MVN University, Haryana

Department of Computer Science and Engineering

Master of Technology (Full Time) in Computer Science and Engineering

Semester-I

S.No Name of subject Sub code Teaching Schedule Total Credit

L T P

1 Mathematical Foundations for Data Science-1 CSL-501-20 3 0 0 3 3

2 Python Programming CSL-503-20 3 0 0 3 3

3 Advance Networking CSL-505-20 3 0 0 3 3

4 Internet Of Things CSL-507-20 3 0 0 3 3

5 Python Programming Lab CSP-503-20 0 0 4 2 2

6 Research and Publication Ethics CSL-509-20 3 0 0 3 3

Total 15 0 2 17 16

Semester-II

S.No Name of subject Sub code Teaching Schedule

Total Credit

L T P

1 Mathematical Foundations for Data Science-2 CSL-502-20 3 0 0 3 3

2 Data Structures using Python CSL-504-20 3 0 0 3 3

3 Next Generation Data Bases CSL-506-20 3 0 0 3 3

4 IoT Edge Nodes CSL-508-20 3 0 0 3 3

5 Next Generation Data Bases Lab CSP-506-20 0 0 2 2 1

6 Data Structures using Python Lab CSP-504-20 0 0 2 2 1

7 Elective - 1* 3 0 0 3 3

8 Term Paper-1 CST-510-20 0 0 4 4 2

Total 15 0 8 23 19

(1*) Elective-1

S.No Name of subject Sub code Teaching Schedule

Total Credit

L T P

1 Software Testing CSL-516-20 3 0 0 3 3

2 Big Data Basics CSL-518-20 3 0 0 3 3

3 Cyber security CSL-522-20 3 0 0 3 3

4 Optimization Techniques CSL-524-20 3 0 0 3 3

Total 3 0 0 3 3

MVN University, Haryana

Semester-III

S.No Name of subject Sub code Teaching Schedule

Total Credit

L T P

1 Data Analysis with Python CSL-511-20 3 0 0 3 3

2 Artificial and Computation Intelligence CSL-513-20 3 0 0 3 3

3 Data Analysis with Python Lab CSP-511-20 0 0 2 2 1

4 Artificial and Computation Intelligence Lab CSP-513-20 0 0 2 2 1

5 Elective- 2* 3 0 0 3 3

6 Term Paper-2 CST-515-20 0 0 4 4 2

Total 9 0 8 17 13

(2*) Elective-2

S.No Name of subject Sub code Teaching Schedule

Total Credit

L T P

1 Block chain CSL-517-20 3 0 0 3 3

2 Cloud Computing CSL-519-20 3 0 0 3 3

3 Computer vision and computer vision Lab CSL-521-20 3 0 0 3 3

Total 3 0 0 3 3

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

M.TECH(CSE)

1st sem

CSL-501-20 Mathematical Foundations for Data

Science-I

L T P Cr.

3 0 0 3

Objective: The objective of this course is to provide a conceptual understanding of data science.

Unit I:The Art and Science of learning from Data: Using Data to answer Statistical questions, Sample verses

Population, using calculators and computers.

Unit II:Exploring Data with Graphs and Numerical summaries: Types of Data, Graphical summaries of

Data, Measuring the center of Quantitative Data, Measuring the variability of Quantitative Data, Using

Measures of position to describe Variability, Recognizing and Avoiding the misuses of Graphical

Summaries.

Unit III: Association: Contingency, Correlation, and Regression: The association between two Categorical

variables, The Association between two Quantitative variables, Predicting the outcome of a variable,

Cautions in analyzing Association.

Unit IV:Gathering Data: Experimental and Observational Studies, Good and Poor ways to Sample, Good

and Poor ways to Experiment, Other ways to conduct Experimental and Non-Experimental Studies.

Unit V: Probability in our daily Life: How Probability Quantifies Randomness, Finding Probabilities,

Conditional Probability, Applying the Probability Rules,

Unit VI:Probability Distributions: Summarizing possible Outcomes, their Probabilities, Probability for Bell-

Shaped Distributions, Probability when each Observation has two possible outcomes.

References:

1. A. Agresti, C. Franklin and B. Klingenberg: Statistics: The art and Science of learning from Data, Pearson.

2. J. K. Sharma: Business Statistics,Viks Publications.

3. S.C. Gupta and V. K. Kapoor: Fundamentals of Mathematical Statistics. S. Chand and Sons.a

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

M.TECH(CSE)

1st sem

CSL-505-20 Internet of Things L T P Cr.

3 0 0

Objective: The objective of this course is to provide a conceptual understanding of Internet ofThings.

Objective

The goal of this course is to provide an introduction to IoT. The course will discuss topics necessary for the

participant to be able to understand the basic structure of IoT. The syllabus is intended to impart basic

knowledge about architecture, protocols and security in IoT.

Note:Total five questions are to be attempted .

