MSc IT Part 1 Syllabus 2019 20 onse side converted Syllabus Mumbai University by munotes
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Academic Council: 26/07/2019
Item No: 4.76
UNIVERSITY OF MUMBAI
Syllabus for M.Sc. Part I
(Semester I and II)
Programme: M.Sc.
Subject: Information Technology
(Choice Based Credit System with effect from
the academic year 2019 – 2020 )
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2
Semester – I
Course Code Course Title Credits
PSIT101 Research in Computing 4
PSIT102 Data Science 4
PSIT103 Cloud Computing 4
PSIT104 Soft Computing Techniques 4
PSIT1P1 Research in Computing Practical 2
PSIT1P2 Data Science Practical 2
PSIT1P3 Cloud Computing Practical 2
PSIT1P4 Soft Computing Techniques Practical 2
Total Credits 24
Semester – II
Course Code Course Title Credits
PSIT201 Big Data Analytics 4
PSIT202 Modern Networking 4
PSIT203 Microservices Architecture 4
PSIT204 Image Processing 4
PSIT2P1 Big Data Analytics Practical 2
PSIT2P2 Modern Networking Practical 2
PSIT2P3 Microservices Architecture Practical 2
PSIT2P4 Image Processing Practical 2
Total Credits 24
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3 Program Specific Outcomes
PSO1: Ability to apply the knowledge of Information Technology with recent trends aligned with
research and industry.
PSO2: Ability to apply IT in the field of Computational Research, Soft Computing, Big Data
Analytics, Data Science, Image Processing, Artificial Intelligence, Networking and Cloud
Computing.
PSO3: Ability to provide socially acceptable technical solutions in the domains o f Information
Security, Machine Learning, Internet of Things and Embedded System, Infrastructure Services as
specializations.
PSO4: Ability to apply the knowledge of Intellectual Property Rights, Cyber Laws and Cyber
Forensics and various standards in int erest of National Security and Integrity along with IT
Industry.
PSO5: Ability to write effective project reports, research publications and content development
and to work in multidisciplinary environment in the context of changing technologies.
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4
SEMESTER I
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5 M. Sc (Information Technology) Semester – I
Course Name: Research in Computing Course Code: PSIT101
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives To be able to conduct business research with an understanding of all
the latest theories.
To develop the ability to explore research techniques used for solving
any real world or innovate problem.
Pre requisites Basic knowledge of statistical methods. Analytical and logical thinking.
Unit Details Lectures
I Introduction: Role of Business Research, Information Systems and
Knowledge Management, Theory Building, Organization ethics and
Issues
12
II Beginning Stages of Research Process: Problem definition,
Qualitative research tools, Secondary data research 12
III Research Methods and Data Collection: Survey research,
communicating with respondents, Observation methods, Experimental
research
12
IV Measurement Concepts, Sampling and Field work: Levels of Scale
measurement, attitude measurement, questionnaire design, sampling
designs and procedures, determination of sample size
12
V Data Analysis and Presentation: Editing and Coding, Basic Data
Analysis, Univariate Statistical Analysis and Bivariate Statistical
analysis and differences between two variables. Multivariate Statistical
Analysis.
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Business Research Methods William
G.Zikmund, B.J
Babin, J.C. Carr, Cengage 8e 2016
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6 Atanu Adhikari,
M.Griffin
2. Business
Analytics Albright
Winston Cengage 5e 2015
3. Research Methods for
Business Students Fifth
Edition Mark Saunders 2011
4. Multivariate Data Analysis Hair Pearson 7e 2014
M. Sc (Information Technology) Semester – I
Course Name: Research in Computing Practical Course Code: PSIT1P1
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hou
rs Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome A learner will be able to:
solve real world problems with scientific approach.
develop analytical skills by applying scientific methods.
recognize, understand and apply the language, theory and models of
the field of business analytics
foster an ability to critically analyze, synthesize and solve complex
unstructured business problems
understand and critically apply the concepts and methods of
business analytics
identify, model and solve decision problems in different settings
interpret results/solutions and identify appropriate courses of
action for a given managerial situation whether a problem or an
opportunity
create viable solutions to decision making problems
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7 M. Sc (Information Technology) Semester – I
Course Name: Data Science Course Code: PSIT102
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives
Develop in depth understanding of the key technologies in data science
and business analytics: data mining, machine learning, visualization
techniques, predictive modeling, and statistics.
Practice problem analysis and decision -making.
Gain practical, hands -on exper ience with statistics programming
languages and big data tools through coursework and applied research
experiences.
