MU MSC CS SEM III REVISED SYLLABUS 20211 1 Syllabus Mumbai University by munotes
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Syllabus
For the
AC – 29/06/2021 Item No. - 6.40
UNIVERSITY OF MUMBAI
Syllabus
For the
Program : M.Sc. S EM-I & I I CBCS
(REVIS ED)
Course: Computer Science
(Choice Based and Credit Sy stem with effe ct
from the acad emic year 2021 -22)
Page 4
AC – 29/06/2021
Item No: 6.40
1
UNIV ERSITY OF MUMBAI
Syllabus for Approv al
Sr. No. Heading Particulars
1. Title of the Course M.Sc. (Computer Scien ce)
2. Eligibility for
Admission Circular No: No. PG/ Univ./VCD/ ICC /
2012-13/ 8, B.E/B.Sc. (Computer Sc ience)
/ BCS / B. Sc. (I.T.) / B.sc. (M aths) / B.Sc.
(Phy) with ancillary mathematics/ B.Sc.
(Stats) with ancillary mathematics
3. Passing Marks 40%
4. Ordinances /
Regulations (if, any) As applicable for all M.Sc. Courses
5. Number of years /
Semesters Two years – Four Semesters
6. Level P.G./ U.G. /Diploma / Certificate
(Strike out which is not applicable)
7. Pattern Yearly / Semester, Choice Based
(Strike out which is not applicable)
8. Status New /Revised
9. To be implemented
from Academic year From the Academic Year 2021 – 2022
Date: 28/06/2021
Dr. Jagdish Bakal Dr. Anuradha Majumdar
BoS Chairperson in Computer Science Dean, Science and Tech nology
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CONTENTS
1. PREAMBLE
2. PROGRAM O UTCOME
3. PROGRAMME STRUCT URE
4. DETAILED S YLLABUS FOR SEMESTER - I & SEM ESTER - II
5. EVALU ATION
6. SCHEM E OF EXAMINATIONS AND DISTRIBUTI ON PATTER N OF
MARKS
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1. PREAMBLE
M.Sc. in Computer Science is a two-year post-graduate programme with
the objective to develop human re sources with core competence in various
thrust areas of Computer Science. It will provide studen ts with opportun ities
to develop and hone core competency in the field of computer science and
encourag e them to make a mark in the much sought-after IT ind ustry.
The Syllabus of this Course creates a unique ident ity for M.Sc. in Comp
Science distinct from similar degrees in other related subjects, focuses on
core Computer Science subjects, incorporate advanced and most recent
trends, Identify and nurture research temper among students, Offer
provision for internship with industry and Focus, as far as possible, only on
open -source software
The syllabus for the semester I and semester II has tried to initiate steps
to meet these goals. By extending the syllabus to semester III and
semester IV, it is assum ed that these goals will be met to a larger extent.
The syllabus proposes to have four core compulsory courses in Semester I
and Semester II. UNIT -1 of Paper I of Semester - I and Se mester - II are
ABILITY ENHAN CEMENT UNITS and UNIT- 4 of all papers of Semester -
I and Semester - II is SKILL ENHA NCEM ENT UNIT. Semester III and
Semester IV proposes ele ctives courses based on a recent and emerging
area. Inclusion of Project as part of t he internal assessment is an attempt to
translate theory int o practice. It is assumed that, with this back ground, a
student can take up challe nging research project in the semester III and
semester IV and will be better fit for industry as he or she will have strong
found ation on fundamentals and e xposure to ad vanced and emerging
trends.
We thank all the industry experts, senior faculties and our colleagu e’s
departme nt of Computer Science of different college s as well as University
of Mumbai; who have given their valuable comme nts and suggestions,
which we tried to incorpor ate. We thank the Chairp erson and members of
the Ad-hoc Board of Studies in Computer Science of University for their
faith in us. Thanks to one and all who have directly or indirectly helped in
this venture.
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2. PROGRAM OUTC OMES
The M. Sc. Computer Science programme is designed to help the students
to:
● To be funda mentally strong at core subject of Computer Science.
● To apply programming and computational skills for industrial
solutions.
● Broad u nderstanding of latest technological trends.
● To ident ify opport unities for establishing an enterprise for immediate
Employment.
● Able to understand and apply fundamental research concepts.
● Able to use efficient soft skills for professional development.
● Engage in independent and life-long learning for continued
professional developm ent.
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3. PROGRAMME STRUCTU RE
A Programme Duration Four Semest er
(2 Years)
B Total Credits required for Successf ul Completion 96
C Credits required from Core Courses 42
D Credits required for the Ability Enhanceme nt
Cour ses 02
E Credits required for the Skill Enhanceme nt
Cour ses 12
F Credits required for the Practical Course 28
G Project 06
H Internship 06
I Minimum Attendance per Semes ter 75%
This is the syllabus for the Semester-I and Semester-II of M.Sc. Computer
Science program of University of Mumbai to be implem ented from the year
2021 -22. The Syllabus offers four Theory Courses and Four Practi cal
Courses each in each Semester.
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SEMESTER - I
The syllabus proposes four subjects in Semester -I. Each subject has
Theory and Practic al components. Each of these Courses is of Four
Credits each and is expected to complete in 60 hours.
The follow ing table gives the details of the Theory Courses in Semester -I.
Semest er - I: Theory courses
Course Code Course Title No of Hours Credit
PSCS101 Algorithm for Optimization 60 04
PSCS102 Software Defined Networking 60 04
PSCS103 Applied Signal and Image
Processing 60 04
PSCS104 Advanced Database Tec hniques 60 04
Total Credits for Theory courses in Semest er-I 16
Semest er - I: Practical Lab courses
The syllabus proposes Four Laboratory courses of 2 Credits each. As far
as the Practical are concerned, equal weightage similar to that of Theory
courses has been given in terms of the number of hours.
The follow ing table gives the details of the Practical Courses in Semester -I
Course Code Course Title No of Hours Credit
PSCSP101 Algorithm for Optimization 60 02
PSCSP102 Software Defined Networking 60 02
PSCSP103 Applied Signal and Image
Processing 60 02
PSCSP104 Advanced Database Tec hniques 60 02
Total Credits for Practical c ourses in Semest er-I 08
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SEMESTER - II
The Syllabus propo ses four subjects in Semester - II also. As in the case of
Semester-I, each subject has theory and practical component s. Each of
these courses is of Four Credits and Two credits respectively and is
expected to complete in 60 hours.
