1
Preamble
UNIVERSITY OF MUMBAI
Syllabus for Semester -III and Semester -IV
Program: M.Sc.
Course: Computer Science
(Credit Based Semester and Grading System with
effect from the academic year 2016 –2017)
2
Preamble
This syllabus is an extension of the syllabus for semester - I and semester – II of MSc
Computer Science of University of Mumbai , which came into existence in the academic
year 2015 -2016. As mentioned in th e syllabus of semester I and II , the intended
philosophy of the new syllabus is to meet following guidelines:
Give strong foundation on core Computer Science subjects .
Expose student to emerging trends in a gradual and incremental way .
Prepare student com munity for the demands of ICT industry .
Offer specialization on a chosen area.
Create research temper among students in the whole process .
This syllabus for the semester - III and semester – IV has tried to continue the steps
initiated in the semester - I and semester –II to meet the goals set. This proposes two
core compulsory subjects in semester I II. The student ha s to continue with the tracks
they have taken in the s emester II as elective subjects. The syllabus also includes
project proposal as part of the practical course in elective subjects.
The semester – IV will have one compulsory subject. Student can choose one subject
as specialization out of the two electives he or she ha s been pursuing since the
semester – II. That means, there will be four s pecializations in the semester IV as
mentioned below:
Cloud Computing
Cyber and Information Security
Business Intelligence and Big Data Analytics
Machine Learning
The syllabus also offers an internship and project implementation in the semester – IV,
each of which has weight s equivalent to a full course. By introducing different electives
as tracks in semester –II, espousing more of that tracks in the semester –III and offering
the opportunity to choose the specialization based on the tracks pursed in seme ster –IV
3
will give the student the added advantage of high level competency in the advanced and
emerging areas of computer science. This will definitely equip the student with i ndustry
readiness as internship in an IT or IT-related organization gives a pra ctical exposure to
what is learned and what is practiced. The strong foundation given in the core courses
in different semesters will give enough confidence to the learner to face and adapt to
the changing trends and requirements of industry and academia.
As one can easily notice, the syllabus offers lots of emphasis on student driven
learning and learning through experience . Research is embedded in the course
structure. By introducing Researching Computing in semester – I, Case study in
semester – II, Project Proposal in semester – III and Project I mplementation in semester
– IV (which together has a weightage equivalent to almost two theory courses) , the
syllabus prepares a strong army of budding computer science researchers. The
syllabus designed on the firm believe that by focusing on student driven research on
cutting edge and emerging trends with lots of practical experience will make the lear ning
more interesting and stimulating. It is hoped that the student community and teacher
colleagues wi ll appreciate the thrust, direction and treatment given in the syllabus.
We thank all our colleagues in the University of Mumbai for their inputs, suggestion s
and critical observations. We acknowledge the contributions of experts from premier
institutions and industry for making the syllabus more relevant. We thank the
chairperson and members of the present and previous Adhoc Board of Studies in
Computer Science of University for their constant support. Thanks to one and all who
have directly or indirectly helped in this venture.
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Structure of the syllabus
This i s the syllabus for the semester –III and seme ster–IV of MSc Computer Science
program of University of Mumbai to be implemented from the year 201 6-2017.
Semester -III
The syllabus offers four theory courses and two practical courses in semester -III. Of the
four theory courses, two are compulsory courses. The remaining two are electives.
Each elective course has two tracks (track A and track B for elective I and track C and
track D for elective II). A student is expected to continue with the tra ck they have
chosen in semester -II.
The syllabus pro poses four subjects in semester -III. Each subject has theory and
practical components.
Semester –III: Theory courses
The four theor y courses offered in semester -III are:
(i) Ubiquitous Computing
(ii) Social Network Analysis
(iii) Elective - I
(a) Track A: Cloud Computing – II (Cloud Computi ng Technologies)
(b) Track B: Cyber and Information Security – II (Cyber Forensics)
(iv) Elective – II
(a) Tra ck C: Business Intelligence and Big Data Analytics – II (Mining Massive
Data sets)
(b) Track D: Machine Learning – II (Advanced Machine Learning)
A student is expected to continue with the same track s he or she has taken in
semester -II for elective –I and elective –II. Each of these theory courses (compulsory as
well as elective) is of four credits each and is expected to complete in 60 hours. The
details are shown in the following table.
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Semester III – Theory courses
Course
Code Course
Nomenclature Lecture
In Hours Credits
PSCS 301 Ubiquitous Computing 60 4
PSCS 302 Social Network Analysis 60 4
PSCS 3031 Elective I - Track A: Cloud Computing –II
(Cloud Computing Technologies)
60
4
PSCS 3032 Elective I - Track B: Cyb er and
Information Security - II (Cyber Forensics)
PSCS 3033 Elective II - Track C: Business Intelligence
and Big Data Analytics –II
(Mining Massive Data sets )
60
4
PSCS 3034 Elective II - Track D: Machine Learning –II
(Advanced Machine Learning)
Total Credits for Theory courses in Semester III 16
Semester –III: Practical Laboratory Courses
The syllabus proposes two laboratory courses of 4 credits each. The laboratory
experiments from the first two theory courses (P SCS301 and PSCS302) are combined
together and are proposed as the first practical course (PSCSP 5). Similarly, the
laboratory experiments from the elective courses are combined together and taken as
the second practical course (PSCSP 6). The following table summarizes the details of
the practical courses in the semester –III.
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Semester -III: Practical Laboratory Courses
Course
code Course Title No of
hours Credits
PSCSP 5
Ubiquitous Computing and Social Network
Analysis 60+60=
120 04
PSCSP 6
Elective I and Elective II
60+60=
120 04
Total Credits for Practical Laboratory courses in Semester –III
08
Project Proposal : The syllabus introduces a project proposal in the semester -III under
lab course PSCSP 6. As per this, a student is expected to select a topic for project
based on the specialization he or she is planning to take in the semester -IV. Needless
to say, the project proposal will be based on a topic related to the elective the student
has been pursuing in semester –II and semester -III and intend s to continue in semester -
IV as specialization .
The proposal will contain introduction, related works, objectives and methodology. The
implementation, experimental results and analysis will be part of the Project
implementation in the semester -IV.
Sem ester –IV
The syllabus proposes two subjects in semester -IV, each with theory and practical
components. In addition, there will be internship with industry and a project
implementation. The important feature of the semester -IV is the specialization a stude nt
can choose. A student can choose a specialization based on the electives one has been
pursuing since semester –II. Since there are two electives in semester -III, a student can
drop one and choose the other as the specialization in semester –IV.
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Semester –IV: Theory courses
The two theory courses offered in semester -IV are:
(i) Simulation and Modeling
(ii) Specialization
(a) Track A: Cloud Computing – III (Building Clouds and S ervices)
(b) Track B: Cyber and Information Security –III (Cryptography and Crypt
Analysis)
(c) Track C: Business Intelligence and Big Data Analytics – III (Intelligent Data
Analysis)
(d) Track D: Machine Learning – III (Computational Intelligence)
Each of the se courses (c ore as well as the specialization) is expected to complete in 60
hours. The details are given in the following table.
Semester -IV: Theory courses
Course Code Course
Nomenclature Lecture
In Hours Credits
PSCS 401 Simulation and Modeling 60 4
PSCS 4021 Specialization - Track A: Cloud Computing –III
(Building Clouds and Services)
60
4
PSCS 4022 Specialization - Track B: Cyber and Information
Security - II (Cryptography and Crypt Analysis)
PSCS 4023 Specialization - Track C: Business Intellige nce and
Big Data Analytics –III (Intelligent Data Analysis)
PSCS 4024 Specialization - Track D: Machine Learning –III
(Computational Intelligence)
Total Credits for Theory courses in Semester -IV 08
8
Semester –IV: Practical Laboratory courses
The syllabus proposes one laboratory course of 4 credits. The laboratory experiments
from the two theory courses are combined together and are proposed as the first
practical course (PSCSP 7).