Pre requisites Basic understanding of statistics
Unit Details Lectures
I Data Science Technology Stack: Rapid Information Factory
Ecosystem, Data Science Storage Tools, Data Lake, Data Vault, Data
Warehouse Bus Matrix, Data Science Processing Tools ,Spark, Mesos,
Akka , Cassandra, Kafka, Elastic Search, R ,Scala, Python, MQTT, The
Future
Layered Framework: Definition of Data Science Framework, Cross -
Industry Standard Process for Data Mining (CRISP -DM),
Homogeneous Ontology for Recursive Uniform Schema, The Top
Layers of a Layered Framework, Layered Framework for High -Level
Data Science and Engineering
Busin ess Layer: Business Layer, Engineering a Practical Business
Layer
Utility Layer: Basic Utility Design, Engineering a Practical Utility
Layer
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II Three Management Layers: Operational Management Layer,
Processing -Stream Definition and Management, Audit, Balance, and
Control Layer, Balance, Control, Yoke Solution, Cause -and-Effect,
Analysis System, Functional Layer, Data Science Process
Retrieve Superstep : Data Lakes, Data Swamps, Training the Trainer
Model, Understanding the Business Dynamics of t he Data Lake,
Actionable Business Knowledge from Data Lakes, Engineering a
Practical Retrieve Superstep, Connecting to Other Data Sources,
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III Assess Superstep: Assess Superstep, Errors, Analysis of Data,
Practical Actions, Engineering a Practical Assess Superstep, 12
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8 IV Process Superstep : Data Vault, Time -Person -Object -Location -Event
Data Vault, Data Science Process, Data Science,
Transform Superstep : Transform Superstep , Building a Data
Warehouse, Transforming with Data Science, Hypothesis Testing,
Overfitting and Underfitting, Precision -Recall, Cross -Validation Test.
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V Transform Superstep: Univariate Analysis, Bivariate Analysis,
Multivariate Analysis, Linear Regression, Logistic Regression,
Clustering Techniques, ANOVA, Principal Component Analysis
(PCA), Decision Trees, Support Vector Machines, Networks, Clusters,
and Grids, Data Mining, Pattern Rec ognition, Machine Learning,
Bagging Data,Random Forests, Computer Vision (CV) , Natural
Language Processing (NLP), Neural Networks, TensorFlow.
Organize and Report Supersteps : Organize Superstep, Report
Superstep, Graphics, Pictures, Showing the Differenc e
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Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Practical Data Science Andreas François
Vermeulen APress 2018
2. Principles of Data Science Sinan Ozdemir PACKT 2016
3. Data Science from Scratch Joel Grus O’Reilly 2015
4. Data Science from Scratch
first Principle in python Joel Grus Shroff
Publishers 2017
5. Experimental Design in
Data science with Least
Resources N C Das Shroff
Publishers 2018
M. Sc (Information Technology) Semester – I
Course Name: Data Science Practical Course Code: PSIT1P2
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome Apply quantitative modeling and data analysis techniques to the
solution of real world business problems, communicate findings, and
effectively present results using data visualization techniques.
Recognize and analyze ethical issues in business related to intellectual
property, data security, integrity, and privacy.
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9 Apply ethical practices in everyday business activities and make well -
reasoned ethical business and data management decisions.
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 B ig Data.
Apply algorithms to build machine intelligence.
Demonstrate use of team work, leadership skills, decision making and
organization theory.
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10 M. Sc (Information Technology) Semester – I
Course Name: Cloud Computing Course Code: PSIT103
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives
To learn how to use Cloud Services.
To implement Virtualization.
To implement Task Scheduling algorithms.
Apply Map -Reduce concept to applications.
To build Private Cloud.
Broadly educate to know the impact of engineering on legal and
societal issues involved.
Unit Details Lectures
I Introduction to Cloud Computing: Introduction, Historical
developments, Building Cloud Computing Environments, Principles of
Parallel and Distributed Computing: Eras of Computing, Parallel v/s
distributed computing, Elements of Parallel Computing, Elements of
distributed computing, Technologies for distributed computing.
Virtualization: Introduction, Characteristics of virtualized
environments, Taxonomy of virtualization techniques, Virtuali zation
and cloud computing, Pros and cons of virtualization, Technology
examples. Logical Network Perimeter, Virtual Server, Cloud Storage
Device, Cloud usage monitor, Resource replication, Ready -made
environment.
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II Cloud Computing Architecture: Introduction, Fundamental concepts
and models, Roles and boundaries, Cloud Characteristics, Cloud
Delivery models, Cloud Deployment models, Economics of the cloud,
Open challenges. Fundamental Cloud Security: Basics, Threat
agents, Cloud security threats, additional considerations. Industrial
Platforms and New Developments: Amazon Web Services, Google
App Engine, Microsoft Azure.
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III Specialized Cloud Mechanisms: Automated Scaling listener, Load
Balancer, SLA monitor, Pay -per-use monitor, Audit monitor, fail over
system, Hypervisor, Resource Centre, Multidevice broker, State
Management Database. Cloud Management Mechanisms: Remote
administration system, Resource Ma nagement System, SLA
Management System, Billing Management System, Cloud Security
Mechanisms: Encryption, Hashing, Digital Signature, Public Key
Infrastructure (PKI), Identity and Access Management (IAM), Single
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11 Sign-On (SSO), Cloud -Based Security Groups, Hardened Virtual
Server Images
IV Fundamental Cloud Architectures: Workload Distribution
Architecture, Resource Pooling Architecture, 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 Recovery Architecture, Bare -Metal Provisioning
Architecture, Rapid Provisioning Architecture, Storage Workload
Management Architecture
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V Cloud Delivery Model Considerations: Cloud Delivery Models: The
Cloud Provider Perspective, Cloud Delivery Models: The Cloud
Consumer Perspective, Cost Metrics and Pricing Models : Business
Cost Metrics, Cloud Usage Cost Metrics, Cost Management
Considerations, Service Quality Metrics and SLAs: Service Quality
Metrics, SLA Guidelines
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Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Mastering Cloud
Computing Foundations and
Applications Programming Rajkumar Buyya,
Christian
Vecchiola, S.
