluation of clustering, KMeans and
Hierarchical clustering. Introduction to Linear Algebra, Latent Semantic
Indexing
5
Text Books: 1. C. D. Manning, P. Raghavan, and H. Schutze, An Introduction to Information Retrieval, Cambridge
University Press, 2009.
Reference Books: 1. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Pearson Education, 1999.
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319
2HS601: Entrepreneurship Development [3 0 0 3 0]
Learning Outcomes:
After learning the course the students should be able to
Develop idea generation, creative and innovative skills
Aware of different opportunities and successful growth stories
Learn how to start an enterprise and design business plans those are suitable for funding by
considering all dimensions of business.
Understand entrepreneurial process by way of studying different case studies and find
exceptions to the process model of entrepreneurship.
Run a small enterprise with small capital for a short period and experience the science and
art of doing business.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 INTRODUCTION TO ENTREPRENEURSHIP
Understanding the Meaning of Entrepreneur; Characteristics and Qualities of
an Entrepreneur; Entrepreneurs Vs. Intrapreneurs and Managers;
Classification of Entrepreneurs; Factors Influencing Entrepreneurship;
Entrepreneurial Environment; Entrepreneurial Growth; Problems and
Challenges of Entrepreneurs; Entrepreneurial Scenario in India
8
2 MICRO, SMALL AND MEDIUM ENTERPRISES (MSMES)
MSMEs – Definition and Significance in Indian Economy; MSME Schemes,
Challenges and Difficulties in availing MSME Schemes, Forms of Business;
Women Entrepreneurship; Rural Entrepreneurship; Family Business and First
Generation Entrepreneurs
8
3 IDEA GENERATION AND FEASIBILITY ANALYSIS
Idea Generation; Creativity and Innovation; Identification of Business
Opportunities; Market Entry Strategies; Marketing Feasibility; Financial
Feasibilities; Political Feasibilities; Economic Feasibility; Social and Legal
Feasibilities; Technical Feasibilities; Managerial Feasibility, Location and
Other Utilities Feasibilities
13
4 BUSINESS MODEL AND PLAN IN RESPECTIVE INDUSTRY
Business model – Meaning, designing, analyzing and improvising; Business
Plan – Meaning, Scope and Need; Financial, Marketing, Human Resource and
Production/Service Plan; Business plan Formats; Project report preparation
and presentation; Why some Business Plan fails?
11
5 FINANCING AND HOW TO START UP BUSINESS?
Financial opportunity identification; Banking sources; Non-banking
5
23
320
Institutions and Agencies; Venture Capital – Meaning and Role in Entrepreneurship; Government Schemes for funding business; Pre launch,
Launch and Post launch requirements; Procedure for getting License and
Registration; Challenges and Difficulties in Starting an Enterprise.
Text Books : 1. Jayshree Suresh, “Entrepreneurial Development”, Margham Publishers, Chennai, 2011.
2. Poornima M Charantimath, “Entrepreneurship development small business enterprises”, Pearson, 2013
Reference Books:
1. Raj Shankar, “Entrepreneurship: Theory And Practice”, Vijay Nicole imprints ltd in collaboration with Tata
Mc-graw Hill Publishing Co.ltd.-new Delhi, 2012 2. Robert D. Hisrich, Mathew J. Manimala, Michael P Peters and Dean A. Shepherd, “Entrepreneurship”, 8th
Edition, Tata Mc-graw Hill Publishing Co.ltd.-new Delhi, 2012
3. Martin Roger, “The Design of Business”, Harvard Business Publishing, 2009 4. Roy Rajiv, “Entrepreneurship”, Oxford University Press, 2011
5. Drucker.F, Peter, “Innovation and Entrepreneurship”, Harper business, 2006.
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321
2CSE601: Theory of Computation [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Understand the basic concepts and application of Theory of Computation.
Apply this basic knowledge of Theory of Computation in the computer field to solve
computational problems and in the field of compiler also.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Review of Mathematical Background
Sets, Functions, Logical statements, Proofs, Relations, Languages, The
Principal of Mathematical induction, the strong principle of Mathematical
induction, Recursive definitions, Structural Induction
3
2 Regular Languages and Finite Automata
Regular expressions, Regular languages, Memory required to recognize a
language, Finite automata, Distinguishable strings, Union, intersection and
complement of regular languages.