Semester -IV: Practical course
Semester –IV: Internship with industry
The syllabus proposes an internship for about 8 weeks to 12 weeks to be done by a
student. It is expected that a student chooses an I T or IT-related industry and formally
works as a full time intern during the period. The student should subject oneself with a n
internship evaluation with proper documentation of the attendance and the type of work
he or she has done in the chosen organiza tion. Proper certification (as per the
guidelines given in Appendix 1 and 2) by the person, to whom the student was
reporting, with Organization’s seal should be attached as part of the documentation.
Semester –IV: Internship
Course
code Course Title No of
hours Credits
PSCSP 7
Simulation & M odeling and Specialization 60+60=
120 04
Course
code Course Title No of
hours Credits
PSCSP 8 Internship with industry 300 06
9
Semester –IV: Project Implementation
The syllabus proposes project implementation as part of the semester –IV. The project
implementation is continuation of the project proposal the student s has submitted and
evaluated in semester -III. The student is expected to continue with the proposal made
and examined in the semester -III and implement the same in the semester –IV. In
addition , experimental set up, analysis of results, comparison with res ults of related
works, conclusion and future prospects will be part of the project implementation. A
student is expected to make a project implementation report and appear for a project
viva. He or she needs to spend around 200 hours for the project implem entation , which
fetches 6 credits. The details are given below:
Semester –IV: Project Implementation
Course
code Course Title No of
hours Credits
PSCSP 9
Project I mplementation 200 06
10
Detailed syllabus of semester – III
Course Cod e Course Title Credits
PSCS 301 Ubiquitous Computing 04
Unit I: Basics of Ubiquitous Computing
Examples of Ubiquitous Computing Applications, Holistic Framework for UbiCom: Smart
DEI, Modeling the Key Ubiquitous Computing Properties, Ubiquitous System
Environment Interaction, Architectural Design for UbiCom Systems: Smart DEI Model,
Smart Devices and Services, Service Architecture Models, Service Provision Life Cycle.
Unit II: Smart Mobiles, Cards and Device Networks
Smart Mobile Devices, Users, Resource s and Code, Operating Systems for Mobile
Computers and Communicator Devices, Smart Card Devices, Device Networks .
Human –Computer Interaction (HCI): Explicit HCI, Implicit HCI, User Interfaces and
Interaction for Devices, Hidden UI Via Basic Smart Devices, Hidden UI Via Wearable
and Implanted Devices, Human Centered Design (HCD).
Unit III: Smart Environments
Tagging, Sensing and Controlling, Tagging the Physical World, Sensors and Sensor
Networks, Micro Actuation and Sensing: MEMS, Embedded Systems and Rea l Time
Systems, Control Systems.
Unit IV: Ubiquitous Communication
Audio Networks, Data Networks, Wireless Data Networks, Universal and Transparent
Audio, Video and Alphanumeric Data Network Access, Ubiquitous Networks, Network
Design Issues.
Text book:
Ubiquitous Computing Smart Devices, Environments and Interactions, Stefan
Poslad, Wiley, 2009 .
References :
Ubiquitous Computing Fundamentals. John Krumm , Chapman & Hall/CRC 2009.
Ambient in telligence, wireless networking and ubiquitous computing , Vasila kos,
A., & Pedrycz, W. ArtechHouse, Boston , 2006.
http://www.eecs.qmul.ac.uk/~stefan/ubicom .
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Course Code Course Title Credits
PSCS 302 Social Network Analysis 04
Unit I: Introduction to social net work analysis (SNA)
Introduction to networks and relations - analyzing relationships to understand people and
groups, binary and valued relationships, symmetric and asymmetric relationships,
multimode relationships, Using graph theory for social networks an alysis - adjacency
matrices,
edge -lists, adjacency lists, graph traversals and distances, depth -first
traversal, breadth -first traversal paths and walks, Dijkstra’s algorithm, graph distance
and graph diameter,
social networks vs. link analysis, ego -centric and socio -centric
density .
Unit II: Networks, Centrality and centralization in SNA
Understanding networks - density, reachability, connectivity, reciprocity, group -external
and group -internal ties in networks, ego networks, extracting and visualizing ego
networks, structural holes , Centrality - degree of centrality, closeness and betweenness
centrality, local and global centrality, centralization and graph centers, notion of
importance within network, Google pagerank algorithm, Analyzing network structure -
bottom -up approaches using cliques, N -cliques, N -clans, K -plexes, K -cores, F -groups
and top -down approaches using components, blocks and cut -points, lambda sets and
bridges, and factions.
Unit III: Measures of similarity and structural equivalence in SNA
Approaches to network pos itions and social roles - defining equivalence or similarity,
structural equivalence, automorphic equivalence, finding equivalence sets, brute force
and Tabu search, regular equivalence, equivalence of distances: Maxsim, regular
equivalence, Measuring simil arity/dissimilarity - valued relations, Pearson correlations
covariance and cross -products, Understanding clustering - agglomerative and divisive
clusters, Euclidean, Manhattan, and squared distances, binary relations, matches:
exact, Jaccard, Hamming,
Unit IV: Two-mode networks for SNA
Understanding mode networks - Bi-partite data structures, visualizing two -mode data,
quantitative analysis using two -mode Singular value decomposition (SVD) analysis,
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two-mode factor analysis, two -mode correspondence analysis , qualitative analysis using
two-mode core -periphery analysis, two -mode factions analysis, affiliation and attribute
networks.
Text book:
Introduction to Social Network Methods : Robert A. Hanneman , Mark Riddle ,
University of California, 2005 [Published in digital form and available at
http://faculty.ucr.edu/~hanneman/nettext/index.html ].
Social Network Analysis for Startups - Finding connections on the social web:
Maksim Tsvetovat , Alexa nder Kouznetsov , O'Reilly Media, 2011.
Social Network Analysis - 3rd edition , John Scott , SAGE Publications, 2012 .
Reference book:
Exploratory Social Network Analysis with Pajek, Second edition: Wouter de
Nooy, Andrej Mrvar, Vladimir Batagelj, Cambridge University Press, 2011.
Analyzing Social Networks, Stephen P Borgatti, Martin G. Everett, Jeffrey C.
Johnson, SAGE Publications, 2013.
Statistical Analysis of Network Data with R: Eric D. Kolaczyk, Gábor Csárdi,
Springer, 2014.
Network Analysis: Methodol ogical Foundations, (Editors) Ulrik Brandes, Thomas
Erlebach. Springer, 2005.
Models and Methods in Social Network Analysis: (Editors) Peter J. Carrington,
John Scott,Stanley Wasserman, Cambridge University Press, 2005.
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Course Code Course Title Credits
PSCS 3031 Electi ve I- Track A: Cloud Computing -II
(Cloud Computing Technologies) 04
Unit I: Parallel and Distributed Computing
Elements of parallel computing, elements of distributed computing, Technologies for
distrib uted computing: RPC, Distributed object frameworks, Service oriented computing
Virtualization – Characteristics, taxonomy, virtualization and cloud computing.
Unit II: Computing Platforms
Cloud Computing definition and characteristics, Enterprise Computin g, The internet as a
platform, Cloud computing services: SaaS, PaaS, IaaS, Enterprise architecture, Types
of clouds.
Unit III: Cloud Technologies
Cloud computing platforms, Web services, AJAX, mashups, multi -tenant software,
Concurrent computing: Thread programming, High -throughput computing: Task
programming, Data intensive computing: Map -Reduce programming.
Unit IV: Software Architecture
Dev 2.0 platforms, Enterprise software: ERP, SCM, CRM
Custom enterprise applications and Dev 2.0, Cloud application s.