Thamarai Selvi Elsevier - 2013
2. Cloud Computing
Concepts, Technology &
Architecture Thomas Erl,
Zaigham
Mahmood,
and Ricardo
Puttini Prentice
Hall - 2013
3. Distributed and Cloud
Computing, From Parallel
Processing to the Internet of
Things Kai Hwang, Jack
Dongarra, Geoffrey
Fox MK
Publishers -- 2012
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12 M. Sc (Information Technology) Semester – I
Course Name: Cloud Computing Practical Course Code: PSIT1P3
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome Analyze the Cloud computing setup with its vulnerabilities and
applications using different architectures.
Design different workflows according to requirements and apply
map reduce programming model.
Apply and design suitable Virtualization concept, Cloud Resource
Management and design scheduling algorithms.
Create combinatorial auctions for cloud resources and design
scheduling algorithms for computing clouds
Assess cloud Storage systems and Cloud security, the risks
involved, its impact and develop cloud application
Broadly educate to know the impact of engineering on legal and
societal issues involved in addressing the security issues of cloud
computing.
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13 M. Sc (Information Technology) Semester – I
Course Name: Soft Computing Techniques Course Code: PSIT104
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives • Soft computing concepts like fuzzy logic, neural networks and genetic
algorithm, where Artificial Intelligence is mother branch of all.
• All these techniques will be more effective to solve the problem
efficiently
Pre requisites Basic concepts of Artificial Intelligence. Knowledge of Algorithms
Unit Details Lectures
I Introduction of soft computing, soft computing vs. hard computing,
various types of soft computing techniques, Fuzzy Computing, Neural
Computing, Genetic Algorithms, Associative Memory, Adaptive
Resonance Theory, Classification, Clustering, Bayesian Networks,
Probabilistic rea soning, applications of soft computing.
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II Artificial Neural Network: Fundamental concept, Evolution of Neural
Networks, Basic Models, McCulloh -Pitts Neuron, Linear Separability,
Hebb Network.
Supervised Learning Network: Perceptron Networks, Adaptive Linear
Neuron, Multiple Adaptive Linear Neurons, Backpropagation Network,
Radial Basis Function, Time Delay Network, Functional Link Networks,
Tree Neural Network.
Associative Memory Networks: Training algorithm for pattern
Association, Autoassociative memory network, hetroassociative
memory network, bi -directional associative memory, Hopfield
networks, iterative autoassociative memory networks, temporal
associative memory networks.
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III UnSupervised Learning Networks: Fixed weight competitive nets,
Kohonen self -organizing feature maps, learning vectors quantization,
counter propogation networks, adaptive resonance theory networks.
Special Networks: Simulated annealing, Boltzman machine, Gaussian
Machine, Cauchy Machine, Probabilistic neural net, cascade correlation
network, cognition network, neo -cognition network, cellular neural
network, optical neural network
Third Generation Neu ral Networks:
Spiking Neural networks, convolutional neural networks, deep learning
neural networks, extreme learning machine model.
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14 IV Introduction to Fuzzy Logic, Classical Sets and Fuzzy sets:
Classical sets, Fuzzy sets.
Classical Relations and Fuzzy Relations:
Cartesian Product of relation, classical relation, fuzzy relations,
tolerance and equivalence relations, non -iterative fuzzy sets.
Membership Function: features of the membership functions,
fuzzification, methods of membershi p value assignments.
Defuzzification: Lambda -cuts for fuzzy sets, Lambda -cuts for fuzzy
relations, Defuzzification methods.
Fuzzy Arithmetic and Fuzzy measures: fuzzy arithmetic, fuzzy
measures, measures of fuzziness, fuzzy integrals.
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V Fuzzy Rule base and Approximate reasoning:
Fuzzy proportion, formation of rules, decomposition of rules,
aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems,
Fuzzy logic control systems, control system design, architecture and
operation of FLC system , FLC system models and applications of FLC
System.
Genetic Algorithm: Biological Background, Traditional optimization
and search techniques, genetic algorithm and search space, genetic
algorithm vs. traditional algorithms, basic terminologies, simple gene tic
algorithm, general genetic algorithm, operators in genetic algorithm,
stopping condition for genetic algorithm flow, constraints in genetic
algorithm, problem solving using genetic algorithm, the schema
theorem, classification of genetic algorithm, Hol land classifier systems,
genetic programming, advantages and limitations and applications of
genetic algorithm.
Differential Evolution Algorithm, Hybrid soft computing techniques –
neuro – fuzzy hybrid, genetic neuro -hybrid systems, genetic fuzzy
hybrid an d fuzzy genetic hybrid systems.