Unit 1 Introduction to Internet of Things:

Definition and Characteristics of IoT, Physical Design of IoT – IoT Protocols, IoT communication models, Iot

Communication APIs IoT enabaled Technologies – Wireless Sensor Networks, Cloud Computing, Big data

analytics, Communication protocols, Embedded Systems, IoT Levels and Templates Domain Specific IoTs –

Home, City, Environment, Energy, Retail, Logistics, Agriculture, Industry, health and Lifestyle

Unit 2 IoT and M2M:

Software defined networks, network function virtualization, difference between SDN and NFV for IoT

Basics of IoT System Management with NETCOZF, YANG- NETCONF, YANG, SNMP NETOPEER

Unit 3 IoT Data Link Layer & Network Layer Protocols:

PHY/MAC Layer (3GPP MTC, IEEE 802.11, IEEE 802.15), Wireless HART, Z-Wave, Bluetooth Low Energy,

Zigbee Smart Energy, DASH7 - Network Layer-IPv4, IPv6, 6LoWPAN, 6TiSCH,ND, DHCP, ICMP, RPL, CORPL,

CARP.

Unit 4 Transport & Session Layer Protocols:

Transport Layer (TCP, MPTCP, UDP, DCCP, SCTP)-(TLS, DTLS) – Session Layer-HTTP, CoAP, XMPP, AMQP,

MQTT.

MVN University, Haryana

Unit 5 Service Layer Protocols & Security:

Service Layer -oneM2M, ETSI M2M, OMA, BBF – Security in IoT Protocols – MAC 802.15.4, 6LoWPAN, RPL,

Application Layer

Unit 6 IoT Security:

Need for encryption, standard encryption protocol, light weight cryptography, Quadruple Trust Model for

IoT-A – Threat Analysis and model for IoT-A, Cloud security

Text Book:

1. Jan Holler, VlasiosTsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos, David Boyle, “From

Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence”, 1st Edition, Academic

Press, 2014.

2. Peter Waher, “Learning Internet of Things”, PACKT publishing, BIRMINGHAM – MUMBAI

3. Bernd Scholz-Reiter, Florian Michahelles, “Architecting the Internet of Things”, ISBN 978-3-642-19156-5 e-ISBN

978-3-642-19157-2, Springer

4. Daniel Minoli, “Building the Internet of Things with IPv6 and MIPv6: The Evolving World of M2M

Communications”, ISBN: 978-1-118- 47347-4, Willy Publications

5. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on Approach)”, 1st Edition, VPT, 2014.

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

M.TECH(CSE)

1st sem

CSL-505-20 Advance Networking L T P Cr.

3 0 0 3

Objective: The objective of this course is to provide a conceptual understanding of Internet ofThings.

Objective

The goal of this course is to provide

Note:Total five questions are to be attempted .

Unit 1 Wireless LAN and WAN technologies:

Introduction to Wireless networks : Infrastructure networks, Ad-hoc networks, IEEE 802.11

architecture and services, Medium Access Control sub-layers, CSMA/CA Physical Layer, 802.11 Security

considerations .Asynchronous Transfer Mode (ATM): Architecture, ATM logical connections, ATM cells ,

ATM Functional Layers, Congestion control and Quality of service

Unit 2 Emerging Wireless Technologies:

WPAN 802.15.1 architecture ,Bluetooth Protocol Stack, Bluetooth Link Types, Bluetooth Security, Network

Connection Establishment in Bluetooth, Network Topology in Bluetooth, Bluetooth Usage

Models,802.15.3- Ultra Wide Band , 802.15.4- Zigbee , RFID,Wireless Sensor Networks: Introduction and

Applications, Wireless Sensor Network Model, Sensor Network Protocol Stack

Unit 3 Optical Networking:

SONET : SONET/SDH, Architecture, Signal, SONET devices, connections, SONET layers, SONET frames, STS

Multiplexing, SONET Networks,DWDM: Frame format, DWDM architecture ,Optical Amplifier , Optical

cross connect Performance and design considerations

Unit 4 Network Design, Security and Management:

3 tier Network design layers: Application layer, Access layer, Backbone layers, Ubiquitous computing and

Hierarchical computing,Network Security: Security goal, Security threats, security safeguards, firewall

types and design,Network management definitions, functional areas (FCAPS), SNMP,RMON

Unit 5 Routing in the Internet:

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Intra and inter domain Routing, Unicast Routing Protocols: RIP, OSPF, BGP,Multicast Routing Protocols:

,Drawbacks of traditional Routing methods.

Unit 6 Cloud computing

Cloud Computing Evolution, Definition, SPI framework of Cloud Computing, Cloud service delivery

models,Cloud deployment models, key drivers to adoption of cloud, impact of cloud computing on users,

examples of cloud service providers: Amazon, Google, Microsoft, Salesforce etc.

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

M.TECH(CSE)

1st sem

CSL-503-20 Python Programming

L T P

Cr

3 1 0 4

Objective

The goal of this course is to provide an introduction to Python. The course will discuss topicsnecessary for

the participant to be able to create and execute Python programs. The lectures and presentations are

designed to provide knowledge and experiences to students that serve as a foundation for continued

learning of presented area.