The follow ing table gives the details of the Theory Courses in Semester -II.
Semest er - II: Theory courses
Course Code Course Title No of Hours Credit
PSCS201 Applied Machine and Deep Learning 60 04
PSCS202 Natural Language Processing 60 04
PSCS203 Web Mining 60 04
PSCS204 Embedded and IoT Technology 60 04
Total Credits for Theory courses in Semest er -II 16
Semest er - II: Practical Lab courses
The Syllabus proposes Four Labo ratory courses of 2 Credits each. As far
as the Practical are concerned, equal weightage similar to that of Theory
courses has been given in terms of the number of hours.
The follow ing table summari zes the details of the practical courses in the
Semester - II.
Course Code Course Title No of Hours Credit
PSCSP201 Applied Machine and Deep Learning 60 02
PSCSP202 Natural Language Processing 60 02
PSCSP203 Web Mining 60 02
PSCSP204 Embedded and IoT Technology 60 02
Total Credits for Practical Courses in Semest er -II 08
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4.DETAILED SYLL ABUS FOR S EMESTER - I & SEMESTER - II
SEMESTER- I
Course Code Course Title Credits
PSCS101 Algorithm for Optimization 04
Course Outcome: -
● You will be able to effectively implem ent optimization techniqu es to the
existing algorithm to improve its performance.
● You will be able to work in the areas of Machine Learning and Data
Sciences Algorithms
Cour se Specific Outcome: -
● Optimization with a focus on practical algor ithms for the design of
engineering system s
● Exposure to multivariable calculus, linear algebra, and probabil ity
concepts.
● Learn a wide variety of optimization topics, introducing the underl ying
mathematical probl em formulations and the algorithms for solving them.
UNIT 1: (Ability Enhanceme nt)
Introduct ion to Optimization Process
Basic Optimization Proble m, Constraints, Critical Points, Condit ions
for Local Minima, Contour Plots. Unimodali ty, Fibonac ci Search,
Golden Section Search, Quadratic Fit Sear ch.
15L
UNIT 2: Order Methods
First-Order Methods, Gradient Descent, Conjugate Gradie nt,
Adagrad, RMSPr op, Adadel ta, Ad am, Hypergradient Descen t.
Second-Order Methods, Newton’s Method, Secant Method, Quasi-
Newton Methods.
15L
UNIT 3: Sam pling and Surrogate Models
Sampling Plans, Full Factorial, Random Sa mpling, Uniform
Proje ction Plans, Stratified Sam pling, Space-Filling Metrics.
Surroga te Models, Fitting Surrogate Models, Linear Models, Basis
Functions, Fitting Noisy Obje ctive Functions, Model Selection,
15L
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Probabil istic Surrogate Models, Gaussian Distribution, Gaussian
Processes, Pred iction
UNIT 4: (Skill Enhanceme nt)
Optimization and Uncer tainty
Optimization under Uncertainty, Uncertainty, Set-Based Uncertainty,
Probabil istic Uncertainty. Uncertainty Propagation , Sampling
Methods, Taylor Approximation, Polynomial Chaos, Bayesian Monte
Carlo. Dynamic Progra mming, Ant Colony Optimization. Expression
Optimization, Grammar s, Genetic Programm ing, Grammat ical
Evolution, Probabil istic Gra mmars, Probabili stic Prototype Trees
15L
TEXT BOOK:
1. Algori thms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, The
MIT Press 201 9.
REFERENCE BOOKS:
1. Think Julia: How to Think Like a Computer Scientist by Allen B. Downey
and Ben Lauwens 1 st Edition 2019 O'reilly.
2. Decision Making Under Uncertainty: Theory and Appli cation by Mykel J.
Kochenderfer MIT Lincoln Labo ratory Series 201 5.
3. Introduction to Algorithms, By Thomas H. Cormen, Charles E. Leiserson,
Ronald L. Rivest and Clifford Stein 3Ed. (International Edition) (MIT
Press) 2009
Course
Code Course Title Credits
PSCSP101 Practical Course on Algorithm Optimization 02
Note: All the Practical’s should be implemented using Julia
Link: Julia:https://julialang.org/
1 Implement Contour Plots.
2 Implement Fibonacci and Golden section search.
3 Implement Qua dratic Fit Search.
4 Implement Gr adient descent.
5 Implement qu asi-Newton methods to find the local maxima.
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6 Implement the Adagrad method with application, RMSprop and
Adadelt a.
7 Implement radi al basis functions using surrogate modelling.
8 Apply Random Forest in surrogate Model.
9 Implement Ga ussian Process and its application.
10 Path finding using Ant Colony Optimization with an application.
Course Code Course Title Credits
PSCS102 Software Defined Networking 04
Course Outcome: -
● To make the students capable of understanding computer network b asics.
● To Obtain the knowledge of Software defined networks with
under standing of data plane, control plane and application plane.
● To apply network virtualization for industry standa rd solutions.
● To improve skills in implementing network virtualization and Software
Defined N etwork (SDN).
Cour se Specific Outcome: -
● Learner s will be able to unde rstand basic concepts of Software Defined
Networking and network virtualization.
● Learner s will be able to explore Open Flow specifications to build Software
defined networks.
● Learner s will be able to analyse and implem ent theories and practical
related to Network managem ent and Virtualization.
● Learner s will be able to apply knowledge of Software Defined Networking
as per industry standards.
Unit 1: Introduction to Com puter Networking
Basic Concepts and Definitions: LA N, MAN, WAN, AD-Hoc,
Wireless Network, Understanding the layered architecture of
OSI/RM and TCP-I P Model, Concepts and implementation of IPV4
and IPV6, Study of various network Routing protocols, Introduction
to Transport layer and Appl ication layer protocols.