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Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Artificial Intelligence and
Soft Computing Anandita
Battacharya Das SPD 3rd 2018
2. Principles of Soft computing S.N.Sivanandam
S.N.Deepa Wiley 3rd 2019
3. Neuro -Fuzzy
Computing and Soft J.S.R.Jang,
C.T.Sun and
E.Mizutani Prentice
Hall of
India 2004
4. Neural Networks, Fuzzy
Logic and Genetic
Algorithms: Synthesis &
Applications S.Rajasekaran,
G. A.
Vijayalakshami Prentice
Hall of
India 2004
5. Fuzzy Logic with
Engineering Applications Timothy J.Ross McGraw -
Hill 1997
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15 6. Genetic Algorithms: Search,
Optimization and Machine
Learning Davis
E.Goldberg Addison
Wesley 1989
7. Introduction to AI and
Expert System Dan W.
Patterson Prentice
Hall of
India 2009
M. Sc (Information Technology) Semester – I
Course Name: Soft Computing Techniques
Practical Course Code: PSIT1P4
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome • 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 for cla ssification 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.
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SEMESTER II
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17 M. Sc (Information Technology) Semester – II
Course Name: BigData Analytics Course Code: PSIT201
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives To provide an overview of an exciting growing field of big data analytics.
To introduce the tools required to manage and analyze big data like
Hadoop, NoSql MapReduce.
To teach the fundamental techniques and principles in achieving big data
analytics with scalability and streaming capability.
To enable students to have skills that will help them to solve complex real -
world problems in for decision support.
Unit Details Lectures
I Introduction to Big Data, Characteristics of Data, and Big Data
Evolution of Big Data, Definition of Big Data, Challenges with big
data, Why Big data? Data Warehouse environment, Traditional
Business Intelligence versus Big Data. State of Practice in Analytics,
Key roles for New Bi g Data Ecosystems, Examples of big Data
Analytics.
Big Data Analytics, Introduction to big data analytics, Classification of
Analytics, Challenges of Big Data, Importance of Big Data, Big Data
Technologies, Data Science, Responsibilities, Soft state eventu al
consistency. Data Analytics Life Cycle
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II Analytical Theory and Methods: Clustering and Associated
Algorithms, Association Rules, Apriori Algorithm, Candidate Rules,
Applications of Association Rules, Validation and Testing,
Diagnostics, Regression, Linear Regression, Logistic Regression,
Additional Regression Models.
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III Analytical Theory and Methods: Classification, Decision Trees, Naïve
Bayes, Diagnostics of Classifiers, Additional Classification Methods,
Time Series Analysis, Box Jenkins methodology, ARIMA Model,
Additional methods. Text Analysis, Steps, Text Analysis Example,
Collecting Raw Text, Representing Text, Term Frequency -Inverse
Document Frequency (TFIDF), Categorizing Documents by Topics,
Determining Sentiments
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IV Data Product, Building Data Products at Scale with Hadoop, Data
Science Pipeline and Hadoop Ecosystem, Operating System for Big
Data, Concepts, Hadoop Architecture, Working with Distributed file
system, Working with Distributed Computation, Framework for Python
and Hadoop Streaming, Hadoop Streaming, MapReduce with Python,
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18 Advanced MapReduce. In -Memory Computing with Spark, Spark
Basics, Interactive Spark with PySpark, Writing Spark Applications,
V Distributed Analysis and Patterns, Computing with Keys, Design
Patterns, Last -Mile Analytics, Data Mining and Warehousing,
Structured Data Queries with Hive, HBase, Data Ingestion, Importing
Relational data with Sqoop, Injesting stream data with flume. Analytics
with higher level APIs, Pig, Spark’s higher level APIs.
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,
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Big Data and Analytics Subhashini
Chellappan
Seema Acharya Wiley First
2. Data Analytics with Hadoop
An Introduction for Data
Scientists Benjamin
Bengfort and
Jenny Kim O’Reilly 2016
3. Big Data and Hadoop V.K Jain Khanna
Publishing First 2018
M. Sc (Information Technology) Semester – II
Course Name: BigData Analytics Practical Course Code: PSIT2P1
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome • Understand the key issues in big data management and its
associated applications in intelligent business and scientific
computing.
• Acquire fundamental enabling techniques and scalable
algorithms like Hadoop, Map Reduce and NO SQL in big data
analytics.
• Interpret business models and scientific computing paradigms,
and apply software tools for big data analytics.
• Achieve adequate perspectives of big data analytics in various
applications like recommender systems, social media
applica tions etc.
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19 M. Sc (Information Technology) Semester – I
Course Name: Modern Networking Course Code: PSIT202
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives To understand the state -of-the-art in network protocols, architectures and
applications.
Analyze existing network protocols and networks.
Develop new protocols in networking
To understand how networking research is done
To investigate novel ideas in the area of Networking via term -long research
projects.