Text book:
Enterprise Cloud Computing Technology, Architectu re, Applications, Gautam
Shroff , Cambridge University Press, 2010
Mastering In Cloud Computing, Rajkumar Buyya, Christian Vecchiola And
Thamari Selvi S, Tata Mcgraw -Hill Education, 2013
Cloud Computing: A Practical Approach, Anthony T Velte, Tata Mcgraw Hill,
2009
References:
Architecting the Cloud: Design Decisions for Cloud Computing Service Models
(SaaS, PaaS, and IaaS), Michael J. Kavis, Wiley CIO, 2014
Cloud Computing: SaaS, PaaS, Ia aS, Virtualization, Business Models, Mobile,
Security and More, Kris Jamsa, Jones & Bartlett Learning, 2013
14
Course Code Course Title Credits
PSCS3032 Elective I - Track B: Cyber and Information Security - II
(Cyber Forensics) 04
Unit I: Computer Foren sic Fundamentals: Introduction to Computer Forensics and
objective , the Computer Forensics Specialist, Use of Computer Forensic in Law
Enforcement, Users of Computer Forensic Evidence, Case Studies , Information
Security Investigations. Types of Computer Fo rensics Technology: Types of Military
Computer Forensic Technology, Types of Law Enforcement Computer Forensic
Technology, Types of Business Computer Forensic Technology, Specialized Forensics
Techniques, Hidden Data, Spyware and Adware, Encryption Method s and
Vulnerabilities, Protecting Data from Being Compromised, Internet Tracing Methods,
Security and Wireless Technologies. Types of Computer Forensics Systems: Study
different Security System: Internet, Intrusion Detection, Firewall, Storage Area, Networ k
Disaster Recovery, Public Key Infrastructure, Wireless Network, Satellite Encryption,
Instant Messaging (IM), Net Privacy, Identity Management, Biometric, Identity Theft.
Unit II: Data Recovery: Data Recovery and Backup, Role of Data Recovery, Hiding
and Recovering Hidden Data. Evidence Collection: Need to Collect the Evidence, Types
of Evidences, The Rules of Evidence, Collection Steps. Computer Image Verification
and Authentication: Special Needs of Evidence Authentication. Identification of Data:
Timekeeping, Forensic Identification and Analysis of Technical Surveillance Devices,
Reconstructing Past Events: How to Become a Digital Detective, Useable File Formats,
Unusable File Formats, Converting Files.
Unit III: Network Forensics: Sources of Network Based Evidence, Principles of
Internetworking, Internet Protocol Suite. Evidence Acquisition: Physical Interception,
Traffic Acquisition Software, Active Acquisition. Traffic Analysis: Protocol Analysis,
Packet Analysis, Flow Analysis, Hig her-Layer Traffi c analysis. Statistical Flow Analysis:
Sensors, Flow Record Export Protocols, Collection and Aggregation, Analysis. Wireless:
the IEEE Layer 2 Protocol Series, Wireless Access Point, Wireless Traffic Capture and
Analysis, Common Attacks, Locating Wireless Devices. Network Intrusion Detection and
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Analysis: NIDS/NIPS Functionality, Modes of Detection, Types of NIDS/NIPS,
NIDS/NIPS Evidence Acquisition.
Unit IV: Network Devices and Mobile Phone Forensics : Sources of Logs, Network
Architecture, Collecting and Analyzing Evidence, switches, routers, firewalls, interfaces
Web Proxies: Need to Investigate Web Proxies, Functionality, Evidence, Squid, Web
Proxy Analysis, Encrypted Web Traffic. Mobile Phone Forensics: Crime and Mobile
Phones, Voice, SMS and Identi fication of Data Interception in GSM, Mobile Phone
Tricks, SMS Security, Mobile Forensic .
Text book:
Computer Forensics Computer Crime Scene Investigation, John R. Vacca,
Second Edition, 2005 .
Network Forensics, Sherri Davidoff, Jonathan HAM, Prentice Hall, 2012 .
Mobile Phone Security and Forensic: A Practical Approach, Second Edition, Iosif
I. Androulidkis, Springer, 2012 .
References:
Digital forensics: Digital evidence in criminal investigation”, Angus
M.Marshall, John – Wiley and Sons, 2008.
Comput er Forensics with FTK, Fernando Carbone, PACKT Publishing, 2014 .
Practical Mobile Forensics, Satish Bommisetty, Rohit Tamma, Heather Mahalik,
PACKT Publishing, 2014 .
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Course Code Course Title
Credits
PSCS3033 Elective I - Track C: Business Intelligen ce and Big Data
Analytic s –II (Mining Massive Data sets ) 04
Unit I: Introduction To Big Data
Big data: Introduction to Big data Platform, Traits of big data, Challenges of
conventional systems, Web data, Analytic processes and tools, Analysis vs Reportin g,
Modern data analytic tools, Statistical concepts: Sampling distributions, Re -sampling,
Statistical Inference, Prediction error. Data Analysis: Regression modeling, Analysis of
time Series: Linear systems analysis, Nonlinear dynamics, Rule induction, Neu ral
networks: Learning and Generalization, Competitive Learning, Principal Component
Analysis and Neural Networks, Fuzzy Logic: Extracting Fuzzy Models from Data, Fuzzy
Decision Trees, Stochastic Search Methods.
Unit II: MAP REDUCE
Introduction to Map R educe: The map tasks, Grouping by key, The reduce tasks,
Combiners, Details of MapReduce Execution, Coping with node failures. Algorithms
Using MapReduce: Matrix -Vector Multiplication, Computing Selections and Projections,
Union, Intersection, and Differen ce, Natural Join. Extensions to MapReduce: Workflow
Systems, Recursive extensions to MapReduce, Common map reduce algorithms.
Unit III: SHINGLING OF DOCUMENTS
Finding Similar Items, Applications of Near -Neighbor Search, Jaccard similarity of sets,
Simila rity of documents, Collaborative filtering as a similar -sets problem, Documents, k -
Shingles, Choosing the Shingle Size, Hashing Shingles, Shingles built from Words.
Similarity -Preserving Summaries of Sets, Locality -Sensitive hashing for documents. The
Theo ry of Locality -Sensitive functions. Methods for high degrees of similarity.
Unit IV: MINING DATA STREAMS
Introduction to streams concepts – Stream data model and architecture, Stream
computing, Sampling data in a stream, Filtering streams, Counting disti nct elements in a
stream, Estimating moments, Counting oneness in a Window, Decaying window, Real
time analytics Platform(RTAP).
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Text book:
Mining of Massive Datasets, Anand Rajaraman and Jeffrey David Ullman,
Cambridge University Press, 2012.
Big Data , Big Analytics: Emerging Business Intelligence and Analytic Trends for
Today's Businesses, Michael Minelli, Wiley, 2013
References:
Big Data for Dummies, J. Hurwitz, et al., Wiley, 2013
Understanding Big Data Analytics for Enterprise Class Hadoop and Str eaming
Data , Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George
Lapis, McGraw -Hill, 2012.
Big data: The next frontier for innovation, competition, and productivity , James
Manyika ,Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, C harles
Roxburgh, Angela Hung Byers, McKinsey Global Institute May 2011.
Big Data Glossary , Pete Warden, O’Reilly, 2011.
Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools,
Techniques, NoSQL, and Graph , David Loshin, Morgan Ka ufmann Publishers,
2013
Course Code Course Title Credits
PSCS3034 Elective I - Track D: Machine Intelligence - II
(Advanced Machine Learning Techniques) 04
Unit I: Probability
A brief review of probability theory, Some common discrete distributions , Some
common continuous distributions, Joint probability distributions, Transformations of
random variables, Monte Carlo approximation, Information theory.