6
3 Nondeterminism and Kleen’s Theorem
Non-deterministic finite automata, Non deterministic finite automata with ^
transitions, Kleen's theorem
8
4 Regular and Non Regular Language
Minimization of Finite automata, Non-regular and regular languages,
Pumping Lemma, Decision problems and decision algorithms, regular
languages in relation to programming languages
8
5 Context-Free Languages and Push-Down Automata
Context-free languages, Regular Grammars, Derivation tree and ambiguity,
An Unambiguous CFG, Simplified and Normal forms, Chomsky normal form
6
6 Pushdown Automata and CFL
Push -Down Automata, Definition and examples, Deterministic PDA, Types
of acceptances and their equivalence, Equivalence of CFG and PDA,
Introduction to parsing, Top-down and bottomup parsing, Non-CFL and CFL,
Pumping Lemma for CFL, Intersection and Complement of CFL
8
7 Turing Machine
Models of computation, TM definition, Combining TMs, Computing a
function with TMs. Variations on Turing Machines, Doubly infinite and more
than one Tapes, Non-deterministic and Universal TM
6
Reference Books:
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322
1. Introduction to Languages and Theory of Computation: By John C. Martin
2. Computation: Finite and Infinite: By Marvin L. Minsky, Prentice-Hall, 1967
3. Introduction to formal languages: By G. E. Reevsz, Mc-graw hill. 4. Formal language theory: By M. H.
Harrison
26
323
2CSE602: Information Security [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Understand the principles and practices of cryptographic techniques.
Understand a variety of generic security threats and vulnerabilities, and identify & analyze
particular security problems for given application.
Appreciate the application of security techniques and technologies in solving real-life
security problems in practical systems.
Apply appropriate security techniques to solve security problem
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
Security goals, attacks, Security services, security mechanisms
4
2 Cryptographic Mathematics
Modular arithmetic, linear congruence, Algebraic structure, checking of
primeness, primality testing, Chinese remainder theorem, quadratic
congruence
4
3 Classical Ciphers
Symmetric cipher
steganography
model,
substitution
ciphers,
transposition
ciphers,
4
4 Modern symmetric key ciphers
Modern block ciphers, modern stream ciphers, Data Encryption standard,
advanced encryption standard, Electronic code book mode, CBC, cipher
feedback mode, output feedback mode
8
5 Public key cryptography
RSA, RSA proof, RSA attacks, Rabin cryptosystem, Key management: Diffie
Hellman
6
6 Message Authentication and Hash functions
Authentication requirements, functions, Message authentication codes
(MAC), Hash functions, security of Hash functions
4
7 Hash algorithms
SHA- 512
4
8 Digital Signatures
Basics, digital signature standards
6
9 IP Security 5
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324
Reference Books:
1. William Stallings: “Cryptography and Network Security – Principles and Practice”, 4/E, Pearson Education,
2005.
2. Bruce Scheneir: “Applied Cryptography”, 2/E, John Wiley, 1996. 3. Behrouz Forouzan: “Cryptography & Network Security”, 1/E, TMH, 2007.
28
325
ELECTIVE – IV
2CSE60E13: Big Data Analytics [3 0 4 3 2]
Learning Outcomes:
Upon Completion of the course, the students will 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
Conduct big data analytics using system tools
Suggest appropriate solutions to big data analytics problems
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Overview of big data analytics
Introduction to big data, Big data analytics applications
7
2 Technologies and tools for big data analytics
Introduction to MapReduce/Hadoop, Data analytics using
MapReduce/Hadoop, Data visualization techniques, Spark
13
3 Theory and methods for big data analytics
Selected machine learning and data mining methods (such as support vector
machine and logistic regression), Statistical analysis techniques (such as
conjoint analysis and correlation analysis), Time series analysis D. Big data
graph analytics
15
4 Case studies 10
Reference Books:
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.