Pre requisites Fundamentals of Networking
Unit Details Lectures
I Modern Networking
Elements of Modern Networking
The Networking Ecosystem ,Example Network Architectures,Global
Network Architecture,A Typical Network Hierarchy Ethernet
Applications of Ethernet Standards Ethernet Data Rates Wi -Fi
Applications of Wi-Fi,Standards Wi-Fi Data Rates 4G/5G Cellular First
Generation Second Generation, Third Generation Fourth Generation
Fifth Generation, Cloud Computing Cloud Computing Concepts The
Benefits of Cloud Computing Cloud Networking Cloud Storage,
Internet of Things Things on the Internet of Things, Evolution Layers
of the Internet of Things, Network Convergen ce Unified
Communications, Requirements and Technology Types of Network
and Internet Traffic,Elastic Traffic,Inelastic Traffic, Real -Time Traffic
Characteristics Demand: Big Data, Cloud Computing, and Mobile
TrafficBig Data Cloud Computing,,Mobile Traffic, Requirements:
QoS and QoE,,Quality of Service,Quality of Experience, Routing
Characteristics, Packet Forwarding, Congestion Control ,Effects of
Congestion,Congestion Control Techniques, SDN and NFV Software -
Defined Networking,Network Functions Virtualiza tion Modern
Networking Elements
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II Software -Defined Networks
SDN: Background and Motivation, Evolving Network Requirements
Demand Is Increasing,Supply Is IncreasingTraffic Patterns Are More
ComplexTraditional Network Architectures are Inadequate, The SDN
Approach Requirements SDN Architecture Characteristics of Software -
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20 Defined Networking, SDN - and NFV -Related Standards Standards -
Developing Organizations Industry Consortia Open Development
Initiatives, SDN Data Plane and OpenFlow SDN Data Plane, Data
Plane Functions Data Plane Protocols OpenFlow Logical Network
Device Flow Table Structure Flow Table Pipeline, The Use of Multiple
Tables Group Table OpenFlow Protocol, SDN Control Plane
SDN Control Plane Architecture Control Plane Functions, Southbound
Interface Northbound InterfaceRouting, ITU -T Model, OpenDaylight
OpenDaylight Architecture OpenDaylight Helium, REST REST
Constraints Example REST API, Cooperation and Coordination
Among Controllers, Centralized Versus Distributed Controllers, High -
Availability Clusters Federated SDN Networks, Border Gateway
Protocol Routing and QoS Between Domains, Using BGP for QoS
Management IETF SD Ni OpenDaylight SNDi SDN Application Plane
SDN Application Plane Architecture Northbound Interface Network
Services Abstraction Layer Network Applications, User Interface,
Network Services Abstraction Layer Abstractions in SDN, Frenetic
Traffic Engineering PolicyCop Measurement and Monitoring Security
OpenDaylight DDoS Application Data Center Networking, Big Data
over SDN Cloud Networking over SDN Mobility and Wireless
Information -Centric Networking CCNx, Use of an Abstraction Layer
III Virtualization, Network Functions Virtualization: Concepts and
Architecture, Background and Motivation for NFV, Virtual Machines
The Virtual Machine Monitor, Architectural Approaches Container
Virtualization, NFV Concepts Simple Example of the Use of NFV,
NFV Principles H igh-Level NFV Framework, NFV Benefits and
Requirements NFV Benefits, NFV Requirements, NFV Reference
Architecture NFV Management and Orchestration, Reference Points
Implementation, NFV Functionality, NFV Infrastructure,Container
Interface,Deployment of NFVI Containers,Logical Structure of NFVI
Domains,Compute Domain, Hypervisor Domain,Infrastructure
Network Domain, Virtualized Network Functions, VNF
Interfaces,VNFC to VNFC Communication,VNF Scaling, NFV
Management and Orchestration, Virtualize d Infrastructure
Manager,Virtual Network Function Manager,NFV Orchestrator,
Repositories, Element Management, OSS/BSS, NFV Use Cases
Architectural Use Cases, Service -Oriented Use Cases, SDN and NFV
Network Virtualization, Virtual LANs ,The Use of Virtual
LANs,Defining VLANs, Communicating VLAN Membership,IEEE
802.1Q VLAN Standard, Nested VLANs, OpenFlow VLAN Support,
Virtual Private Networks, IPsec VPNs,MPLS VPNs, Network
Virtualization, Simplified Example, Network Virtualization
Archite cture, Benefits of Network Virtualization, OpenDaylight’s
Virtual Tenant Network, Software -Defined Infrastructure,Software -
Defined Storage, SDI Architecture
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21 IV Defining and Supporting User Needs, Quality of Service, Background,
QoS Architectural Framework, Data Plane, Control Plane, Management
Plane, Integrated Services Architecture, ISA Approach
ISA Components, ISA Services, Queuing Discipline, Differentiated
Services, Services, DiffServ Field, DiffServ Configuration and
Operati on, Per -Hop Behavior, Default Forwarding PHB, Service Level
Agreements, IP Performance Metrics, OpenFlow QoS Support, Queue
Structures, Meters, QoE: User Quality of Experience, Why
QoE?,Online Video Content Delivery, Service Failures Due to
Inadequate QoE Considerations QoE -Related Standardization Projects,
Definition of Quality of Experience, Definition of Quality, Definition
of Experience Quality Formation Process, Definition of Quality of