Note: Total five questions are to be attempted .

Unit 1 Rapid Introduction to Procedural Programming, Data Types :

Creating and Running Python Programs, Data Types, Object References, Collection Data Types, Logical

Operations, Control Flow Statements, Arithmetic Operators, Input/Output basic functions.

Identifiers and Keywords, Integral Types, Integers, Booleans ,Floating-Point Types , Floating-Point

Numbers, Complex Numbers ,Decimal Numbers , Strings , Comparing Strings , Slicing and Striding Strings,

String Operators and Methods, String Formatting with the str.format() Method.

Unit 2 Collection Data Types, Control Structures and Functions :

Sequence Types , Tuples , Named Tuples , Lists , Set Types , Sets , Frozen Sets , Mapping Types, Dictionaries

, Default Dictionaries, Ordered Dictionaries, Iterating and Copying Collections, Iterators and Iterable

Operations and Functions, Copying Collections.

Control Structures, Conditional Branching, Looping, Creating and Calling Functions, Custom Functions,

Accessing Variables in the Global Scope, Lambda Functions, functions as data,using command line

arguments

Unit 3 Modules and packages:

Modules and Packages , some important package like numpy,pandas, Custom Modules , Overview of

Python’s Standard Library, String Handling, Mathematics and Numbers , Times and Dates , Algorithms and

Collection Data Types, Encodings .

MVN University, Haryana

Unit 4 Object-Oriented Programming:

The Object-Oriented Approach, Object-Oriented Concepts and Terminology, Custom Classes, Attributes

and Methods, Inheritance and Polymorphism, Encapsulation, Exception handling

Unit 5 Multithreading and File Handling:

Thread ,Starting a thread, Threading module, Synchronization in threads Types of files supported by

Python, File operations using Python, Python, Reading, Looping over a file object in Python, File Write

method in Python, Splitting text file.

Unit 6 Database Programming and Regular expressions :

Benefits of Python for database programming, DB-API (SQL-API) for Python, Connection objects, Cursor

objects, Python and MySQL, SQL operations.Working with RegEx module, RegEx Functions,

Metacharacters,Special Sequences

Text Book:

Programming in Python 3, A Complete Introduction to the Python Language, Second Edition, Mark

Summerfield, Addison-Wesley

Reference Books:

1. Introducing Python Paperback – 2014 by Lubanovic Bill (Author) by Sedgewick/ Wayne/ Dondero

2. Introduction to Programming in Python 1 Paperback – 29 Jul 2016

3. "Doing Math with Python" by Amit Saha

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

M.TECH(CSE)

1st sem

CSP-503-20 Python Programming Lab

L T P

Cr

0 0 2 1

Course objectives:

Interpret the use of procedural statements like assignments, conditional statements, loops and function

calls.Infer the supported data structures like lists, dictionaries and tuples in Python.Illustrate the

application of matrices and regular expressions in building the Python Programs.Discover the use of

external modules in creating excel files and navigating the file systems.Describe the need for Object-

oriented programming concepts in Python.

1a) Write a Python program to print all the Disarium numbers between 1 and 100.

b) Write a Python program to encrypt the text using Caesar Cipher technique. Display the encrypted text.

Prompt the user for input and the shift pattern.

2.Devise a Python program to implement the Rock-Paper-Scissor game.

3.Write a Python program to perform Jump Search for a given key and report success or failure. Prompt

the user to enter the key and a list of numbers.

4.The celebrity problem is the problem of finding the celebrity among n people. A celebrity is someone

who does not know anyone (including themselves) but is known by everyone. Write a Python program to

solve the celebrity problem.

5.Write a Python program to construct a linked list. Prompt the user for input. Remove any duplicate

numbers from the linked list.

6.Perform the following file operations using Python

a) Traverse a path and display all the files and subdirectories in each level till the deepest level for a

given path. Also, display the total number of files and subdirectories.

b) Read a file content and copy only the contents at odd lines into a new file.

MVN University, Haryana

7.Create a menu drive Python program with a dictionary for words and their meanings. Write functions

to add a new entry (word: meaning), search for a particular word and retrieve meaning, given meaning

find words with the same meaning, remove an entry, display all words sorted alphabetically.

8.Using Regular Expressions, develop a Python program to

a) Identify a word with a sequence of one upper case letter followed by lower case letters.

b) Find all the patterns of “1(0+)1” in a given string.

c) Match a word containing ‘z’ followed by one or more o’s.

Prompt the user for input.

9.Write a Python program to plot the Line chart in MS Excel Sheet using XlsxWriter module to display the

annual net income of the companies mentioned below.

10.Devise a Python program to implement the Hangman Game.