6. Arvind Sathi, Big Data Analytics: Disruptive Technologies for Changing the Game, MC Press, 2012
29
326
2CSE60E14: Artificial Intelligence [3 0 4 3 2]
Learning Outcomes:
Upon Completion of the course, the students will be able to
apply artificial intelligence techniques, including search heuristics, knowledge representation,
planning and reasoning
describe the key components of the artificial intelligence (AI) field
explain search strategies
solve problems by applying a suitable search method
analyse and apply knowledge representation
describe and list the key aspects of planning in artificial intelligence
analyse and apply probability theorem and Bayesian networks
describe the key aspects of intelligent agents
differentiate the key aspects of evolutionary computation, including genetic algorithms and
genetic programming
describe the key aspects of machine learning
analyse problem specifications and derive appropriate solution techniques for them
design and implement appropriate solutions for search problems and for planning problems
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
What is intelligence? Foundations of artificial intelligence (AI). History of
AI; Problem Solving- Formulating problems, problem types, states and
operators, state space, search strategies
5
2 Informed Search Strategies
Best first search, A* algorithm, heuristic functions, Iterative deepening
A*(IDA), small memory A*(SMA); Game playing - Perfect decision game,
imperfect decision game, evaluation function, alpha-beta pruning
6
3 Reasoning
Representation, Inference, Propositional Logic, predicate logic (first order
logic), logical reasoning, forward chaining, backward chaining; AI languages
and tools - Lisp, Prolog, CLIPS
7
4 Planning
Basic representation of plans, partial order planning, planning in the blocks
world, hierarchical planning, conditional planning, representation of resource
constraints, measures, temporal constraints
6
5 Uncertainty 7
30
327
Basic probability, Bayes rule, Belief networks, Default reasoning, Fuzzy sets and fuzzy logic; Decision making- Utility theory, utility functions, Decision
theoretic expert systems
6 Inductive learning
decision trees, rule based learning, current-best-hypothesis search, least-
commitment search , neural networks, reinforcement learning, genetic
algorithms; Other learning methods - neural networks, reinforcement learning,
genetic algorithms
7
7 Communication
Communication among agents, natural language processing, formal grammar,
parsing, grammar
7
Text Books: 1. Stuart Russell and Peter Norvig. Artificial Intelligence – A Modern Approach, Pearson Education Press,
2001.
2. Kevin Knight, Elaine Rich, B. Nair, Artificial Intelligence, McGraw Hill, 2008.
Reference Books: 1. George F. Luger, Artificial Intelligence, Pearson Education, 2001. 2. Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kauffman, 2002.
31
328
ELECTIVE – V
2CSE60E15: Data Warehousing & Data Mining [3 0 2 3 1]
Learning Outcomes:
Upon Completion of the course, the students will be able to
Store voluminous data for online processing
Preprocess the data for mining applications
Apply the association rules for mining the data
Design and deploy appropriate classification techniques
Cluster the high dimensional data for better organization of the data
Discover the knowledge imbibed in the high dimensional system
Evolve Multidimensional Intelligent model from typical system
Evaluate various mining techniques on complex data objects
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
Introduction to Data Mining, Importance of Data Mining, Data Mining
functionalities, Classification of Data mining systems, Data mining
architecture, Major Issues in Data Mining, Data mining metrics, Applications
of Data Mining, Social impacts of data, Data Mining from a Database
Perspective
6
2 Data Pre-processing
Introduction, Descriptive Data Summarization, Data Cleaning, Data
Integration and Transformation, Data Reduction, Data Discretization.
7
3 Classification and Prediction
Basic issues regarding classification and predication, Classification by
Decision Tree, Bayesian classification, classification by back propagation,
Associative classification, Prediction, Statistical-Based Algorithms, Decision
Tree -Based Algorithms, Neural Network -Based Algorithms, Rule-Based
Algorithms, Other Classification Methods, Combining Techniques, Classifier
Accuracy and Error Measures
9
4 Clustering
Similarity and Distance Measures, Hierarchical Algorithms, Partitioned
Algorithms, Clustering Large Databases, Clustering with Categorical
Attributes
8
5 Association Rules
Basic Algorithms, Advanced Association Rule Techniques, Measuring the
Quality of Rules
8
32
329
6 Applications and other Data mining techniques
Data Mining Applications, Mining Event Sequences,
Mining, Web Mining, The WEKA data mining Workbench
Visual
DM,
Text
7
Text Books:
1. J. Han and M. Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufman, 3/E, 2011.
2. Alex Berson, Stephen J. Smith, "Data Warehousing, Data Mining, and OLAP", MGH, 1998.
33
330
2CSE60E16: Computer Graphics & Visualization [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to
Explain fundamental concepts within computer graphics such as geometrical transformations,
illumination models, removal of hidden surfaces and rendering
Explain the ideas in some fundamental algorithms for computer graphics and to some extent
be able to compare and evaluate them
Explain and apply fundamental principles within interaction programming
Explain and understand fundamental concepts within information visualization and scientific
visualization.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Introduction
History of computer graphics, applications, graphics pipeline, physical and
synthetic images, synthetic camera, modeling, animation, rendering, relation
to computer vision and image processing, review of basic mathematical
objects (points, vectors, matrix methods)
6
2 Introduction to OpenGL
OpenGL architecture, primitives and attributes, simple modeling and
rendering of two- and three-dimensional geometric objects, indexed and RGB
color models, frame buffer, double buffering, GLUT, interaction, events and
callbacks, picking
6
3 Geometric transformations
Homogeneous coordinates, affine transformations (translation, rotation,
scaling, shear), concatenation, matrix stacks and use of model view matrix in
OpenGL for these operations
6
4 Viewing
Classical three dimensional viewing, computer viewing, specifying views,
parallel and perspective projective transformations; Visibility- z-Buffer, BSP
trees, Open-GL culling, hidden-surface algorithms
7
5 Shading
Light sources, illumination model, Gouraud and Phong shading for polygons.
Rasterization- Line segment and polygon clipping, 3D clipping, scan
conversion, polygonal fill, Bresenham's algorithm
7
6 Discrete Techniques
Texture mapping, compositing, textures in OpenGL; Ray Tracing- Recursive
ray tracer, ray-sphere intersection
7
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331
7 Representation and Visualization
Bezier curves and surfaces, B-splines, visualization, interpolation, marching
squares algorithm
6
Text Books: 1. Edward Angel, Interactive Computer Graphics. A Top-Down Approach Using OpenGL (fifth Edition),
Pearson Education, 2008
2. Donald Hearn and Pauline Baker, Computer Graphics with OpenGL (third edition), Prentice Hall, 2003 3.F. S. Hill Jr. and S. M. Kelley, Computer Graphics using OpenGL (third edition), Prentice Hall, 2006
4.Peter Shirley and Steve Marschner, Computer Graphics (first edition), A. K. Peters, 2010
35
332
ELECTIVE – VI
2CSE60E17: NoSQL Databases [3 0 2 3 1]
Learning Outcomes:
The student should know and understand:
Define, compare and use the four types of NoSQL Databases (Document-oriented, KeyValue
Pairs, Column-oriented and Graph).
Demonstrate an understanding of the detailed architecture, define objects, load data, query
data and performance tune Column-oriented NoSQL databases.
Explain the detailed architecture, define objects, load data, query data and performance tune
Document-oriented NoSQL databases.
Demonstrate an understanding of the detailed architecture, define objects, load data, query
data and performance tune Key-Value Pair NoSQL databases.
Explain the detailed architecture, define objects, load data, query data and performance tune
Graph NoSQL databases.
Evaluate NoSQL database development tools and programming languages.
Perform hands-on NoSql database lab assignments that will allow students to use the four NoSQL database types via products such as Cassandra, Hadoop Hbase, MongoDB, Neo4J and
Riak.Contents
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Comparison of relational databases to new NoSQL stores,MongoDB,
Cassandra, Neo4j installation, use and deployment, Replication and sharding,
MapReduce on databases. Introduction, Overview,and History of NoSQL
Databases – The Definition of the Four Types of NoSQL Database,Column-
oriented NoSQLdatabases using Apache HBASE,Column-oriented
NoSQLdatabases using Apache Cassandra, NoSQL Key/Value databases
using MongoDB,NoSQL Key/Valuedatabases using Riak,Graph NoSQL
databases using Neo4,NoSQL databasedevelopment tools and programming
languages
45
Text Books: 1. Sadalage, P. & Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot
Persistence. (1st Ed.). Upper Saddle River, NJ: Pearson Education, Inc. ISBN- 13: 978-0321826626 ISBN-
10: 0321826620
References Books : 1. Redmond, E. & Wilson, J. (2012). Seven Databases in Seven Weeks: A Guide to Modern Databases and
the NoSQL Movement (1st Ed.). Raleigh, NC: The Pragmatic Programmers, LLC. ISBN-13: 978-
1934356920 ISBN-10: 1934356921
36
333
2CSE60E18: Discrete Mathematics [3 0 2 3 1]
Learning Outcomes:
After learning the course the students should be able to:
Construct mathematical arguments using logical connectives and quantifiers.