Directed graphical models (Bay es nets): Introduction, Examples, Inference, Learning,
Conditional independence properties of DGMs. Mixture models and EM algorithm:
Latent variable models, Mixture models, Parameter estimation for mixture models, The
EM algorithm.
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Unit II: Kernels
Introduction, kernel function, Using Kernel inside GLMs, kernel trick, Support vector
machines, Comparison of discriminative kernel methods.
Markov and hidden Markov mode ls: Markov models, Hidden Markov Models (HMM),
Inference in HMMs, Learning for HMMs. Undirected graphical models (Markov random
fields): Conditional independence properties of UGMs, Parameterization of MRFs,
Examples of MRFs, Learning, Conditional random f ields (CRFs), applications of CRFs.
Unit III: Monte Carlo inference
Introduction, Sampling from standard d istributions, Rejection sampling, Importance
sampling , Particle filtering , Applications: visual object tracking, time series forecasting,
Rao-Blackwellised Particle Filtering (RBPF).
Markov cha in Monte Carlo (MCMC) inference : Gibbs sampling, Metropolis Hastings
algorithm, Speed and accuracy of MCMC.
Unit IV: Graphical model structure learning
Structure learning for knowledge discovery, Learn ing tree structures, Learning DAG
structure with latent variables, Learning causal DAGs , Learning undirected Gaussian
graphical models, Learning undirected discrete graphical models. Deep learning: Deep
generative models, Deep neural networks, Applications of deep networks.
Text book:
Machine Learning: A Probabilistic Perspective: Kevin P Murphy, The MIT Press
Cambridge (2012).
References:
Introducing Monte Carlo Methods with R, Christian P. Robert, George Casella,
Springer, 2010
Introduction to Machin e Learning (Third Edition): Ethem Alpaydın, The MIT Press
(2015).
Pattern Recognition and Machine Learning: Christopher M. Bishop, Springer
(2006)
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Bayesian Reasoning and Machine Learning: David Barber, Cambridge University
Press (2012).
Statistical And Ma chine Learning Approaches For Network Analysis, Edited By
Matthias Dehmer, Subhash C. Basak: John Wiley & Sons, Inc (2012)
Practical Graph Mining with R: Edited by Nagiza -F-Samatova et al, CRC Press
(2014)
https://class.coursera.org/pgm/lecture/preview
List of practical Experiments for Semester –III
Course Code Course Title Credits
PSCS P301 Ubiquitous Computing 02
No List o f Practical Experiments
1 Design and develop locatio n based messaging app
2 Design and develop chat messaging app which is a location -based
3 Design and develop app demonstrating Simple Downstream Messaging
4 Design and develop app demonstrating Send Upstream Messages
5 Design and develop app for De vice Group Messaging
6 Implementing GCM Network Manager
7 Demonstrate use of OpenGTS (Open Source GPS Tracking System)
8 Context -Aware system
Context -awareness is a key concept in ubiquitous computing. The Java Context -
Awareness Framework (JCAF) is a J ava-based context -awareness infrastructure
and programming API for creating context -aware applications
9 Develop application demonstrating Human Computer Interaction
10 Write a Java Card applet
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Course Code Course Title Credits
PSCSP 302 Social Networ k Analysis 02
Sr
No
List of Practical Experiments
1 Write a program to compute the following for a given a network: (i) number of
edges, (ii) number of nodes; (iii) de gree of node ; (iv) node with lowest degree ; (v)
the adjacency list; (vi) matrix of the graph.
2 Perform following tasks: (i) View data collection forms and/or import one -
mode/two -mode datasets; (ii) Basic Networks matrices transformations
3 Comput e the f ollowing node l evel measures: (i) Density ; (ii) Degree ;
(iii) Reciprocity ; (iv) Transitivity ; (v) Centralization ; (vi) Clustering.
4 For a given network find the following: (i) Length of the shortest path from a given
node to another node ; (ii) t he density of th e graph ; (iii) Draw egocentric network of
node G with chosen configuration parameters.
5 Write a program to distinguish between a network as a matrix, a network as an
edge list, and a network as a sociogram (or “network graph”) using 3 distinct
networks representative s of each.
6 Write a program to exhibit structural equivalence, automatic equivalence, and
regular equivalence from a network.
7 Create sociograms for the persons -by-persons network and the committee -by-
committee network for a given relevant problem. Create one -mode network and
two-node network for the same.
8 Perform SVD analysis of a network.
9 Identify ties within the network using two -mode core periphery analysis.
10 Find “factions” in the network using two -mode faction analysis.
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Note:
One may use programming languages like R , Python , Pajek etc and open software /
tools like (i) EGONet ; (ii) Ora; (iii) Netlogo ; (iv) Pajek ; and (v) NetDraw ; to do the
practical experime nts.
Course Code
Course Title Credits
PSCSP 3031 Practical Course on Elective I -Track A:Cloud
Computing -II (Cloud Computing Technologies ) 02
Sr
No
List o f Practical Experiments
1 Execute & check the performance of existing algorithms using Cloud Sim.
2 Install a Cloud Analyst and Integrate with Eclipse/Netbeans. Monitor the
performance of an Existing Algorithms.
3 Build an application on private cloud.
4 Demonstrate any Cloud Monitoring tool.
5 Evaluate a Private IAAS Cloud using TryStack.
6 Implement FOSS -Cloud Functionality - VDI (Virtual Desktop Infrastructure)
7 Implement FOSS -Cloud Functionality VSI (Virtual Server Infrastructure)
Infrastructure as a Service (IaaS)
8 Implement FOSS -Cloud Functionality - VSI Platform as a Service (PaaS)
9 Implement FOSS -Cloud Functionality - VSI Software as a Service (SaaS)
10 Explore FOSS -Cloud Functionality - Storage Cloud
22
Course Code Course Title Credits
PSCSP 3032 Practical Course on Elective I -Track B: Cyber and
Information Security - II (Cyber Forensics) 02
Sr
No
List of Practical Experiments
1 Write a program to take backup of mysql database
2 Write a program to restore mysql database
3 Use DriveImage XML to image a hard drive
4 Write a program to create a log file
5 Write a program to fi nd a file in a directory
6 Write a program to find a word in a file
7 Create forensic images of digital devices from volatile data such as memory
using Imager for: (i) Computer System; (ii) Server; (iii) Mobile Device
8 Access and extract relevant info rmation from Windows Registry for investigation
process using Registry View, perform data analysis and bookmark the findings
with respect to: (i) Computer System; (ii) Computer Network; (iii) Mobile Device;
(iv) Wireless Network
9 Generate a report based on the analysis done using Registry View for different
case scenario of the following: (i) Computer System; (ii) Computer Network;
(iii) Mobile Device; (iv) Wireless Network
10 Create a new investigation case using Forensic Tool: (i) Comput er System; (ii)
Computer Network; (iii) Mobile Device ;(iv) Wireless Network .
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Course Code Course Title Credits
PSCSP3033 Practical Course on Elective II -Track C: Business
Intelligence and Big Data Analytics - II
(Mining Ma ssive Data sets -I) 02
No List of Practical Experiments
1 Generate regression model and interpret the result for a given data set.
2 Generate forecasting model and interpret the result for a given data set .
3 Write a map -reduce program to count the n umber of occurrences of each
alphabetic character in the given dataset. The count for each letter should be
case -insensitive (i.e., include both upper -case and lower -case versions of the
letter; Ignore non -alphabetic characters).
4 Write a map -reduce prog ram to count the number of occurrences of each word in
the given dataset. (A word is defined as any string of alphabetic characters
appearing between non -alphabetic characters like nature's is two words. The
count should be case -insensitive. If a word occu rs multiple times in a line, all
should be counted)
5 Write a map -reduce program to determine the average ratings of movies. The
input consists of a series of lines, each containing a movie number, user number,
rating and a timestamp.