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE)

M. Tech CSE II Semester

CSL-504-20 Data Structures Using Python L T P Credits

3 0 2 4

Unit 1: Introduction to Data Structures: Concept Data Structure, Need of Data structure, Advantages of

using DS, Types of data structures; Built-in Data Structures- List, Dictionary, Tuple, Sets, User-Defined

Data Structures-Arrays vs. List, Stack, Queue, Trees, Linked Lists, Graphs, Hash Maps, Booleans, integers,

floating point numbers, and characters -- into more complex strings.

Unit 2: Strings and Regular expression: Introduction, different ways of creating a string, Internal

implementation of String, string formatting, built-in functions in string, accessing individual character of a

string, string comparison, string concatenation. Regular Expression: Operations, Using Special Characters,

Regular Expression Methods, Named Groups in Python Regular Expressions, Regular Expression with glob

Module.

Unit 3: Lists: Introduction and types of list, Creating Lists, Basic List Operations, Indexing and Slicing in

Lists, Built-In Functions Used on Lists, List Methods, The del Statement.

Unit 4: Dictionaries, Tuples and Set: Creating Dictionary, Accessing and Modifying key: value Pairs in

Dictionaries, Built-In Functions Used on Dictionaries, Dictionary Methods, the del Statement, Tuples and

Sets: Creating Tuples, Basic Tuple Operations, Indexing and Slicing in Tuples, Built-In Functions Used on

Tuples, Relation between Tuples and Lists, Relation between Tuples and Dictionaries, Tuple Methods,

using zip () Function, Sets, Set Methods, Traversing of Sets, Frozen set.

Unit 5: Database Connectivity: Introduction to database connectivity using python, Types of database

connectivity-ODBC & PDBC, Creating the connection with database using MYSQL connector, Python

database API (PD-API) and Practical implementation of MYSQL connector in python, introduction to

Python MongoDB Connectivity, introduction to Python MY Connectivity

Unit-6: Introduction to Numpy Module: Introduction to numpy, Creating Arrays, Using arrays and

scalars, Indexing Arrays, Array Transposition, Universal Array Function, Array Processing, Array Input

and Output.

Suggested Readings

Text Book:

1. Data Structures and Algorithms in Python Paperback – 1 January 2016 by Michael T.

Goodrich , Roberto Tamassia , Michael H. Goldwasser .

2. Data Structure and Algorithmic Thinking with Python Paperback – 1 January 2015 by Narasimha Karumanchi

MVN University, Haryana

Reference Books:

1. Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles Paperback – 1 January 2016 by Narasimha Karumanchi 2. Problem-Solving with Algorithms and Data Structures Using Python is written by Bradley N. Miller and David L.

Note: Latest editions of all the suggested books are recommended.

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE)

CSE II Semester

CSL-508-20 IoT Edge Nodes L T P Cr

3 0 0 3

Course Objective:

This course will introduce students to the idea of integrating high-level activities like data analytics with

basic hardware components like sensors and actuators to create complex systems that improve people's lives

and support society. This course unit provides a timely opportunity to familiarize yourself with the

fundamental principles of the forthcoming IoT revolution.

This course provides an excellent opportunity to become acquainted with the basic concepts and primary

problems that constitute the Internet of Things. The diversity of data types generated by IoT edge-nodes, as

well as the flexibility of components that can be incorporated into such systems, are highlighted.

Unit 1 Introduction to Fog Computing:Fog Computing: A Platform for Internet of Things and Analytics: a

massively distributed number of sources - Big Data Metadata Management in Smart Grids: semantic

inconsistencies – role of metadata

Unit 2 IoT Edge and Gateway Network :IoT Edge basic introduction; What and where is the “edge” Edge

/Fog computing Value of keeping data local; An edge-first approach; The power of edge to cloud; IoT

Edge cloud interface; Communication protocols and protocol stacks for the edge devices Overview of

Edge Networks in IoT; Implementation of IoT Edge Gateway; Edge Architecture

Unit 3 Basics of Cloud System in IoT :Cloud Path; AMulti-Tier Cloud Computing Framework, Femto

Clouds; Leveraging Mobile Devices to Provide Cloud Service at the Edge Fast; Scalable and Secure On

loading of Edge Functions Using Air Box

Unit 4 Design issues for the IoT edge:Sensors and actuators for IoT systems, Interoperability and reliability

issues, Communication protocols and protocol stacks for the edge devices, Hardware security for edge

devices

Unit 5 Identity management of IoT Devices:Identity lifecycle – authentication credentials – IoT IAM

infrastructure – Authorization with Publish / Subscribe schemes – access control

Concerns in data dissemination – Lightweight and robust schemes for Privacy protection – Trust and Trust

models for IoT – self-organizing Things - Preventing unauthorized access.

Unit 6 Energy Harvesting:Harvesters – micro generators, strategies for enhancing the performance of energy

harvesters. Electromechanical modeling of Lumped parameter model and coupled distributed parameter

models, closed-form solutions, micro fabricated coils and magnetic materials – scaling – power

maximations – micro and macro scale implementations. Non-linear techniques – vibration control & steady

state cases

MVN University, Haryana

Text Books:

1. Raj Kamal, ‖Internet of Things-Architecture and design principles‖, McGraw Hill Education.

2. Holger Karl & Andreas Willig, "Protocols And Architectures for Wireless Sensor Networks" , John

Wiley, 2005.