Verify the correctness of an argument using propositional and predicate logic and truth tables.
Demonstrate the ability to solve problems using counting techniques and combinatorics in the
context of discrete probability.
Solve problems involving recurrence relations and generating functions.
Use graphs and trees as tools to visualize and simplify situations.
Perform operations on discrete structures such as sets, functions, relations, and sequences.
Construct proofs using direct proof, proof by contraposition, proof by contradiction, proof by
cases, and mathematical induction. Apply algorithms and use definitions to solve problems to prove statements in elementary
number theory.
SYLLABUS
Unit
No. Topics
Lectures
(Hours)
1 Sets and propositions
combination, finite, uncountably infinite and infinite sets, mathematical
induction, principles of inclusion and exclusion, propositions
3
2 Permutations, combinations, discrete probabilities
rules of sums and products, permutations, combinations, generation, discrete
probability, conditional probability, information
3
3 Relations and functions
relational model of data bases, properties of binary relations, equivalence
relation, partitions, partial ordering, lattices, chains and antichains, functions
and pigeon-hole principle
4
4 Graphs
Basic terminology, multi- and weighted graphs, paths, circuits, shortest path,
Eulerian path, Travelling Salesman problem, factors of a graph, planar graphs
5
5 Trees
trees, rooted trees, path length, prefix codes, binary search trees, spanning
trees and cut-sets, minimum spanning trees, transport networks
5
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334
6 Finite-state machines
FSM as models of physical systems, equivalent machines, FSM as language
recognizer
4
7 Analysis of algorithms
time complexity of algorithms, example of shortest path algorithm,
complexity, tractable and non-tractable problems
4
8 Computability and Formal languages
Russel's paradox and non-computability, ordered sets, languages, phrase
structured grammars, types of grammars and languages
4
9 Recurrence relations
linear recurrence relations with constant coefficient, homogeneous, particular
and total solutions, generating functions, sorting algorithms, matrix
multiplication
3
10 Discrete numerical functions
manipulations of numerical functions, asymptotic behavior, generating
functions, combinatorial problems
3
11 Group
groups and sub-groups, generators, evaluation of powers, cosets, Lagrange's
theorem, permutation group and Burnsides theorem, group codes,
isomorphism, automorphism, homomorphism, normal subgroups, rings,
integral domains and fields, ring homomorphism, polynomial rings and cyclic
codes
4
12 Lattices and Boolean algebras
Lattices and algebraic systems, principle of duality, properties of algebraic
systems, distributive lattices, boolean algebras, uniqueness, boolean functions
and expressions, propositional calculus
3
Text Books:
1. "Elements of Discrete Mathematics", C.L. Liu, 2nd Ed., McGraw-Hill
Reference Books: 1. "Modern Applied Algebra", Birkoff and Bartee, McGraw-Hill, CBS. 2. "Discrete Mathematics - A Unified Approach", Stephen A. Wiitala, Computer Science Series,McGraw-
Hill.
38
335
Proposed Summary for BDA Course structure semester 1 to 8
Category of
Course
Credits (AS
per AICTE)
Credits (AS per
Course structure)
%
HS 14 9 64.29
BS 31 16 51.61
ES 24 13 54.17
CS 60 83 138.33
CS* 18 35 194.44
OE 9 0 0.00
Project Work 20 20 100.00
MC 9 1 + 5* + 5 **
Total 176+ 9 177 + 10
AICTE MODEL STRUCTURE Project Work HS
11% 8%
OE
5% BS
18%
CS*
10%
ES
14%
CS
34%
OE 0%
BDA COURSE STRUCTURE Project Work HS
11% 5% BS 9%
ES
8% CS* 20%
CS 47%
38
Total Credits requirement – 177 for graduation
10 credits to earn through audit course, social internship, industry internship and
capstone course. Audit Course are to be decide by university time to time. (Credits not
counted for graduation requirement. It is a Pass/Fail course)
Societal Internship in summer after semester – II (credits not counted for graduation
requirement. It is a Pass/Fail course)
Industry Internship in summer after Semester – IV (credits not counted for graduation
requirement. It is a Pass/Fail course )
Capstone Course in summer after semester- VI (credits not counted for graduation
requirement. It is a Pass/Fail course)
Research Project: - One full semester project. This is enabling students to take project in
industry or any other research organization.