6 Write a map -reduc e program: (i) to find matrix -vector multiplication; (ii) to
compute selections and projections; (iii) to find union, intersection, difference,
natural Join for a given dataset.
7 Write a program to construct different types of k -shingles for given docu ment .
8 Write a program for measuring similarity among documents and detecting
passages which have been reused.
9 Write a program to compute the n - moment for a given stream where n is given.
10 Write a program to demonstrate the Alon -Matias -Szegedy Al gorithm for second
moments.
Note: The experiments may be done using software/tools like Hadoop / WEKA / R /
Java etc.
24
Course Code Course Title Credits
PSCSP3034 Practical Course on Elective II - Track D: Machine
Intelligence - II (Advanced Machine Learning
Techniques) 02
Sr
No
List of Practical Experiments
1 Find probability density function or probab ility mass function, cumulative
distribution function and joint distribution function to calculate probabilities and
quantiles for standard statis tical distributions.
2 Create a Directed Acyclic Graph (DAG) using (i) set of formulae (ii) set of vectors
and (iii) set of matrices. Find parents and children of nodes. Read conditional
independence from DAG. Add and remove edges from graph.
3 Create a Bayesian network for a given narrative. Set findings and ask queries
[One may use narratives like ‘chest clinic narrative’ and package gRain for the
purpose].
4 Implement EM algorithm .
5 Use string kernel to find the similarity of two amino acid sequence where
similarity is defined as the number of a substring in common.
6 Demonstrate SVM as a binary classifier .
7 Create a random graph and find its page rank .
8 Apply random walk technique to a multivariate time series .
9 Implement two stage Gibbs Sam pler.
10 Implement Metropolis Hastings algorithm .
25
Detailed syllabus of semester – IV
Course Code Course Title Credits
PSCS 401 Simulation and Modeling 04
Unit I: Introduction
Introduction to Simulation, Need of Simulation, Time to simulate, Inside si mulation
software: Modeling the progress of Time, Modeling Variability, Conceptual Modeling:
Introduction to Conceptual modeling, Defining conceptual model, Requirements of the
conceptual model, Communicating the conceptual model, Developing the Conceptual
Model: Introduction, A framework for conceptual modeling, methods of model
simplification.
Unit II: Model Verification and Validation
Data Collection and Analysis: Introduction, Data requirements, Obtaining data,
Representing unpredictable variability, S electing statistical distributions. Obtaining
Accurate Results: Introduction, The nature of simulation models and simulation output,
Issues in obtaining accurate simulation results, example model, dealing with
initialization bias: warm -up and initial condi tions, Selecting the number of replications
and run -length. Searching the Solution Space: Introduction, The nature of simulation
experimentation, Analysis of results from a single scenario, Comparing alternatives,
Search experimentation, and Sensitive anal ysis. Verification, Validation and
Confidence: Introduction, Defining Verification and Validation, The difficulties of
verification and validation, Methods of verification and validation, Independent
verification and validation.
Unit III: Modeling and sim ulation modeling
Types of models, Analytical vs Simulation modeling, Application of simulation modeling,
Level of a bstraction, Simulation Modeling. Methods, System Dynamics, Discrete Event
Modeling, Agent Based modeling: Introduction to Agent, Agent -based modeling , Time in
agent based models, Space in agent based models, Discrete space, Continuous space
movement in continuous space, Communication between agents, Dynamic creation and
destruction of agents, Statics on agent population, Condition triggered eve nts and
transition in agents. Building agents based models: The problem statement, Phases of
26
modeling, Assumptions, 3 D animation. Dynamics Systems: Stock and flow diagrams,
examples of stock and flow diagrams. Multi -method modeling: Architecture, Technica l
aspects of combining modeling methods, Examples.
Unit IV: Design and behavior of models
Designing state -based behavior: Statecharts, State transitions, Viewing and debugging
Statecharts at runtime, Statecharts for dynamic objects. Discrete events and E vent
model object: Discrete event, Event -the simplest low level model object, Dynamic
events, and Exchanging data with external world. Presentation and animation: Working
with shapes, groups and colors, Designing interactive models: using controls, Dynamic
properties of controls, 3D Animation . Randomness in Models: Probability distributions,
sources of randomness in the model, randomness in system dynamics model, random
number generators, Model time, date and calendar: Virtual and real time: The model
time, date and calendar, Virtual and real -time execution modes.
Text book:
Simulation: The Practice of Model Development and Use by Stewart Robinson,
John Wiley and Sons, Ltd, 2004 .
The Big Book of Simulation Modeling: Multi Method Modeling by Andrei
Borshch ev, 2013 .
References:
Agent Based Modeling and Simulation, Taylor S, 2014 .
Simulation Modeling Handbook: A Practical Approach, Christopher A. Chung,
2003 .
Object Oriented Simulation: A Modeling and Programming Per spective, Garrido,
José M, 2009.
Simulatio n, Modeling and Analysis, Averill M Law and W. David Kelton, "Tata
McGraw Hill, Third Edition, 2003.
Process Control: Modeling, Design and Simulation, Wayne Bequette W, Prentice
Hall of India, 2003.
27
Course Code Course Title Credits
PSCS 4021 Specializa tion: Cloud Computing -III
(Building Clouds and Services ) 04
Unit I: Cloud Reference Architectures and Security
The NIST definition of Cloud Computing, Cloud Computing reference architecture,
Cloud Computing use cases, Cloud Co mputing standards. Cloud Computing Security -
Basic Terms and Concepts, Threat Agents, Cloud Security Threats. Cloud Security
Mechanisms, Encryption, Hashing, Digital Signature, Public Key Infrastructure (PKI),
Identity and Access Management (IAM), Single S ign-On (SSO), Cloud -Based Security
Groups, Hardened Virtual Server Images .
Unit II: Cloud Computing Mechanisms
Cloud Infrastructure Mechanisms, Logical Network Perimeter, Virtual Server, Cloud
Storage Device, Cloud Usage Monitor, Resource Replication Read y-Made
Environment . Specialized Cloud Mechanisms, Automated Scaling Listener, Load
Balancer, SLA Monitor, Pay -Per-Use Monitor, Audit Monitor, Failover System,
Hypervisor, Resource Cluster, Multi -Device Broker, State Management Database .
Cloud Management Me chanisms, Remote Administration System, Resource
Management System, SLA Management System, Billing Management System .
Unit III: Cloud Computing Architecture
Fundamental Cloud Architectures, Workload Distribution Architecture, Resource Pooling
Architectur e, Dynamic Scalability Architecture, Elastic Resource Capacity Architecture,
Service Load Balancing Architecture, Cloud Bursting Architecture, Elastic Disk
Provisioning Architecture, Redundant Storage Architecture. Advanced Cloud
Architectures, Hypervisor Clustering Architecture, Load Balanced Virtual Server
Instances Architecture, Non -Disruptive Service Relocation Architecture, Zero Downtime
Architecture, Cloud Balancing Architecture, Resource Reservation Architecture,
Dynamic Failure Detection and Recover y Architecture, Bare -Metal Provisioning
Architecture, Rapid Provisioning Architecture, Storage Workload Management
Architecture .
28
Unit IV: Working with Clouds
Cloud Delivery Model Considerations, Cloud Delivery Models: The Cloud Provider
Perspective, Build ing IaaS Environments, Equipping PaaS Environments, Optimizing
SaaS Environments, Cloud Delivery Models: The Cloud Consumer Perspective. Cost
Metrics and Pricing Models, Business Cost Metrics, Cloud Usage Cost Metrics, Cost
Management Considerations. Servi ce Quality Metrics and SLAs, Service Quality
Metrics, Service Availability Metrics, Service Reliability Metrics, Service Performance
Metrics, Service Scalability Metrics, Service Resiliency Metrics.