15L
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UNIT 2:Software Defined Networking
Elements of Modern Networking, Require ments and Technology ,
SDN: Background and Motivation, SDN Data Plane and OpenF low,
SDN Control Plane , SDN Appl ication Plane
15L
UNIT 3: Network Functions Virtualization
Concepts and Ar chitecture, NF V Functiona lity, Network
Virtualization Quality of Service, MODERN NETWORK
ARCHI TECTURE: CLOUDS AN D FOG, Cloud Computing, The
Internet of Things: Components
15L
UNIT 4: (Skill Enhanceme nt)
Design and implementation of Network
Understand and implem ent Layer 2/3 switching techniques (VLAN
/TRUNKI NG/ Managing Spanning Tree), Implementation of OSPF
V2 and V3, Implem entation BGP, Implementation Multicast Routing,
Implementation of MPLS, Implementation of Traffic Filtering by using
Standard and Extended Access Control List, Implem entation of
Routing redistribution, Implementation of Policy Based Routing/
Load Bal ancing /QOS/Natting /VRF
15L
TEXT BOOK:
1. Behro uz A Forouzan ―TCPIP Protocol Suite‖ Fourth Edition 2010
2. William Stallings, ―Foundat ions of Modern Networking‖, Pearson
Ltd.,2016.
3. Software Defined Networks: A Comprehensi ve Approach by Paul
Goran sson and Chuck Black, Morgan Kaufmann Publications, 2014
4. SDN - Software Defined Networks by Thomas D. Nadeau & Ken Gray ,
O'Reilly, 2013
REFERENCE BOOKS:
1. Network Progra mmability and A utomation-Jason Edelman, Matt Oswalt
First Edition 2018.
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Course
Code Course Title Credits
PSCSP102 Practical Course on Software Defined Networking 02
Note: All the Practical’s should be implemented using GNS3/EVE-
NG/CISCO VIRL
Link: GNS3 :https://www.gns3.com/software/download
EVE-NG: https://www.eve-ng. net/index .php/ download/
CISCO
VIRL:https://learningnet work.cisco.com/s/ques tion/0D53i00000K swpr/virl-
15-download
1 Implement IP SLA (IP Service Level Agreement)
2 Implement IPv4 ACLs
1. Standard
2. Extended
3 1. Implement SPAN Technolog ies (Switch Port Analyzer)
2. Implement SNM P and Syslog
3. Implement Flexible NetFlow
4 1. Implement a GRE Tunnel
2. Implement V TP
3. Implement NAT
5 Implement Inter-VLAN Routing
6 Observe STP Topology Changes and Implement RSTP
1. Implement Ad vanced STP Modifications and Mechanisms
2. Implement MST
7 1. Implement EtherChannel
2. Tune and Optimize EtherChann el Operations
8 OSPF Implementation
1. Implement Sing le-Area OSPFv2
2. Implement Multi-Area OSP Fv2
3. OSP Fv2 Route Summari zation and Filtering
4. Implement Multiarea OSP Fv3
9 Implement BGP Communities
1. Implement MP-BGP
2. Implement eB GP for IPv4
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3. Implement BGP Path Manipulation
10 Implement IPsec Site-to-Site VPNs
1. Implement GR E over IPsec Site-to-Site VPNs
2. Implement V RF Lite
11 Simulating SDN with
1. Open Daylight SDN Controller with the Mininet Network
Emulator
2. OFN et SDN network emulator
12 Simulating Open Flow Using MININET
Course Code Course Title Credits
PSCS103 Applied Signal and Image Processing 04
Course Outcome: -
● Introduce the concepts of signal processing terms and relate them to
image pr ocessing
● Learn about basic image processing techniqu es (e.g., noise removal and
image enhan cement).
● Develop skills to design and implement algor ithms for advanced image
analysis
● Apply image pr ocessing to design solutions to real-life problem s
Cour se Specific Outcome: -
● Understanding the terminologi es of signal and digital image processing
● Abilit y to apply various images, intensity transformations, and spatial
filtering.
● Knowledge of Pe rform frequency domain operations on images.
● Abilit y to apply image segm entation and extract image features.
● Apply image pr ocessing algorithms in practical applications.
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UNIT 1: Fund amentals of Digital Signals Processing
Periodic signals, Spectral decomposition, Signa ls, Reading and
writing Waves, Spectrums, Wave objects, Signal objects
Noise: Uncorrelated noi se, Integrated spectrum, Brownian noi se,
Pink Noise, Gaussian noi se; Autocorrelat ion: Correlation, S erial
correlation, Autocorrelat ion, Autocorrelation of periodic signals,
Correlat ion as a dot prod uct
Frequency do main Oper ations: Representing Image as Signals,
Sampling and Fourier Transforms, Discre te Fourier Transform,
Convolution and Frequency Domain Filtering , Smoothing u sing low-
pass filters, Sharpening u sing hi gh-pass filters. Fast Fourier
Transforms.
15L
UNIT 2: Image Processing f undamentals and Pixel-
Transformation
Definition, Application of Image Processing, Image Proc essing
Pipeline, Tools and Librari es for Image Proc essing, Image types and
files formats.
Intensity Transformations- Log Transform, Power-law Transform,
Contrast Stretching, Thresholding
Histog ram Processing- Histogram Equali zation and Histogram
Matchi ng;
Linear an d Non-linear smoothing of Images, Sharpening of images
Image Deri vative: Derivatives and gradients, Lapl acian, the effect of
noise on gradie nt computation
15L
UNIT 3: Structural and Morphological Operations
Edge Detection: Sobel, Canny Prew itt, Robert edge detection
techniques, LoG and DoG filters, Image Pyramids: Gaussian
Pyramid, Lapla cian Pyramid
Morphologi cal Image Processing: Erosion, Dilation, Opening and
closing, Hit-or-Miss Transformation, Skeletonizing, Computing the
15L
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convex hull, removing small objects, White and black top-hats,
Extracting the boundary, Gray scale operations
UNIT 4: (Skill Enhanceme nt)
Advanced Image Processing Operations
Extracting Image Features and Descriptors: Feature detector versus
descriptors, Boundary Pro cessing and feature descriptor, Principal
Components, Harris Corner Detector, Blob detector, Histogram of
Orien ted Gradients, Scale-invariant feature transforms, Haar-like
features
Image Segmentation: Hough Transform for detecting lines and
circles, Thresholding and O tsu’s segmentation, Edge-based/region-
based segmentation
Region gro wing, Region splitting and Merging, Watershed algor ithm,
Active Contours, morpholog ical snakes, and Grab Cut algorithms
15L
TEXT BOOK:
1. Digital Image Processing by Rafael G onzalez & Richard Woods, Pearson;
4th edition, 2018
2. Think DSP: Digital Signal Processing in Python by Allen Downey, O'Reilly
Media; 1st edition (Augu st 16, 2016)
REFERENCE BOOKS:
1. Understanding Digital Image Processing, Vipin Tyagi, CRC Pre ss, 2018
2. Digital Signal and Image Pr ocessing by Tamal Bose, John Wiley 2010
3. Hands-On Image Processing with Python by Sandipan Dey,Packt
Publi shing, 2018
4. Funda mentals of Digital Images Processing by A K Jain, Pear son, 2010
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Course
Code Course Title Credits
PSCSP103 Practical Course on Applied Signal and Image
Processing 02
Note: All the Practical’s should be implemented using Python
Link:https://www.python.org/downloads/
1 Write program to demonstrate the following aspects of signal
processing on suitable data
1. Upsampling and downsampling on Image/speech signal
2. Fast Fourier Transform to compute DFT
2 Write program to perform the following on signal
1. Create a triangle signal and plot a 3-period segment.
2. For a given signal, plot t he segment and compute the
correlation between them.