3. Feng Zhao & Leonidas J. Guibas, ―Wireless Sensor Networks- An Information Processing Approach",

Elsevier, 2007.

Reference Books: 1.Carlos Manuel Ferreira Carvalho, Nuno Filipe Silva VeríssimoPaulino, “CMOS Indoor Light Energy

Harvesting System for Wireless Sensing Applications”, springer

2. Danick Briand, Eric Yeatman, Shad Roundy ,“Micro Energy Harvesting”

3. Securing the Internet of Things Elsevier

4. Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and Implementations

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE) CSE II Semester

CSL-502-20 Mathematical Foundations for Data Science –

II

L T P Cr.

0 3 0

3

Objective: The objective of this course is to provide a conceptual understanding of data science.

Unit I: Sampling Distributions: How sample proportions vary around the population proportion, How mple

mean vary around the population mean.

Unit II: Statistical Inference: Confidence Intervals: Point and interval estimates of population parameters,

Constructing a confidence interval to estimate a population proportion, Constructing a confidence interval

to estimate a population mean, Choosing a sample size for a study.

Unit III: Statistical Inference: Significance Tests about Hypotheses: - Steps for performing a significance

test, Significance tests about proportions, Significance tests about means, Decisions and types of errors in

significance test, Limitations of significance tests, The likelihood of a Type II error and the power of a test.

Unit IV: Comparing two Groups: Categorical response: Comparing two proportions, Quantitative response:

Comparing two means, Other ways of comparing means, Permutation Test, Analyzing dependent samples

adjusting for the effects of other variables.

Unit V: Analyzing the Association between Categorical Variables: Independent and Dependent

Associations, Testing categorical variables for independence, Determining the strength of the association,

Using Residuals to reveal the pattern of association, Fisher’s Exact and Permutations Tests.

Unit VI: Analyzing the Association between Categorical Variables: Modelling how two variables are

related, Inference about model parameters and the association, Describing the strength of association, How

the data vary about the regression line, Exponential regression: A model for nonlinearity.

References:

1. A. Agresti, C. Franklin and B. Klingenberg: Statistics: The art and Science of learning from Data,

Pearson.

2. J. K. Sharma: Business Statistics, Viks Publications.

3. S.C. Gupta and V. K. Kapoor: Fundamentals of Mathematical Statistics, S. Chand and Sons.

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE) CSE II Semester

CSL-518-20

Big Data Basics

L T P Cr

3 0 0 3

Objective: This subject gives students all around learning of the Big Data framework using Hadoop and

Spark, including YARN, HDFS and Map Reduce. You will be able to learn how to use Pig, Hive, Note:

Total five questions are to be attempted .

Unit 1: Big Data Basics:Understanding Big data,4 V's of big data,Capturing Big Data,Benefitting from Big

Data,Technology challenges for big data,Big Data Sources,Big Data Application.Standard big data

Architecture,Big data architecture examples

Unit2: Hadoop Ecosystem & HDFS:Hadoop Framework, HDFS design goals,Master-Slave Architecture,

Block system,Secondary nodes,checkpoint node, streaming and batch processing,Introduction to

Pig,Hive,Sqoop,HBase.

Unit 3: Resource Management and Parallel Processing:Overview of mapreduce, YARN Introduction,

Difference between MR1 and MR2, Mapper and Reducer, MapReduce job Execution Flow, Sample

MapReduce Application.

Unit 4: PIG and HIVE:Pig Datatypes (scalar , complex) , Running Pig (interactive , Batch) , Pig Operators

–Local, Store,Dump,Distinct, Filter, ForEach, generate , Limit, Union ,join, order by.

Hive Services , Comparing Hive to traditional Databases , Relational Data Analysis –(data types

(primitive,complex)databases-tables,create,alter,delete),Hive Schema & Data storage Loading data into

Hive views Storing query results.

Unit 5: Spark :Spark Introduction:What is Spark,Why is Spark,Scala Introduction:benefits of scala,Scala

programming basics ,Spark Architecture:RDDs,transformations,actions ,Spark Ecosystem,Spark Vs

Hadoop.

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Unit 6 :Introduction to Cloud Computing:Introduction Concepts of cloud, virtualizations and virtual

machine, Virtualization in fabric / cluster/grid, Virtual network, information model and data model for

virtual machine Service oriented architecture, On-Demand computing.

Text Book:

1.Big Data by Anil Maheshwari,Mc Graw Hill

Reference Books:.

1.Hadoop for Dummies by Dirk Deroos, Paul C. Zikopoulos , Roman B. Melnyk

2. Big Data, Black Book by DT Editorial Services (Author)

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE)

CSE II Semester

CSL-506-20 Next Genertion Databases L T P Cr

3 0 0 3

Objective:

Next Generation Databases using MongoDB would equip the learner to master the skills needed to

become MongoDB experienced professional. By going through this MongoDB course you would be

mastering data modelling, ingestion, query and Sharding, Data Replication with MongoDB along with

installing, updating and maintaining MongoDB environment.