Text book:
Cloud Computing Concepts, Technology & Arch itecture, Thomas Erl, Zaigham
Mahmood, and Ricardo Puttini, Prentice Hall, 2013 .
Cloud Security - A Comprehensive Guide to Secure Cloud Computing, Ronald L.
Krutz, Russell Dean Vines, Wiley Publishing, Inc., 2010 .
Open Stack Cloud Computing Cookbook, Kevin Jackson, Cody Bunch, Egle
Sigler, Packt Publishing, Third Edition, 2015.
Reference:
Tom Fifield, Diane Fleming, Anne Gentle, Lorin Hochstein, Jonathan Proulx,
Everett Toews, and Joe, Topjian,OpenStack Operations Guide, O'Reilly Media,
Inc, 2014 .
NIST Cl oud Computing Standards Roadmap, Special Publication 500 -291,
Version 2, NIST, July 2013, http://www.nist.gov/itl/cloud/upload/NIST_SP -500-
291_Version -2_2013_June18_FINAL.pdf
https://www.openstack.org
http://cloudstack.apache.org
http://www.foss -cloud.org/en/wiki/FOSS -Cloud
http://www.ubuntu.com/cloud/open stack/autopilot
29
Course Code Course Title Credits
PSCS 4022 Specialization: Cyber and Information Security
(Cryptography and Crypt Analysis ) 04
Unit I: Introduction to Number Theory
Topics in Elementary Number Theory: O and notations, time estimates for doing
arithmetic -divisibility and the Euclidean algorithm, Congruence: Definitions and
properties, linear congruence, residue classes, Euler’s phi function, Fermat’s Little
Theorem, Chinese Reminder Theorem, Applications to factoring, finite fields, qu adratic
residues and reciprocity: Quadratic residues, Legendre symbol, Jacobi Symbol. (proofs
of the theorems are not expected to cover).
Unit II: Simple Cryptosystems
Shift Cipher, Substitution Cipher, Affine Cipher, Vigenère Cipher, Vermin Cipher, Hill
Cipher, Permutation Cipher, Stream Cipher, Cryptanalysis of Affine Cipher, Substitution
Cipher, Vigenère Cipher and Hill Cipher, Block Ciphers, Algorithm Modes, DES, Double
DES, Triple DES, Meet -in-Middle Attack, AES, IDEA algorithm. Cryptographic Hash
Functions: Hash Functions and Data Integrity, Security of Hash Functions, Secure Hash
Algorithm, Message Authentication Code, Nested MACs, HMAC .
Unit III: RSA Cryptosystem
The RSA Algorithm, Primarily Testing, Legendre and Jacobi Symbols, The Solovay -
Stras sen Algorithm, The Miller -Rabin Algorithm, Factoring Algorithm: The pollard p -1
Algorithm, Dixon’s Random Squares Algorithm, Attacks on RSA, The Rabin
Cryptosystem. Public Key Cryptosystems: The idea of public key Cryptography, The
Diffie -Hellman Key Agree ment, ElGamal Cryptosystem, The Pollard Rho Discrete
Logarithm Algorithm, Elliptic Curves, Knapsack problem .
Unit IV: Key Distribution and Key Agreement Scheme
Diffie -Hellman Key distribution and Key agreement scheme, Key Distribution Patterns,
Mitchell -Piper Key distribution pattern, Station -to-station protocol, MTI Key Agreement
30
scheme. Public -Key Infrastructure: What is PKI?, Secure Socket Layer, Certificates,
Certificate Life cycle, Trust Models: Strict Hierarchy Model, Networked PKIs, The web
browser Model, Pretty Good Privacy .
Text book:
Discrete Mathematics and Its Applications, Kenneth H. Rosen, 7th Edition,
McGraw Hill, 2012.
Cryptography Theory and Practice, 3rd Edition, Douglas R. Stinson, 2005 .
Reference:
Network Security and Cryptography, A tul Kahate , McGraw Hill, 2003 .
Cryptography and Network Security: Principles and Practices, William Stalling,
Fourth Edition, Prentice Hall , 2013 .
Introduction to Cryptography with coding theory, second edition , Wade Trappe ,
Lawrence C. Washington , Pearson, 2005.
Course Code Course Titl e Credits
PSCS 4023 Specialization: Business Intelligence and Big Data
Analytics (Intelligent Data Analysis ) 04
Unit I: Clustering
Distance/Similarity, Partitioning Algorithm: K -Means; K -Medoids, Partitioning Algorithm
for large data set: CLARA; CLARANS, Hierarchical Algorithms: Agglomerative
(AGNES); Divisive (DIANA), Density based clustering: DBSCAN, Clustering in Non -
Euclidean Spaces, Clustering for Streams and Parallelism.
Unit II: Classification
Challenges, Distance based Algorithm: K nearest Neigh bors and kD -Trees, Rules and
Trees based Classifiers, Information gain theory, Statistical based classifiers: Bayesian
classification, Document classification, Bayesian Networks. Introduction to Support
31
Vector Machines, Evaluation: Confusion Matrix, Costs, Lift Curves, ROC Curves,
Regression/model trees: CHAID (Chi Squared Automatic Interaction Detector). CART
(Classification And Regression Tree).
Unit III: Dimensionality Reduction
Introduction to Eigen values and Eigen vectors of Symmetric Matrices, Prin cipal-
Component Analysis, Singular -Value Decomposition, CUR Decomposition.
Unit IV: Link Analysis And Recommendation Systems
Link analysis: PageRank, Efficient Computation of PageRank, Topic -Sensitive
PageRank, Link Spam. Recommendation Systems: A Model for Recommendation
Systems, Content -Based Recommendations, Collaborative Filtering, Dimensionality
Reduction.
Text book:
Mining of Massive Datasets, Anand Rajaraman and Jeffrey David Ullman,
Cambridge University Press, 2012.
Data Mining: Introductory an d Advanced Topics, Margaret H. Dunham, Pearson,
2013.
Reference:
Big Data for Dummies, J. Hurwitz, et al., Wiley, 2013.
Networks, Crowds, and Markets: Reasoning about a Highly Connected World,
David Easley and Jon Kleinberg, Cambridge University Press, 2010.
Lecture Notes in Data Mining, Berry, Browne, World Scientific, 2009.
Data Mining: Concepts and Techniques third edition, Han and Kamber, Morgan
Kaufmann , 2011.
Data Mining Practical Machine Learning Tools and Techniques, Ian H. Witten,
Eibe Frank, T he Morgan Kaufmann Series in Data Management Systems, 2005.
Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools,
Techniques, NoSQL and Graph, David Loshin, Morgan Kaufmann Publishers,
2013 .
32
Course Code Course Title Credits
PSCS 4024 Specialization: Machine Learning -III
(Computational Intelligence ) 04
Unit I: Artificial Neural Networks
The Artificial Neuron, Supervised Learning Neural Networks, Unsupervised Learning
Neural Networks, Radial Basis Function Net works, Reinforcement Learning,
Performance Issues.
Unit II: Evolutionary Computation
Introduction to Evolutionary Computation, Genetic Algorithms, Genetic Programming,
Evolutionary Programming, Evolution Strategies, Differential Evolution, Cultural
Algori thms, Co -evolution.
Unit III: Computational Swarm Intelligence
Particle Swarm Optimization(PSO) - Basic Particle Swarm Optimization, Social Network
Structures, Basic Variations and parameters, Single -Solution PSO. Advanced Topics
and applications. Ant Alg orithms - Ant Colony Optimization Meta -Heuristic, Cemetery
Organization and Brood Care, Division of Labor, Advanced Topics and applications.
Unit IV: Artificial Immune systems, Fuzzy Systems and Rough Sets
Natural Immune System, Artificial Immune Models, F uzzy Sets, Fuzzy Logic and
Reasoning, Fuzzy Controllers, Rough Sets.