3 Write program to demonstrate the following aspects of signal on
sound/image data
1. Convolution operation
2. Template Matching
4 Write program to implement point/pixel intensity transformations
such as
1. Log and Power-law transformations
2. Contrast adjustments
3. Histogram equa lization
4. Thresholdin g, and halftoning operations
5 Write a program to apply various enhancements on images
using image derivatives by implementing Grad ient and Laplacian
operatio ns.
6 Write a program to implement linear and nonlin ear noise
smoothing on suitable image or sound signal.
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7 Write a program to apply various image enha ncement using
image derivatives by implem enting smoot hing, sharpen ing, and
unsharp masking filters for gene rating suitable images for
specific appl ication require ments.
8 Write a program to Apply edge d etection techniques such as
Sobel an d Canny to extract meaningful information from the
given image samples
9 Write the program to implement various morpholog ical image
processing techniques.
10 Write the program to extract image features by implementing
methods l ike corner and blob detectors, HoG and Haar features.
11 Write the program to apply segmentation for detecting lines,
circles, and other shapes/objects. Also, implement edge-based
and region-based segmentation.
Course Code Course Title Credits
PSCS104 Advanced Database Tec hniques 04
Course Outcome: -
● To cover advanced topics of databases to become more proficient.
● To provide studen ts with theoretical knowledge and practical skills in
advanced topics in dat abase systems , big data and modern data-intensive
systems .
● To Expand Students, view and introduce advanced topics and Business
Intelligence.
Cour se Specific Outcome: -
● To form professional competencies related to design and implementation
of non-relational databases, including object-oriented, paral lel and
Distrib uted.
● Learner s will be able to explore XML, and Mobile databases.
● Learne rs will be able to deal with methods used for dealing with spatial
and Te mporal Databases.
● Learner will have a solid grasp on business intelligence tools and XML.
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UNIT 1: E nhanced Database Models
Obje ct–Oriented Databases: Need of Object-orien ted databases,
Complex Data Types, Structured Types and Inheri tance, Object-
Identity and Reference, ODL and OQL, Implementing O-R Features,
Persistent Program ming Langua ges, Obje ct-Oriented versus Object-
Relational , Example of Obje ct orien ted and obje ct relational database
implementation, comparison of RDBM S, OOD BMS, ORDBMS
XML Databases: Structured Semi structure and unstructured data,
XML hiera rchical tree data model, Documents DTD and XML sche ma,
XML Documents & Database, XML query and t ransformation, Storage
of XML data, Xpath. XQuer y, Join and Nesting Queries, XML
database applications.
Spatial Databases: Types of spatial data, Geogr aphical Information
System s (GIS), Conceptual Data Models for spatial databases,
Logical data models for spatial databases: Raster and vector model.
Physical data models for spatial databases: Clustering methods
(space filling curves), Storage methods (R-tree). Query processing.
Temporal Databases: Time ontology, structure, and granularity,
Temporal data models, Temporal relational alge bra.
15L
UNIT 2: Coop erative Transaction Model
Parallel and Distributed D atabases: Architecture of paral lel
databases, Paral lel query e valuation, Paralle lizing individual
operatio ns, Sorting Joins
Distrib uted Databases: Concepts, Data fragmentation, Replication
and allocation techniques for distributed database design, Query
processing, Concurren cy control and recovery in distributed
databases,
Architecture and Design: Centralised versus non centralized
Databases, Homogeneous and Heterogeneo us DDB MS, Functions
and Architecture, Distrib uted database design, query pro cessing in
DDBM S, Distributed concurrency managem ent, deadl ock
managem ent, Distributed Commit Protocols: 2 PC and 3 PC,
Concepts of repl ication servers.
Mobile Database: Overview, Features, Advantages and
Disadvantages, Mobile databases in Android S ystem
15L
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UNIT 3: Learning the NoSQL Basics
Introduction to NoSQL: Characteristics of NoSQL, NoSQL Storage
types, Advantages and Dra wbacks, NoSQL Products
Interfacing and i nteracting with NoSQL: Storing Data In and
Accessing Data from Mongo DB, Redis, HBase and Apache
Cassan dra, Lang uage Bindi ngs for NoSQL Data Stores
Understanding the storage ar chitecture: Working with Column-
Orien ted Databases, HBase Distributed Storage Architecture,
Document Store Internals, Understanding Key/Value Stores in
Memcached and R edis, Eventually Consistent Non-relat ional
Databases
Performing CRUD operatio ns: Creating Records, Accessing Data,
Updating and Del eting Data
15L
UNIT 4: : (Skill Enhanceme nt)
Gaining Proficiency With NoSQL
Quer ying NoSQL Stores: Similarities Be tween SQL and Mongo DB
Query Features, Accessing Data from Colum n-Orien ted Databases
Like HBase, Quer ying Redis Data Stores
Indexing And Ordering Data Sets: Essential Concepts Behind a
Database Index, Indexing and Orde ring in Mongo DB, ouchDB and
Apache Cassandra
Managing Transactions And Data Integrity: RDBM S and ACID,
Distrib uted ACID System s, Upholding CAP, Consistency
Implementations
Using NoSQL in The Cloud: Goog le App Engine Data Store, Amazon
SimpleDB
15L
TEXT BOOK:
1. Database Manag ement System s by Raghu Ramakrishnan and Johannes
Gehr ke, McGraw Hill, 3rd Edition, 20 14
2. Professional NoSQL By Shashank Tiwari, Wrox-John Wiley & Sons, Inc,
2011
3. Getting Started with NoSQL, Gaurav Vaish, Packt Publishing Ltd, 2013
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REFERENCE BOOKS:
1. Advanced Database Manage ment System by byRini Chakrabarti and
Shilbhadra Dasgupta, Dreamtech Press, 2017
2. SQL & NoSQL Databas es, Andreas Meier · Michael Kaufmann, Springer
Vieweg, 2019
3. Parallel and Distributed System s by Arun Kulkarni, Nupur Prasad Giri,
Wiley, Second edition, 2017
4. Practical Hadoop Migration: How to Integrate Your RDBMS with the
Hadoop Ecosystem and R e-Architect Relational Applications to NoSQL By
Bhushan Lakhe, Apress; 1st edition, 2 016.