Unit 1: Introducing Data Modeling: The relationship between MongoDB and NoSQL, Introducing

NoSQL (Not Only SQL), NoSQL databases types, Dynamic schema, scalability, and redundancy,

Database design and data modeling, The ANSI-SPARC architecture

Data Modeling with MongoDB: Introducing documents and collections (JSON, BSON), Characteristics of

documents, Designing a document, Common document patterns

Unit 2: Querying Documents: Understanding the read operations, Selecting all documents, Comparison

operators, Logical operators, Element operators, Evaluation operators, Array operators, Projections,

Introducing the write operations, Inserts, Updates, Write concerns, Bulk writing documents

Unit 3: Indexing: Indexing documents, Indexing a single field, Indexing more than one field, Indexing

multikey fields, Indexing for text search, Creating special indexes, Time to live indexes, Unique indexes,

Sparse indexes

Unit 4: Optimizing Queries: Understanding the query plan, Evaluating queries, Covering a query, The

query optimizer, Reading from many MongoDB instances

Managing the Data: Operational segregation, Capped collections, Data self-expiration

Unit 5: Scaling: Scaling out MongoDB with sharding, Choosing the shard key, Scaling a social inbox

schema design, Fan out on read, Fan out on write, Fan out on write with buckets

Unit 6:Logging and Real-time Analytics with MongoDB: Log data analysis, Error logs, Access logs,

Measuring the traffic on the web server, Designing the schema, Capturing an event request, A one-document

solution, TTL indexes, Sharding, Querying for reports

MVN University, Haryana

Suggested Readings

Text Book: “MongoDB Data Modeling”, Wilson da Rocha Franca.

Reference Books: https://university.mongodb.com/

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE) CSE III Semester

CSL-511-20

Data Analysis with Python

L T P Cr

3 0 0 3

Objective: This subject will give complete instructions for manipulating, processing, cleaning, and

crunching datasets in Python. The learners are expected to understand how to solve a broad set of data

analysis problems effectively.

Pre-requisite : Python language fundamentals, Basics of Statistics

Unit 1: NumPy Basics: Arrays and Vectorized Computation: The NumPy ndarray: Creating ndarrays, Data

Types for ndarrays, Arithmetic with NumPy Arrays, Basic Indexing and Slicing, Boolean Indexing, Fancy

Indexing, Transposing Arrays and Swapping Axes, Universal Functions: Fast Element-Wise Array

Functions, Array-Oriented Programming with Arrays: Expressing Conditional Logic as Array Operations,

Mathematical and Statistical Methods, Methods for Boolean Arrays, Sorting, Unique and Other Set Logic,

File Input and Output with Arrays, Linear Algebra, Pseudorandom Number Generation

Unit 2: Getting Started with pandas: Introduction to pandas Data Structures Series: DataFrame,Index Objects,

Essential Functionality: Reindexing, Dropping Entries from an Axis,Indexing, Selection, and Filtering,

Integer Indexes, Arithmetic and Data Alignment, Function Application and Mapping,Sorting and

Ranking,Axis Indexes with Duplicate Labels, Summarizing and Computing Descriptive

Statistics:Correlation and Covariance,Unique Values, Value Counts, and Membership

Unit 3: Data Loading, Storage, and File Formats: Reading and Writing Data in Text Format,Reading Text Files

in Pieces,Writing Data to Text Format,Working with Delimited Formats,JSON Data

XML and HTML: Web Scraping,Binary Data Formats,Using HDF5 Format,Reading Microsoft Excel Files,

Interacting with Web APIs, Interacting with Databases

MVN University, Haryana

Unit 4: Data Cleaning and Preparation: Handling Missing Data: Filtering Out Missing Data, Filling In Missing

Data, Data Transformation: Removing Duplicates, Transforming Data Using a Function or Mapping,

Replacing Values, Renaming Axis Indexes, Discretization and Binning, Detecting and Filtering Outliers,

Permutation and Random Sampling, Computing Indicator/Dummy Variables: String Manipulation, String

Object Methods, Regular Expressions, Vectorized String Functions in pandas

Unit 5 : Data Wrangling: Join, Combine, and Reshape: Hierarchical Indexing: Reordering and Sorting Levels,

Summary Statistics by Level, Indexing with a Data Frame’s columns, Combining and Merging Datasets:

Database-Style Data Frame Joins, Merging on Index, Concatenating Along an Axis, Combining Data with

Overlap, Reshaping and Pivoting: Reshaping with Hierarchical Indexing, Pivoting “Long” to “Wide”

Format, Pivoting “Wide” to “Long” Format

Unit 6 : Plotting and Visualization: A Brief matplotlib API Primer: Figures and Subplots, Colors, Markers,

and Line Styles, Ticks, Labels, and Legends, Annotations and ,Drawing on a Subplot, Saving Plots to File,

matplotlib Configuration, Plotting with pandas and seaborn: Line Plots, Bar Plots, Histograms and Density

Plots, Scatter or Point Plots, Facet Grids and Categorical Data

Text Book:

1. Python for Data Analysis, 2nd Edition by Wes McKinney, Publisher(s): O'Reilly Media, Inc.

ISBN: 9781491957660

Reference Books:.

1. Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and

visualization using Python by Stefanie Molin

2. Data Analysis with Python | Free Courses in Data Science https://cognitiveclass.ai › courses › data-

analysis-python

MVN University, Haryana

SCHOOL OF COMPUTER SCIENCE & ENGINEERING

Programme: M.TECH(CSE) CSE III Semester

CSP-511-20

Data Analysis with Python Lab

L T P Cr

3 0 0 3

Objective: This Lab will give complete instructions for manipulating, processing, cleaning, and crunching

datasets in Python. The learners are expected to understand how to solve a broad set of data analysis

problems effectively.

Pre-requisite : Python language fundamentals, Basics of Statistics

1. Python Language Basics, IPython, and Jupyter Notebooks

2.Programs based on Built-in Data Structures, Functions

3. Programs based on NumPy Basics: Arrays and Vectorized Computation

4.Programs based on pandas data structures like series,DataFrames,IndexObjects, Indexing ,Selection,Filtering

5. Programs related to Data Loading, Storage

a.) Reading and Writing Data in Text Format

b.) Reading and Writing Text Files in Pieces

c.)Working with JSON Data

d.) Interacting with Web APIs

6.Programs based on Data cleaning and prepration,Data transformation,String manipulation,Regular

Expressions, Vectorized String Functions in pandas

7. Programs based on Data Wrangling: Join, Combine, Combining and Merging Datasets, Reshaping and

Pivoting

MVN University, Haryana

8.Programs related to Plotting and Visualizations

9.Programs related to Reshaping and Pivoting

10.Programs related to histograms and Density Plots

At last students have to apply all concepts they learned through to analyze particular data .

MVN University, Haryana

SCHOOL OF ENGINEERING AND TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

M. Tech CSE III Semester

CSL-513-20 Artificial & Computation Intelligence L T P Credits

3 0 0 3

Course Objective: This course is to provide the students with an overview of the concepts and fundamentals

of neural networks, transfer-learning, different deep belief networks, fuzzy systems, Reinforcement

Learning, and Genetic algorithm. This course also helps the students to design and implement solutions for

real-life problems using artificial intelligence.

Unit 1: Neural Networks Basics: Introduction, Linear Separable Problems, and Perceptron – Multi-layer

neural networks and Back Propagation, Practical aspects of Deep Learning: Train / Test sets, Bias/variance,

Overfitting and regularization, Linear models and optimization, Vanishing/exploding gradients, Gradient

checking, Hyper Parameter Tuning.

Unit 2: Deep Neural Network Architectures: Introduction, Convolutional neural networks, vgg16,

Resnet50, Recurrent neural networks – LSTM.

Unit 3: Introduction to Reinforcement Learning: Elements of Reinforcement Learning – Multi-armed

Bandits, Finite Markov Decision Processes – Dynamic Programming

Unit 4: Deep Belief Networks: Introduction, Tensor Flow, Keras or MatConvNet for implementation,

Monte Carlo Methods – Temporal-Difference Learning – n-step Bootstrapping - Planning and Learning

with Tabular Methods.

Unit 6: Computational Learning: Introduction, mistake bound analysis, sample complexity analysis, VC

dimension, Occam learning, accuracy and confidence boosting, Dimensionality reduction: Principal

component Analysis, feature selection and visualization.

Unit 5: Fuzzy Systems Concepts and Paradigms: Fuzzy sets and Fuzzy Logic, Theory of Fuzzy sets,

Approximate Reasoning, Fuzzy Systems Implementations, Fuzzy Rule System Implementation

MVN University, Haryana

Suggested Readings

Text Book:

1. Artificial Intelligence by Elaine Rich, Kevin Knight and Shiva Shankar B Nair, Tata McGraw Hill.

2. D.C. Eberhart, “Computational Intelligence: Concept to Implementations”, Morgan Kaufmann

Publishers, 2007.

3. Ian Good fellow, Yoshua Bengio and Aeron Courville,” Deep Learning”, MIT Press, First Edition,

2016.

Reference Books:

1. Artificial Intelligence: A Modern Approach by S. Russell and P. Norvig, Prentice Hall.

2. Wendy L. Martinez and Angel R, “Martinez Computational Statistics,” Chapman

&Hall/CRC, 2002.

3. Timothy J Rose, “Fuzzy Logic with Engineering Applications”, Third Edition, Wiley, 1995.

Note: Latest editions of all the suggested books are recommended.