Text book:
Computational Intelligence - An Introduction (Second Edition): Andries P.
Engelbrecht, John Willey & Sons Publications (2007).
Reference:
Computational Intelligence And Fe ature Selection: Rough And Fuzzy
Approaches, Richard Jensen Qiang Shen, IEEE Press Series On Computational
Intelligence, A John Wiley & Sons, Inc., Publication, 2008.
Computational Intelligence And Pattern Analysis In Biological Informatics,
(Editors) . Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. L.Wang, John
Wiley & Sons, Inc, 2010 .
33
Neural Networks for Applied Sciences and Engineering: From Fundamentals to
Complex Pattern Recognition 1st Edition, Sandhya Samarasinghe, Auerbach
Publications, 2006.
Introduction to Evolutionary Computing (Natural Computing Series) 2nd ed, A.E.
Eiben , James E Smith, Springer; 2015.
Swarm Intelligence, 1st Edition, Russell C. Eberhart, Yuhui Shi, James Kennedy,
Morgan Kaufmann, 2001
Artificial Immune System: Applicatio ns in Computer Security, Ying Tan, Wiley -
IEEE Computer Society, 2016.
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
1st Edition, Richard Jensen, Qiang Shen, Wiley -IEEE Press, 2008
List of Practical Experiments for Semester –IV
Course Code Course Title Credits
PSCSP 401 Practical course on Simulation and modeling 02
Sr
No
List o f Practical Experiments
1 Design and develop agent based model by
Creating the agent population
Defining the agent behavior
Add a chart to visuali ze the model output.
[Use a case scenario like grocery store, telephone call center etc for the
purpose].
2 Design and develop agent based model by
Creating the agent population
Defining the agent behavior
Adding a chart to visualize the model output
Adding word of mouth effect
Considering product discards
34
Consider ing delivery time
[Use a case scenario like restaurant].
3 Design and develop agent based model by
Creating the agent population
Defining the agent behavior
Adding a chart to visualize the mod el output
Adding word of mouth effect
Considering product discards
Consider delivery time
Simulating agent impatience
Comparing model runs with different parameter values
[Use a scenario like market model]
4 Design and develop System Dynamic model by
Creating a stock and flow diagram
Adding a plot to visualize dynamics
Parameter Variation
Calibration
[ Use a case scenario like spread of contagious disease for the purpose]
5 Design and develop a discrete -event model that will simulate process by:
Creati ng a simple model
Adding resources
Creating 3D animation
Modeling delivery
[Use a case situation like a company’s manufacturing and shipping].
6 Design and develop time -slice simulation for a scenario like airport model to
design how passengers move withi n a small airport that hosts two airlines, each
with their own gate. Passengers arrive at the airport, check in, pass the security
checkpoint and then go to the waiting area. After boarding starts, each airline’s
representatives check their passengers’ tic kets before they allow them to board.
35
7 Verify and validate a model developed like bank model or manufacturing model
8 Create defense model to stimulate aircraft behavior
9 Stimulate the travelling sales man problem to compute the shortest path.
10 Stimulate the Urban dynamics to address the scenarios like:
(a) The problem of public transport line
(b) To compute the time taken for train to enter the station
Course Code Course Title Credits
PSCSP 4021 Practical Course on Specialization : Cloud Computing
(Building Clouds and S ervices) 02
Sr
No List o f Practical Experiments
1 Develop a private cloud using an open source technology .
2 Develop a public cloud using an open s ource technology .
3 Explore Service Offerings, Disk Offerings, Network Offerings and Templates .
4 Explore Working of the following with Virtu al Machines
VM Lifecycle
Creating VMs
Accessing VMs
Assigning VMs to Hosts
5 Explore Working of the following with Virtual Machines
Changi ng the Service Offering for a VM
Using SSH Keys for Authentication
6 Explore the working of the following: Storage Overview
Primary Storage
36
Secondary Storage
7 Explore the working of the following: Storage Overview
Working With Volumes
Working with Volume Snapshots
8 Explore m anaging the Cloud using following:
Tags to Organize Resources in the Cloud
Reporting CPU Sockets
9 Explore m anaging the Cloud using following:
Changing the Database Configuration
File encryption type
10 Explore m anaging the Cloud using following:
Administrator Alerts
Customizing the Network Domain Name
Note
Recommended Open Source Technologies for completing practical:
FOSS -Cloud
Try Stack
Apache CloudStack
OpenStack
Canonical’s OpenStack Autopilot
Recomme nded Configuration: Desktop PC Core I5 with minimum 250 GB Hard Drive
and minimum 8 GB RAM
37
Course Code Course Title Credits
PSCSP 4022 Practical Course on Specialization : Cyber &
Information Security ( Cryptography and Crypt
Analysis ) 02
Sr
No List of Practical Experiments
1 Write a program to implement following:
Chinese Reminder Theorem
Fermat’s Little Theorem
2 Write a program to implement the (i) Affine Cipher (ii) Rail Fence Technique (iii)
Simple Columnar Technique (iv) Vermin Cipher (v) Hill Cipher to perform
encryption and decryption .
3 Write a program to implement the (i) RSA Algorithm to perform encryption and
decryption.
4 Write a program to impl ement the (i) Miller -Rabin Algorithm (i i) pollard p -1
Algorithm to perform encryption and decryption .
5 Write a program to implement the ElGamal Cryptosystem to generate keys and
perform encryption and decryption .
6 Write a program to implement the Diffi e-Hellman Key Agreement algorithm to
generate symmetric keys .
7 Write a program to implement the MD5 algorithm compute the message digest .
8 Write a program to implement different process es of DES algorithm like (i) Initial
Permutation process of DES alg orithm , (ii) Generate Keys for DES algorithm , (iii)
S-Box substitution for DES algorithm .
9 Write a program to encrypt and decrypt text using IDEA algorithm .
10 Write a program to implement HMAC signatures .
38
Course Code Course Title Credits
PSCSP2 023 Practical Course on Specialization :
Business Intelligence & Big Data Analytics
(Intelligent Data Analysis ) 02
Sr
No List of Practical Experiments
1 Pre-process the given data set and hence apply clustering techniques like K -
Means, K -Medoids. Interpret the result.
2 Pre-process the given data set and hence apply partition clustering algorithms.
Interpret the result
3 Pre-process the given data set and hence apply hierarchical algorithms and
density based clustering techniques. Interpret the result.
4 Pre-process the given data set and hence classify the resultant data set using
tree classification techniques. Interpret the re sult.
5 Pre-process the given data set and hence classify the resultant data set using
Statistical based classifiers. Interpret the result.
6 Pre-process the given data set and hence classify the resultant data set using
support vector machine. Interpret the result.
7 Write a program to explain different functions of Principal Components .
8 Write a program to explain CUR Decomposition technique.
9 Write a program to explain links to establish higher -order relationships among
entities in Link Analysis.
10 Write a program to implement step -by-step a Collaborative Filtering
Recommender System.
The experiments may be done using software/ tools like R/Weka/Java etc.
39
Course Code Course Title Credits
PSCSP20 24 Practical Course on Specialization :
Machine Intelligence
(Computational Intelligence ) 02
Sr
No List of Practical Experiments
1 Implement feed forward neural network for a given data .
2 Implement Sel f Organizing Map neural network.
3 Implement Radial Basis Function neura l network with gradient descent.
4 Implement a basic genetic algorithm with selection, mutation and crossover a s
genetic operators.
5 Impleme nt evolution strategy algorithm.
6 Implement general differential evolution algorithm .
7 Implement gbest an d lbest of PSO .
8 Implement simple Ant colony optimization algorithm .
9 Implement basic artificial immune syst em algorithm .
10 Apply different defuzzification methods for centroid calculation of a given fuzzy
rule base .
Note: The above practical experiments may use programming languages like C, Java,
R etc.