Course
Code Course Title Credits
PSCSP104 Practical Course on Advanced Database
Techniques 02
Note: All the Practical’s should be implemented using NoSQL
Link: https://www.oracle.com/database/technol ogies/nosql-database-
server-downloads.html
1 Create different types that include attributes and methods.
Define tables for these types by add ing a sufficient number of
tuples. Demonstrate insert, update and delete operations on
these tables. Execute queries on them.
2 Create an XML database and demonstrate insert, update and
delete oper ations on these tables. Issue queri es on it.
3 Demonstrate distributed databases environment by dividing
given global conceCreate a table that stores spatial data and
issue queries on it. ptual schema, into vertical and Horizontal
fragments and place them on different node s. Execute queries
on these fragments.
4 Create a table that stores spatial data and issues queri es on it.
5 Create a temporal database and issue queri es on it.
6 Demonstrate the Accessing and Storing and performing CRU D
operatio ns in
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1. Mongo DB
2. Redis
7 Demonstrate the Accessing and Storing and performing CRU D
operatio ns in
1. HBase
2. Apache Cassandra
8 Demonstrating MapReduce in Mo ngoDB to count the number of
female (F) and male (M) responde nts in the database.
9 Demonstrate the indexing and orderi ng operations in
1. Mongo DB
2. CouchDB
3. Apache Cassandra
10 Demonstrate the use of data manage ment and operations using
NoSQL in t he Cloud.
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SEMESTER - II
Course Code Course Title Credit
PSCS201 Applied Machine and Deep Learning 04
Course Outcome: -
● Developing projec ts in machine learn ing for industrial applications.
● Understanding and implementing algorithms and techniques of Machine
Learning useful in the field of Data Science, Image Processing, NLP, etc.
Cour se Specific Outcome:
● Understand core concepts of ML through implementations in python.
● Working with diverse toolkits and packages useful for developing projects
in ML
● Implement and understand deep learning and ANNs useful for industry
today.
UNIT 1: (Ability Enhanceme nt)
The Fundame ntals of Machine Learning
What is Machine Learning? Why use Machine Learning ? Types of
Machine Learning , Super vised Learn ing, Unsupervised Learning &
Reinforcement Learning. Challe nges of Machine Learn ing, Testing
and Vali dation
A First Application: Classification, MNIST Dataset, Performance
Measures, Confusion Matrix, Precision and Recall, Precision/Recall
Tradeoff, The ROC Curve, Multiclass Classification, Erro r Analysis.
15L
UNIT 2: Training Models
Linear Regression, Gradie nt Descent, Batch Gradie nt Descent,
Stochastic Gradient Descent, Mini-batch Gradie nt Descent,
Polynomial Regression, Learning Curves, The Bia s/Variance
Tradeoff, Ridge Regres sion, Lasso Regres sion, Early St opping,
Logistic Regression, Decision Boundarie s, Softmax Regression,
Cross Entropy.
15L
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UNIT 3: S uppo rt Vector Machines
Linear SVM Classification, S oft Margin Classification, Nonlinear SVM
Classification, Polynomial Kernel, Gaussian RBF Kernel, SVM
Regression, Decision Trees, Training and Visualizing a D ecision
Tree, Making Predictions, The CART Training Algorit hm, Gini
Impurity vs Entropy, Regularization Hyperparameters.
15L
UNIT 4: (Skill Enhanceme nt)
Fundame ntals of Deep Learning
What is Deep Learning? Need Deep Learning? Introduction to
Artificial Neural N etwork (ANN), Core compone nts of neural
networks, Multi-Layer Perceptron ( MLP), Activation functions,
Sigmoid, Rectified Linear Unit (ReLU), Introduction to Tensors and
Operat ions, Tensorflow framework.
15L
TEXT BOOK:
1. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent System s by
AurélienGér on, S econd Edition, O'reilly 2019
2. Deep Learning with Python by François Chollet Published by Manning
2018
3. Reinforcement Learning: An Introduction by Richard S. Sutton and
Andrew G. Bar to, Second Edition 2014
REFERENCEBOOKS:
1. Introduction to Machine with Python - A Guide for Data Scientists by
Andreas C. Müller & Sarah Guido O'reilly 2016
2. Artificial Neural Networks with TensorFlow 2 ANN Architecture Machine
Learning Projects Poornacha ndra Sarang by Apre ss 2021
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Course
Code Course Title Credits
PSCSP201 Practical Course onApplied Machine and Deep
Learning 02
Note: All the Practical’s should be implemented using Python and
TensorFlow.
Link:Python :https://www.python. org/downloads/
TensorFlow :https://www.tensorflow.org/install
1 Implement Linear Regression (Diabetes Dataset)
2 Implement Log istic Regression (Iris Dataset)
3 Implements Multinomial Logistic Regression (Iris Dataset)
4 Implement SVM classifier (Iris Dataset)
5 Train and fine-tune a Decision Tree for the Moons Dataset
6 Train an SVM regressor on the California Housing Dataset
7 Implement Batch Gradient Descent with early stopping for
Softmax Regression
8 Implement MLP for classification of hand written digits (MNIST
Dataset)
9 Classification of images of clothing using Tensorflow (Fashion
MNIST da taset)
10 Implement Regression to predict fuel efficiency using Tensorflow
(Auto MPG da taset)
Course Code Course Title Credits
PSCS202 Natural Language Processing 04
Course Outcome: -
● Understanding the importance and concepts of Natural Langua ge
Processing (NLP)
● Applying algori thms available for the processing of lingu istic information
and computational propert ies of n atural languag es.
● Knowledge on various morpho logical, syntactic, and semantic NLP
tasks.
● Introduc ing various NLP software libraries and data sets publicly
available.