Experience, QoE Strategies in Practice, The QoE/QoS Layered Model
Summarizing and Merging the ,QoE/QoS Layers, Factors Influencing
QoE, Measurements of QoE, Subjective Assessment, Objective
Assessment, End -User Device Analytics, Summarizing the QoE
Measurement Methods, Applications of QoE Network Design
Implications of Q oS and QoE Classification of QoE/ QoS Mapping
Models, Black -Box Media -Based QoS/QoE Mapping Models, Glass -
Box Parameter -Based QoS/QoE Mapping Models,Gray -Box QoS/QoE
Mapping Models, Tips for QoS/QoE Mapping Model Selection,IP -
Oriented Parameter -Based QoS /QoE Mapping Models,Network Layer
QoE/QoS Mapping Models for Video Services, Application Layer
QoE/QoS Mapping Models for Video Services Actionable QoE over
IP-Based Networks, The System -Oriented Actionable QoE Solution,
The Service -Oriented Actionable QoE Solution, QoE Versus QoS
Service Monitoring, QoS Monitoring Solutions, QoE Monitoring
Solutions, QoE -Based Network and Service Management, QoE -Based
Management of VoIP Calls, QoE-Based Host -Centric Vertical
Handover, QoE -Based Network -Centric Vertical Han dover
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V Modern Network Architecture: Clouds and Fog, Cloud Computing,
Basic Concepts, Cloud Services, Software as a Service, Platform as a
Service, Infrastructure as a Service, Other Cloud Services, XaaS, Cloud
Deployment Models, Public Cloud Private Cloud Community Cloud,
Hybrid Cloud, Cloud Architecture, NIST Cloud Computing Reference
Architecture,ITU -T Cloud Computing Reference Architecture, SDN and
NFV, Service Provider Perspective Private Cloud Perspec tive, ITU -T
Cloud Computing Functional Reference Architecture, The Internet of
Things: Components The IoT Era Begins, The Scope of the Internet of
Things Components of IoT -Enabled Things, Sensors, Actuators,
Microcontrollers, Transceivers, RFID, The Intern et of Things:
Architecture and Implementation, IoT Architecture,ITU -T IoT
Reference Model, IoT World Forum Reference Model, IoT
Implementation, IoTivity, Cisco IoT System, ioBridge, Security
Security Requirements, SDN Security Threats to SDN, Software -
Defined Security, NFV Security, Attack Surfaces, ETSI Security
Perspective, Security Techniques, Cloud Security, Security Issues and
Concerns, Cloud Security Risks and Countermeasures, Data Protection
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22 in the Cloud, Cloud Security as a Service, Addressing Cloud Computer
Security Concerns, IoT Security, The Patching Vulnerability, IoT
Security and Privacy Requirements Defined by ITU -TAn IoT Security
Framework, Conclusion
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Foundations of Modern
Networking: SDN, NFV,
QoE, IoT, and Cloud William
Stallings Addison -
Wesley
Professional October
2015
2. SDN and NFV Simplified
A Visual Guide to
Understanding Software
Defined Networks and
Network Function
Virtualization Jim Doherty Pearson
Education,
Inc
3. Network Functions
Virtualization (NFV)
with a Touch of SDN Rajendra
Chayapathi
Syed Farrukh
Hassan Addison -
Wesley
4. CCIE and CCDE Evolving
Technologies Study
Guide Brad dgeworth,
Jason Gooley,
Ramiro Garza
Rios Pearson
Education,
Inc 2019
M. Sc (Information Technology) Semester – II
Course Name: Modern Networking Practical Course Code: PSIT2P2
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome Demonstrate in -depth knowledge in the area of Computer Networking.
To demonstrate scholarship of knowledge through performing in a group
to identify, formulate and solve a problem related to Computer Networks
Prepare a technical document for the identified Networking System
Conducting experiments to analyze the identified research work in
building Computer Networks
Page 25
23 M. Sc (Information Technology) Semester – I
Course Name: Microservice Architecture Course Code: PSIT203
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives
Gain a thorough understanding of the philosophy and architecture of
Web applications using ASP.NET Core MVC;
Gain a practical understanding of.NET Core;
Acquire a working knowledge of Web application development using
ASP.NET Core MVC 6 and Visual Studio
Persist data with XML Serialization and ADO.NET with SQL Server
Create HTTP services using ASP.NET Core Web API;
Deploy ASP.NET Core MVC applications to the Windows Azure
cloud.
Unit Details Lectures
I Microservices : Understanding Microservices, Adopting
Microservices, The Microservices Way. Microservices Value
Proposition: Deriving Business Value, defining a Goal -Oriented,
Layered Approach, Applying the Goal -Oriented, Layered Approach.
Designing Microservice Systems : The Systems Approach to
Microservices, A Microservices Design Process, Establishing a
Foundation: Goals and Principles, Platforms, Culture.
12
II Service Design: Microservice Boundaries, API design for
Microservices, Data and Microservices , Distributed Transactions and
Sagas, Asynchronous Message -Passing and Microservices, dealing
with Dependencies, System Design and Operations: Independent
Deployability, More Servers, Docker and Microservices, Role of
Service Discovery, Need for an API Gateway, Monitoring and Alerting.