MVN University, Haryana

SCHOOL OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

M. Tech CSE III Semester

CSP-513-20 Artificial & Computation Intelligence Lab L T P Credits

0 0 2 1

Laboratory Objective: This course is to provide the students with a theoretical and practical base in

Artificial Intelligence and computation intelligence. Students will able to Design, Implement, and Analyze

simple problem solving and deep-learning algorithms and they are also able to identify, formulate, and

solve problems with optimal model complexity.

List of Experiments:

1. Write a python program to calculate output in a multi-layer feed forward network.

2. Write a program to demonstrate the line of separation.

3. Write a program to train a neural network to classify two clusters in a 2-D space.

4. Write a program to create data sets and plotting these data values.

5. Write a python code for perceptron implementation with one neuron on output using a bias value.

6. Write a python code to make predictions with KNN classifier on the Iris Flowers Dataset.

7. Write a python code to make predictions with SVM classifier on the diabetic’s dataset.

8. Write a python code to make predictions with ANN on the covid-19 dataset.

9. Write a python code to make predictions with CNN on the digit MNIST dataset.

10. Write a python code to make predictions with CNN on the Fashion-MNIST Dataset.

11. Implement SVM classification by fuzzy concepts.

12. Apply k-Means algorithm to cluster a set of data stored in a .CSV file.

13. Write a program to implement the naïve Bayesian classifier for a sample training data set stored as

a .CSV file. Compute the accuracy of the classifier, considering few test data sets.

MVN University, Haryana

SCHOOL OF ENGINEERING AND TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

M. Tech CSE III Semester

CSL-519-20 CLOUD COMPUTING L T P Credits

3 0 0 3

Objective: This course provides an introduction to cloud computing with an emphasis on its architecture,

service management, cloud security, Data storage etc.

UNIT-I:

Overview of Computing Paradigm: Recent trends in Computing Grid Computing, Cluster Computing,

Distributed Computing, Utility Computing, Cloud Computing Evolution of cloud computing Business

driver for adopting cloud computing, Introduction to Cloud Computing, History of Cloud Computing, Cloud

service providers, Properties, Characteristics & Disadvantages, Pros and Cons of Cloud Computing,

Benefits of Cloud Computing, Cloud computing vs. Cluster computing vs. Grid computing Role of Open

Standards

UNIT-II:

Cloud Computing Architecture Cloud computing stack Comparison with traditional computing architecture

(client/server), Services provided at various levels, How Cloud Computing Works, Role of Networks in

Cloud computing, protocols used, Role of Web services Service Models (XaaS) Infrastructure as a Service

(IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) Deployment Models Public cloud, Private

cloud, Hybrid cloud, Community cloud

UNIT-III:

Infrastructure as a Service (IaaS) Introduction to IaaS, IaaS definition, Introduction to virtualization,

Different approaches to virtualization, Hypervisors, Machine Image, Virtual Machine (VM) Resource

Virtualization Server, Storage, Network Virtual Machine (resource) provisioning and manageability,

storage as a service, Data storage in cloud computing (storage as a service) Examples Amazon EC2 Renting,

EC2 Compute Unit, Platform and Storage, pricing, customers Eucalyptus Platform as a Service(PaaS)

Introduction to PaaS What is PaaS, Service Oriented Architecture (SOA) Cloud Platform and Management

Computation Storage Examples Google App Engine, Microsoft Azure, Software as a Service (PaaS)

Introduction to SaaS, Web services, Web 2.0, Web OS, Case Study on SaaS

UNIT-IV:

Service Management in Cloud Computing Service Level Agreements (SLAs), Billing & Accounting,

Comparing Scaling Hardware: Traditional vs. Cloud, Economics of scaling: Benefitting enormously

MVN University, Haryana

Managing Data Looking at Data, Scalability & Cloud Services Database & Data Stores in Cloud Large

Scale Data Processing

UNIT-V:

Cloud Security Infrastructure Security Network level security, Host level security, Application-level

security Data security and Storage Data privacy and security Issues, Jurisdictional issues raised by Data

location Identity & Access Management, Access Control, Trust, Reputation, Risk, Authentication in cloud

computing, Client access in cloud, Cloud contracting Model, Commercial and business considerations

UNIT-VI:

Data in the cloud: Relational databases, Cloud file systems: GFS and HDFS, Bigtable, HBase and Dynamo,

Issues in cloud computing, implementing real time application over cloud platform Issues in Intercloud

environments, QOS Issues in Cloud, Dependability, data migration, streaming in Cloud. Quality of Service

(QoS) monitoring in a Cloud computing environment.

REFERENCES:

1. Cloud Computing Bible, Barrie Sosinsky, Wiley-India, 2010

2. Cloud Computing: Principles and Paradigms, Editors: Rajkumar Buyya, James Broberg, Andrzej

M. Goscinski, Wile, 2011

3. Cloud Computing: Principles, Systems and Applications, Editors: Nikos Antonopoulos Lee

Gillam, Springer, 2012

4. Cloud Security: A Comprehensive Guide to Secure Cloud Computing, Ronald L. Krutz, Russell

Dean Vines, Wiley-India, 2010