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● Designing and developing practical NLP based applications
Cour se Specific Outcome: -
● The ability to describe the concepts of morpholog y, syntax, semantics,
discourse & pragm atics of n atural language
● Discover various lingui stic and statistical features relevant to the basic
NLP task, namely, spelling correction, morpholog ical analysis, parts-of-
speech tagging, parsing, and semantic anal ysis
● Assess and Evaluate NLP based systems
● Abilit y to choose appropria te solutions for solving typical NLP
subprob lems (tokenizing, tagging, par sing)
● Analyse NLP problems to decompose them inadequate independe nt
compone nts and develop real-life applications
UNIT 1: Introduction to Natural Language Processing (NLP) and
Languag e Modelling
Introduction to NLP: Introduction and appli cations, NLP phas es,
Difficulty of NLP including ambiguity; Spelling error and Noisy
Channel Model; Concepts of Parts-of speech and Formal Gramm ar
of English.
Language Modell ing: N-gram and Neural Langua ge Models
Language Modelling with N-gram, Simple N-gram models, smoothing
(basic techniques), Evaluat ing language models; Neural Network
basics, Training ; Neural Langua ge Model, Case study: application of
neural language model in NLP system developm ent
Python Libraries for NLP: Using Python libraries/packages such as
NaturalLang uage Toolkit (NLTK), spaCy, genism
15L
UNIT 2: Morphology & Pa rsing in NLP
Computational morphology & Parts-of-speech Tagging: basic
concepts; Tagset; Lemmat ization, Early approache s: Rule-based and
TBL; POS tagging using HMM , Introduction to POS Tagging using
15L
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Neural Model.
Parsing B asic concepts: top-down and bottom -up pa rsing, treebank ;
Syntactic parsing: CKY pa rsing; Statistical Par sing basics:
Probabil istic Context-Free Grammar (PCFG); Probabilistic CKY
Parsing of PCFGs.
UNIT 3: Sema ntics and Word Embedding
Semantics Vector Se mantics: Words and Vector; Measuring
Similarity; Semantics with dense vectors; SVD and Latent Semantic
Analysis
Embeddings from predi ction: Skip-gram and Continuous Bag of
words; Concept of Word Sense; Introduction to WordNet
15L
UNIT 4: (Skill Enhanceme nt)
NLP Applications and Case Stu dies
Intelligent Work Processors: Machine Translation; User Interfaces;
man-m achine Interfaces: Natural la nguage Quer ying Tutoring a nd
Authoring System s. Speech Recognition
Commer cial use of NLP: NLP in customer Service, Sentiment
Analysis, Emotion Mining, Handling Frauds and SMS, Bots, LSTM &
BERT models, Conversations
15L
TEXT BOOK:
1. ―Speech and Language Processing‖, Jurafsky Dan and Martin James H.,
3rd Edition, Pea rson, 2018.
2. ―Natural Langua ge Processing with Python‖, Steven Bird, Ewan Klein, and
Edward Loper, 2nd Edition, O’Reilly, 2016.
REFERENCE BOOKS:
1. ―Pra ctical NaturalLangua ge Processing with Python‖, Mathangi Sri,
Apre ss, 2021
2. "Handbo ok of Computational Linguistics and Natural Langua ge
Processing‖, Martin Whitehead, Clanrye International, 2020
3. ―Handbook of Natural Languag e Processing‖, Nitin Indurkhya, and Fred J.
Damerau, Pearson; 2nd edition, 2008
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4. ―Foundatio ns of Statistical Natural Langua ge Processing‖, Mannin g,
Christopher and H einrich, Schutze, MIT Pre ss, 1997
Course
Code Course Title Credits
PSCSP202 Practical Course on Natural Language Processing 02
Note: - The following set of practicals can be performed using any Python
Libraries for NLP such as NLTK, spaCy, genism:
Link:-https://www.python.org/downloads/
1 Write a program to implement sentence segmentation and word
tokenization
2 Write a program to Implement stemming and lemmatization
3 Write a program to Implement a tri-gram model
4 Write a program to Implement PoS tagging using HMM & Neural
Model
5 Write a program to Implement syntactic parsing of a given text
6 Write a program to Implement depe ndency parsing of a given text
7 Write a program to Implement Named Entity Recognit ion (NER)
8 Write a program to Implement Text Summarization for the given
sample text
Apply the concepts and techniques of Natural langua ge processing
learned for real-life applica tions. A suitable application can be modelled
which demonst rates the NLP skills. Some of the concepts/themes f or lab
exercises (not limited to t he following) are described.
9 Consider a scenario of applying NLP in Customer Service.
Design and de velop an application that demonstrates NLP
operatio ns for working with tasks and da ta like voice calls, chats,
Ticket Data, Email Data. Process the data to understand the
voice of the Customer (int ent mining, Top words, word cloud,
classify topics). Identify issues, replace patterns and gain insight
into sales chats.
10 Consider a scenario of Online Review and demonstrate the
concept of sentiment analysis and emotion mining by applying
various approach es like lexicon-based approach and rule-based
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approac hes.
11 Apply NLP in Banking, Financial Services, and Insurance. Design
Appli cation to detect frauds and w ork with SMS data.
12 Demonstrate the use of NLP in designing Virtual Assistants.
Apply LSTM, build conversational Bots.
Course Code Course Title Credits
PSCS203 Web Mining 04
Course Outcome: -
● To Understand the difference between Web Mining and Data mining.
● To Understand the Basics and Needs of Web Mining.
● To Understand Web-based Data.
● To Understand Opinion Mining and Sentiment classification.
Cour se Specific Outcome:
● Develop deep under standing of mining technique s exclusively for the
Internet
● Understand and develop anal ytics for social media data.