Adopting Microservices in Practice: Solution Architecture Guidance,
Organizational Guidance, Culture Guidance, Tools and Process
Guidance, Services Guidance.
12
III Building Microservices with ASP.NET Core: Introduction,
Installing .NET Core, Building a Console App, Building ASP.NET
Core App. Delivering Continuously: Introduction to Docker,
Continuous integration with Wercker, Continuous Integration with
Circle CI, Deploying to Dicker Hub. Building Microservi ce with
ASP.NET Core: Microservice, Team Service, API First Development,
Test First Controller, Creating a CI pipeline, Integration Testing,
Running the team service Docker Image. Backing Services:
12
Page 26
24 Microservices Ecosystems, Building the location Service, Enhancing
Team Service.
IV Creating Data Service: Choosing a Data Store , Building a Postgres
Repository, Databases are Backing Services, Integration Testing Real
Repositories, Exercise the Data Service. Event Sourcing and CQRS:
Event Sourcing, CQRS pattern, Event Sourcing and CQRS, Running
the samples. Building an ASP.NET Core Web Application:
ASP.NET Core Basics, Building Cloud -Native Web Applications.
Service Discovery: Cloud Native Factors, Netflix Eureka, Discovering
and Advertising ASP.NET Core Services. DNS and Platform Supported
Discovery.
12
V Configuring Microservice Ecosystems: Using Environment
Variables with Docker, Using Spring Cloud Config Server, Configuring
Microservices with etcd, Securing Applications and Microservices:
Security in the Cloud, Securing ASP.NET Core Web Apps, Securing
ASP.NET Core Microservices. Building Real -Time Apps and
Services: Real-Time Applications Defined, Websockets in the Cloud,
Using a Cloud Messaging Prov ider, Building the Proximity Monitor.
Putting It All Together: Identifying and Fixing Anti -Patterns,
Continuing the Debate over Composite Microservices, The Future.
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Microservice Architecture:
Aligning Principles,
Practices, and Culture Irakli
Nadareishvili,
Ronnie Mitra,
Matt McLarty,
and Mike
Amundsen O’Reilly First 2016
2. Building Microservices with
ASP.NET Core Kevin Hoffman O’Reilly First 2017
3. Building Microservices:
Designing Fine -Grained
Systems Sam Newman O’Reilly First
4. Production -ready
Microservices Susan J. Fowler O’Reilly 2016
Page 27
25 M. Sc (Information Technology) Semester – II
Course Name: Microservices Architecture
Practical Course Code: PSIT2P3
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Course Outcome
Develop web applications using Model View Control.
Create MVC Models and write code that implements business logic
within Model methods, properties, and events.
Create Views in an MVC application that display and edit data and
interact with Models and Controllers.
Boost your hire ability through innovative and independent
learning.
Gaining a thorough understanding of the philosophy and
architecture of .NET Core
Understanding packages, metapackages and frameworks
Acquiring a working knowledge of the .NET programming model
Implementing multi -threading effectively in .NET applications
Page 28
26 M. Sc (Information Technology) Semester – II
Course Name: Image Processing Course Code: PSIT204
Periods per week
1 Period is 60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40
Objectives Review the fundamental concepts of a digital image processing
system.
Analyze images in the frequency domain using various transforms.
Evaluate the techniques for image enhancement and image restoration.
Categorize various compression techniques.
Interpret Image compression standards.
Interpret image segmentation and representation techniques.
Unit Details Lectures
I Introduction: Digital Image Processing, Origins of Digital Image Processing,
Applications and Examples of Digital Image Processing, Fundamental Steps
in Digital Image Processing, Components of an Image Processing System,
Digital Image Fundamentals: Elements of Visual Perception, Light and the
Electromagnetic Spectrum, I mage Sensing and Acquisition, Image Sampling
and Quantization, Basic Relationships Between Pixels, Basic Mathematical
Tools Used in Digital Image Processing, Intensity Transformations and
Spatial Filtering: Basics, Basic Intensity Transformation Functions, Basic
Intensity Transformation Functions, Histogram Processing, Fundamentals of
Spatial Filtering, Smoothing (Lowpass) Spatial Filters, Sharpening
(Highpass) Spatial Filters, Highpass, Bandreject, and Bandpass Filters from
Lowpass Filters, Combining Spati al Enhancement Methods, Using Fuzzy
Techniques for Intensity Transformations and Spatial Filtering
12
II Filtering in the Frequency Domain: Background, Preliminary Concepts,
Sampling and the Fourier Transform of Sampled Functions, The Discrete
Fourier Transform of One Variable, Extensions to Functions of Two
Variables, Properties of the 2 -D DFT and IDFT, Basics of Filtering in the
Frequency Do main, Image Smoothing Using Lowpass Frequency Domain
Filters, Image Sharpening Using Highpass Filters, Selective Filtering, Fast
Fourier Transform
Image Restoration and Reconstruction: A Model of the Image
Degradation/Restoration Process, Noise Models, Res toration in the Presence
of Noise Only -----Spatial Filtering, Periodic Noise Reduction Using
Frequency Domain Filtering, Linear, Position -Invariant Degradations,
Estimating the Degradation Function, Inverse Filtering, Minimum Mean
Square Error (Wiener) Fil tering, Constrained Least Squares Filtering,
Geometric Mean Filter, Image Reconstruction from Projections
12
III Wavelet and Other Image Transforms: Preliminaries, Matrix -based
Transforms, Correlation, Basis Functions in the Time -Frequency Plane, Basis 12
Page 29
27 Images, Fourier -Related Transforms, Walsh -Hadamard Transforms, Slant
Transform, Haar Transform, Wavelet Transforms
Color Image Processing: Color Fundamentals, Color Models, Pseudocolor
Image Processing, Full -Color Image Proces sing, Color Transformations,
Color Image Smoothing and Sharpening, Using Color in Image Segmentation,
Noise in Color Images, Color Image Compression.