40
Scheme of Examination for Theory Courses
There will be internal and external examination for the theory courses. The weightage of
internal/external and scheme of examination will be as per common guidelines provided
by the University for the PG courses in the faculty of Science.
Scheme of Examina tion for Practical Courses
There will not be any internal examination for practical courses.
External Examination for practical courses :
The evaluation of the external examination of practical course is given below:
Sr
No Semester Course
Code Particular No of
questions Marks
per
question Total
Marks
1
III
PSCSP 5
Laboratory experiment
question
2
40
80
Journal - 10 10
Viva - 10 10
Marks for each course 100
2
III
PSCSP6 Laboratory experiment
question 2 25 50
Journal - 10 10
Viva - 10 10
viva on Project
Proposal Documentation 10
30 Presentation 10
Viva 10
Total Marks 100
41
Sr
No Semester Course
Code Particular No of
questions Marks
per
question Total
Marks
1 IV
PSCSP7
Laboratory experiment
question
2
40
80
Journal - 10 10
Viva - 10 10
Total Marks
100
2
IV
PSCSP8 Intern -
ship Internship
conduct Quality and
relevance 40
100 Documentation 30
Presentation 30
Internship Viva 50 50
Total Marks
150
3 IV PSCSP9 Project
Implem
entation Project
conduct Quality and
relevance 40
100
Documentation 30
Presentation 30
Project viva
50
50
Total Marks
150
42
Guide lines for maintenance of journals:
A student shou ld maintain a journal with at least six practical experiments for each part
of the practical course. Certified journals need to be submitted at the time of the
practical examination.
Guidelines for Project Proposal in Semester - III
• Student should take a topic related to the specialization he or she is planning to
take in Semester -IV.
• Should have studied the related topics in the elective he or she has chosen in
semester -II and semester - III
• A student is expected to devote at least 2 to 3 months of study as part of topic
selection and its documentation.
• The student should be comfortable to implement the proposal in the semester –
IV.
Guidelines for Documentation of Project Proposal in Semester –III
Student is expected to make a project proposal documen tation which should contain the
following:
Title: A suitable title giving the idea about what work is proposed .
Introduction: An introduction to the topic of around 3 -5 pages, giving proper
back ground of the topic discussed.
Related works: A detailed su rvey of the relevant works done by others in the
domain. Student is expected to refer at least 5 research papers in addition to text
books and web -links in the relevant topic. It may be around 7 to 10 pages .
Objective: A detailed objective of the proposal is need ed. It may be of 1 to 2
pages.
Methodology: A proper and detailed procedure of how to solve the problem
discussed. It shall contain the techniques, tools, software and data to be used. It
shall be of around 3 to 5 pages.
43
The report may be of ar ound 20 pages, which needs to be signed by the teacher in
charge and head of the Department. Students should submit the signed project
proposal documentation at the time of viva as part of the University examination.
Guidelines for internship in Semeste r - IV
• Internship should be o f 2 to 3 months with 8 to 12 weeks duration .
• A student is expected to find internship by himself or herself. However, the
institution should assist their students in getting internship in good organizations.
• The home instituti on cannot be taken as the place of internship .
• A student is expected to devote at least 3 00 hours physically at the organization .
• Internship can be on an y topic covered in the syllabus mentioned in the syllabus,
not restricted to the specialization.
• Intern ship can be done, in one of the following , but not restricted to, types of
organizations:
o Software development firms
o Hardware/ manufacturing firms
o Any small scale industries, service providers like banks
o Clinics/ NGOs/professional institutions like that of CA, Advocate etc
o Civic Depts like Ward office/post office/police station/ punchayat.
o Research Centres/ University Depts / College as r esearch Assistant for
research projects or similar capacities .
Guidelines for making Internship Report in Semester –IV
A student is expected to make a report based on the internship he or she has do ne in
an organization. It should contain the following:
Certificate: A certificate in the prescribed Performa (given in appendix 1) from
the organization where the internshi p done.
Evaluation form : The form filled by the supervisor or to whom the intern was
reporting, in the prescribed Performa (given in appendix 2).
44
Title: A suitable title giving the idea about what work the student has performed
during the internship.
Description of the organization: A small description of 1 to 2 pages on the
organization where the student has interned
Description about the activities done by the section where the intern has
worked : A description of 2 to 4 pages about the section or cel l of the
organization where the intern actually worked. This should give an idea about the
type of activity a new employee is expected to do in that section of the
organization.
Description of work allotted and actually done by the intern : A detailed
description of the work allotted and actual work performed by the intern during
the internship period. Intern may give a weekly report of the work by him or her if
needed. It shall be of around 7 to 10 pages.
Self assessment: A self assessment by the int ern on what he or she has learnt
during the internship period. It shall contain both technical as well as inter
personal s kills learned in the process. It shall be of around 2 to 3 pages.
The internship report may be around 15 pages and this needs to be submitted to the
external examiner at the time of University examination.
Guidelines for Research Implementation in Semester - IV
• Student should continue with t opic proposed and evaluated at the semester – III.
• The topic has to be related with the special ization he or she has chosen in the
semester – IV.
• A student is expected to devote at least 3 to 4 months of efforts for the
implementation .
• Student should submit a detailed project implementation report at the time of
viva.
45
Guidelines for Documentatio n of Project Proposal in Semester –IV
A Student should submit project implementation report with following details:
Title: Title of the project (Same as the one proposed and evaluated at the
semester II examination).
Implementation details: A description of how the project has been
implemented. It shall be of 2 to 4 pages.
Experimental set up and results: A detailed explanation on how experiments
were conducted, what software used and the results obtained. Details like screen
shots, tables and graphs can come here. It shall be of 6 to 10 pages.
Analysis of the results: A description on what the results means and how they
have been arrived at. Different performing measures or statistical tools used etc
may be part of this. It shall be of 4 to 6 pages.
Conc lusion: A conclusion of the project performed in terms of its outcome (May
be half a page ).
Future enhancement: A small description on what enhancement can be done
when more time and resources are available (May be half a page ).
Program code: The progra m code may be given as appendix.
The report may be of around 20 pages (excluding program code) , which needs to be
signed by the teacher in charge and head of the Department. Student should submit the
signed project implementation report along with evalua ted copy of the project proposal
documentation (of semester –III) at the time of Project evaluation and viva as part of the
University examination.
46
Appendix 1
(Proforma for the certificate for internship in official letter head )
This is to certi fy that Mr/Ms_______________________________ of
_____________________College/Institution worked as an intern as part of her MSc
course in Computer Science of University of Mumbai. The particulars of internship are
given below:
Internship starting date: __ _____________
Internship end ing date:________________
Actual number of days worked:______________
Tentative number of hours worked:__________ Hours
Broad area of work: ______________________________________________
A small description of work done by the intern during the period :
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
____________________ __________________________________________________
____________________________________________________________ __________
_____________________________________________________________________
Signature:
Name:
Designation:
Contact number:
Email:
(seal of the organization)
47
Appendix 2
(Proforma for the Evaluation of the intern by the supervisor/to whom the intern was
reporting in the organization)
Professional Evaluation of intern
Name of intern: _____________________________________________
College/institu tion:________________________________________
[Note: Give a score in the 1 -5 scale by putting √ in the respective cells]
Sr
No Particular Excellent Very
Good Good Moderate Satisfactory
1 Attendance
2 Punctuality
3 Adaptability
4 Abilit y to shoulder
responsibility
5 Ability to work in
a team
6 Written and oral
communication
skills
7 Problem solving
skills
8 Ability to grasp
new concepts
9 Ability to
complete task
10 Quality of work
done
48
Comments:
______________________________________________________________________
______________________________________________________________________
_____________________
Signature:
Name:
Designation:
Contact number:
Email:
(seal of the organization)