● Design and implementation of various web analytical tool to under stand
complex unstructured data on the Internet for aiding individuals and
Businesses to grow their business
UNIT 1: Introduction to Web Mining
Web Mining, Data Mining, Ba sic Concepts, Difference, Mining
Sequent ial Patterns on Prefix Span, Gener ating Rules from
Sequent ial Pa tterns. Basic Concepts of Information Retrieval,
Information Retrieval Models, Relevance feedbac k, Evaluat ion
measures Text and Web Page Preprocessing, Inverted Index and Its
Compression, latent semantic inde xing, Web Search, Web Spamming
15L
UNIT 2: O pinion Mining and Web Usage Mining
Web Information Retrieval, Sentiment Classification, Feature based
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Opinion Mining and Summarization, Comparative Sentence and
Relation Mining, Opinion Search and Opinion Spam. Web Usage
Mining. 15L
UNIT 3: Social Network & Link Analysis
Link Analysis, Scrapy using python (without pipel ining), Social
Network Analysis, Co-Citation and Bibliograph ic Couplin g, Page Rank,
HITS, Community Discovery
15L
UNIT 4: (Skill Enhanceme nt)
Webpag e crawlers and usage mining
Basic Crawler Algorithm, Implem entation Issues, Universal Crawlers,
Focused Crawlers, Topical Crawlers, Crawler Ethics and Conflicts,
Data modelling and webpage usage mining ., Discovery and analysis
of web usage pa tterns, Recommender system s and collabor ative
filtering, que ry log mining
15L
TEXT BOOK:
1. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data by
Bing Liu (Springer Publi cations) 2017 publi cation
REFERENCE BOOKS:
1. Data Mining: Concepts and Techniques, Second Edition Jiawei Han,
Micheline Kamber (Elsevier Publications),2017
2. Web Mining: Applications and Techniques by Anthony Scime,2010
3. Mining the Web: Discovering Knowledge from Hypertext Data by Soumen
Chakrabar ti 2010
Course
Code Course Title Credits
PSCSP203 Practical Course on Web Mining 02
Note: - The following set of practical’s should be implemented in Scrape,
python:
Link:-Python : https://www.python.org/downloads/
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1 Scrape an online E-Commerce Site for Data.
1. Extract prod uct data from A mazon - be it any product and
put these details in the MySQL database. One can use
pipeline. Li ke 1 pipeline to process the scraped data and
other to put data in the databaseand since Amazon has
some restrictions on scraping of data, ask them to work on
small set of requ ests otherwise proxies and all would ha ve
to be used.
2. Scrape the details like color, dimensions, material e tc. Or
customer ratings by features.
2 Scrape an online Social Media Site for Data. Use python to scrape
information from twitter.
3 Page Ra nk for link anal ysis using python
Create a small set of pages na mely page1, pa ge2, page3 and
page4 ap ply random walk on the same
4 Perform Spam Classifier.
5 Demonstrate Text Mining and Webpage Pre-processing using
meta information from the web pages (Lo cal/Onlin e).
6 Apriori Algor ithm implementation in case study.
7 Develop a basic crawler for the web search for user defined
keywords.
8 Develop a focused crawler for local search.
9 Develop a progra mme for deep search implementation to detect
plagiari sm in documents onlin e.
10 Sentiment analysis for reviews by customers and visualize the
same.
Course Code Course Title Credits
PSCS204 Embedded and IoT Technology 04
Course Outcome: -
● The course is designed to e nable students, to understand and implement
IoT in industry.
● Design and e xecutive proje cts in IoT with Automatic Identification and Data
Capture
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Cour se Specific Outcome: -
● Understand basic componen ts and functionalities of Embedded S ystem
including its hard ware.
● Effectively achieve collabor ation of various technolo gies in IoT and enable
the same using software programming like Python, E mbedded C e tc.
● Understand case studies in IoT and replicate the same for more detailed
analysis of the IoT developm ent.
UNIT 1: Embedded System Basics
Introduction to Embedded Systems , Design of Embedded System s,
Memory Architecture, Input/Output. Basic electronics:
Semiconductors, Transistors, BJT, Flip Flops, Resistors, Capac itors,
CMOS, MOSFET, FPGA, Relays. Microcontrollers, UART
Communi cations, SPI-periphera ls interface, I2C communication,
Wireless Sensor Network (WSN)
15L
UNIT 2: Basics of IOT
Introduction IoT:Evolution of the IoT concept, vision and def inition of
IoT, basic characteristics of IoT, distinguish the IoT from o ther related
technologi es, IoT enabler s, IoT architectures, pros and cons of IoT,
IoT architecture concepts for specific IoT appl ications.
IoT Building Blocks -Hardware and Software:The basic IoT building
blocks, smart thing compone nts and capabil ities, basics of Packet
Tracer with reference to IoT, basics of IoT gateway, Cloud, and
analytics
Sensing Principles and Wireless Sensor Network:Sens or
funda mentals and classification of sensors, physical princ iples of
some comm on sensors, basics of WSNs, WSN architecture and
types, layer-level functionality of WSN protocol stack.
15L
UNIT 3: Advanced IOT Technologies
IoT Gateway:IoT architecture domains, IoT gateway architecture, IoT
15L
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gateway functional ities, IoT gateway selection criteria, IoT gateway
and edge computing, edge computing-based solution for specific IoT
appli cations
IoT Pro tocol Stack:Mapping of IoT pro tocols to layered IoT
architecture, functionali ty of infrastructure, service discovery, and
appli cation layer protocols of IoT protocol stack
IoT Cloud and Fog Computing:Components of IoT Cloud ar chitecture,
usage of application domains of IoT Cloud platforms, layered
architecture of Fog computing, distinguish Fog computing from other
related terms
IoT Appl ications:Main applications of IoT, Implementation details of
various IoT application domains
UNIT 4: (Skill Enhanceme nt)
Security, Commu nication and Data analytics in IOT
IoT Security: Security c onstrai nts in IoT systems, security
require ments of IoT system s, IoT attacks, security threat s at each
layer of IoT architecture, design secure IoT system for specific
appli cation
Social IoT: Nature of social relationships among IoT Devices,
functionality of different componen ts of social IoT architecture, social
aspects of s mart devices in IoT applications
Packet Tracer and IoT: Basics of Packet Tracer and Blockly
programming language, des ign simple IoT proj ects in Packet Tracer.