Image Compression and Watermarking: Fundamentals, Huffman Coding,
Golomb Coding, Arithmetic Coding, LZW Coding, Run -length Coding,
Symbol -based Coding, 8 Bit -plane Coding, Block Transform Coding,
Predictive Coding, Wavelet Coding, Digital Image Watermarking,
IV Morphological Image Processing: Preliminaries, Erosion and Dilat ion,
Opening and Closing, The Hit -or-Miss Transform, Morphological
Algorithms, Morphological Reconstruction¸ Morphological Operations on
Binary Images, Grayscale Morphology
Image Segmentation I: Edge Detection, Thresholding, and Region
Detection: Fundament als, Thresholding, Segmentation by Region Growing
and by Region Splitting and Merging, Region Segmentation Using Clustering
and Superpixels, Region Segmentation Using Graph Cuts, Segmentation
Using Morphological Watersheds, Use of Motion in Segmentation
12
V Image Segmentation II: Active Contours: Snakes and Level Sets:
Background, Image Segmentation Using Snakes, Segmentation Using Level
Sets.
Feature Extraction: Background, Boundary Preprocessing, Boundary
Feature Descriptors, Region Feature Descriptors, Principal Components as
Feature Descriptors, Whole -Image Features, Scale -Invariant Feature
Transform (SIFT)
12
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Digital Image Processing Gonzalez and
Woods Pearson/Prentice
Hall Fourth 2018
2. Fundamentals of Digital
Image Processing A K. Jain PHI
3. The Image Processing
Handbook J. C. Russ CRC Fifth 2010
M. Sc (Information Technology) Semester – II
Course Name: Image Processing Practical Course Code: PSIT2P4
Periods per week
1 Period is 60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40
Practical No Details
1 - 10 10 Practical based on above syllabus, covering entire syllabus
Page 30
28 Course Outcome Understand the relevant aspects of digital image representation and
their practical implications.
Have the ability to design pointwise intensity transformations to meet
stated specifications.
Understand 2 -D convolution, the 2 -D DFT, and have the abitilty to
design systems using these concepts.
Have a command of basic image restoration techniques.
Understand the role of alternative color spaces, and the design
requirements leading to choices of color space.
Appreciate the utility of wavelet decompositions and their role in image
processing systems.
Have an understanding of the underlying mechanisms of image
compression, and the ability to design systems using standard
algorithms to meet design specifications.
Page 31
29 Evaluation Scheme
Internal Evaluation (40 Marks)
The internal assessment marks shall be awarded as follows:
1. 30 marks (Any one of the following):
a. Written Test or
b. SWAYAM (Advanced Course) of minimum 20 hours and certification exam
completed or
c. NPTEL (Advanced Course) of minimum 20 hou rs and certification exam
completed or
d. Valid International Certifications (Prometric, Pearson, Certiport, Coursera,
Udemy and the like)
e. One certification marks shall be awarded one course only. For four courses,
the students will have to complete four certifications.
2. 10 marks
The marks given out of 40 for publishing the research paper should be divided into
four course and should awarded out of 10 in each of the four course.
i. Suggested format of Question paper of 30 marks for the written test.
Q1. Attempt any two of the following: 16
a.
b.
c.
d.
Q2. Attempt any two of the following: 14
a.
b.
c.
d.
ii. 10 marks from every course coming to a total of 40 marks, shall be awarded on
publishing of research paper in UGC approved Journal with plagiarism less than
10%. The marks can be awarded as per the impact factor of the journal, quality of
the paper, importance of the contents published, social value.
Page 32
30 External Examination: (60 marks)
All questions are compulsory
Q1 (Based on Unit 1) Attempt any two of the following: 12
a.
b.
c.
d.
Q2 (Based on Unit 2) Attempt any two of the following: 12
Q3 (Based on Unit 3) Attempt any two of the following: 12
Q4 (Based on Unit 4) Attempt any two of the following: 12
Q5 (Based on Unit 5) Attempt any two of the following: 12
Practical Evaluation (50 marks)
A Certified copy journal is essential to appear for the practical examination.
1. Practical Question 1 20
2. Practical Question 2 20
3. Journal 5
4. Viva Voce 5
OR
1. Practical Question 40
2. Journal 5
3. Viva Voce 5