15L
TEXT BOOK:
1. Introduction to Embedded S ystem s – Cyber physical systems Approach
Edward Ashford Lee &Sanj itArunkumarSeshia Second Edition — MIT
Press — 2017
2. Enabling the Internet of Things Fundamentals, Design and Applications by
Muhammad Azhar Iqbal, Sajjad Hussain, Huanlai Xing, Muhammad Ali
Imran Wiley Pub.1stEdition 2021
REFERENCE BOOKS:
1. Introduction Embedded S ystem s by K.V. Shibu Second Edition McGraw
Hills–2017
2. Build your own IoT Platform Develop a Fully Flexible and Scalable Internet
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of Things Pla tform in 24 Hours by Anand Tamboli 2019 Apress
Course
Code Course Title Credits
PSCSP204 Practical Course onEmbedded and IoT Technology 02
Note: - The following set of practicals should be implemented in
CodeV isionAVR, Proteus8, Cisco Packet T racer, Keli V5, Python
Link: -Python:https://www.python. org/downloads/
CodeV isionAVR :https://www .codevi sion.be/
Proteus8:https://www.labcenter.com/downloads/
Cisco Pack et Tracer:https://www .netacad.com/courses/packe t-tracer
Keli V5: https://www.keil.com/download/
1 Design and imple ment basics embedded circuits
1. Automatic Alarm system - Alarm should get tigger by senor
2. Timer based buzzer
3. Sensor based Counting device
2 Demonstrate communication between two embedded devices
using UART port
3 Built an IoT system to send ticket before entering the bus.
4 Demonstrate an IoT based game which can be played between
two player who are physically at a considerable distance.
5 Develop a IoT appli cation which will record the movement and
orien tation of your phone and give the data back to the PC
6 Develop an IoT appli cation that will raise an alarm whenever with
going to rain outside based on the weather predi ction data.
7 Deploy an IoT application which will alert you by beeping or
vibrating your phone whene ver you get someone call your name.
8 Develop an IoT appli cation for monitoring water levels in tanks and
automatically start the motor to fill the tank if the level goes below
the critical level.
9 Develop an IoT module to which measure the intensity of light and
send the same to your PC/ Phone
10 Develop an IoT application for Motion detection.
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5. EVALUATION
The evaluat ion of each paper shall contain two parts:
(i) Internal Assessment- 40 Marks.
(ii) External Assessment - 60 Marks.
The Internal to External assessment ratio shall be 2:3.
6. SCHE ME OF EXAMINATIONS AND DISTRIBUTI ON PATTERN OF
MARKS
SCHE ME OF EX TERNAL EXAMINATIONOF S EMESTER- I AND
SEMESTER – II
The External Theory examination of all semesters shall be conducted by
the University at the end of each semester.
SCHE ME OF INTERNAL EVALUATIONOF S EMESTER- I AND
SEMESTER – II
Internal evaluat ion is to be done by continuou s assessment which will
consist of two componen ts viz.
● Course Specific Project should be done for Each Course of Semester
- I & II. The scope of the Course Specific Project may be within or
beyond the scope of the 4 units and practical’s prescribed for the
Course.
● As signm ents / Q UIZ / Se minars / Case Studies.
The particulars of the Internal examination for each course of Semester- I
and Sem ester – II are given below:
No Semest er Course
Code Particular Marks Total
Marks
1 I / II PSCS101
/
PSCS201 Course Specific Project 30 40
Assignments / Q UIZ / Se minars /
Case Studies. 10
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2 PSCS102
/
PSCS202 Course Specific Project 30 40
Assignments / Q UIZ / Se minars /
Case Studies. 10
3 PSCS103
/
PSCS203 Course Specific Project 30 40
Assignments / Q UIZ / Se minars /
Case Studies. 10
4 PSCS104
/
PSCS204 Course Specific Project 30 40
Assignments / Q UIZ / Se minars /
Case Studies. 10
SCHE ME OF E XAMINA TION FOR P RACTICAL COURSES
There will not be any Internal examination for practical courses of
Semester- I and Semester – II.
EXTERNAL EXAMINATION FOR P RACTICAL COURS ES
The particulars of the external examination for each practical course of
Semester- I and Semester – II are given below :
No Semest er Course
Code Particular No of
Questions Marks Total
Marks
1 I / II PSCSP101 /
PSCSP201 Laborat ory
experiment
question with
internal
choice 01 40 50
Journal 05
VIVA 05
2 PSCSP102/
PSCSP202 Laborat ory
experiment
question with
internal
choice 01 40 50
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Journal 05
VIVA 05
3 PSCSP103/
PSCSP203 Laborat ory
experiment
question with
internal
choice 01 40 50
Journal 05
VIVA 05
4 PSCSP104/
PSCSP204 Laborat ory
experiment
question with
internal
choice 01 40 50
Journal 05
VIVA 05
GUIDELINES OF JOURN ALS
A student should maintain a Journal with Practical experiments reported for
each of the pr actical course of Semester- I and Se mester - II. Related
theori es/algor ithms need to be explained in a journal.
Certified Journal with at least 70% of the list of the Practical need to be
submitted at the time of the practical examination.
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38
AUDIT COURSES
Students who have obtained Undergraduate Degree in subjects other than
B.SC Computer science from a duly re cognized University shall undertake
an O nline Audit Cour ses provided below.
They are suppo sed to submit the online completion certificate of the
same to the concerned departmen t at the end of each semester 1 and
semester 2.
Semest er I
Course
Code Course Name Online Courses
PSCS101 Algori thm for
Optimization Design and analysis of algor ithms
https://online courses.nptel.ac.in/
noc21_cs68/preview
PSCS102 Software
Defined
Networking Demystifying Networking
https://online courses.nptel.ac.in
/noc21_cs94/preview
PSCS103 Applied Signal
and Image
Processing Digital Signal Processing
https://online courses.nptel.ac.in/noc19_ee50/pr eview
Digital Signal Processing and Applications
https://onlin ecourses.nptel.ac.in/noc21_ee20/p review
PSCS104 Advanced
Database
Techniqu es Introduction to Database Systems
https://nptel.ac.in/courses/106/106/10610622 0/
Introduction to Databases
https://nptel.ac.in/courses/106/104/10610413 5/
Semest er II
Course
Code Course Name Online Courses
PSCS201 Applied
Machine and
Deep Learni ng Introduction to Machine Lea rning
https://online courses.nptel.ac.in/
noc21_cs85/preview
PSCS202 Natural
Language An Introduction to Artificial Intellige nce
https://onlin ecourses.nptel.ac.in/noc21_cs42/preview
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Processing
The Joy of Computing using Python
https://onlin ecourses.nptel.ac.in/noc21_cs75/preview
PSCS203 Web Mining Web Scraping without S crapy
https://www.udemy.com/course/webscr aping -
without-scrapy/
PSCS204 IoT
Technology Design for internet of things
https://online courses.nptel.ac.in/noc21_ee85/pr eview
Introduction to Industry 4.0 and Industrial Internet of
Things
https://online courses.nptel.ac.in/noc21_cs66/preview
Introduction to internet of things
https://online courses.nptel.ac.in/
noc21_cs63/preview