This document provides information about the Engineering Minor in Data Science offered by the School of Computer Science and Engineering. It describes what engineering minors are, lists the courses offered in the Data Science minor, and provides brief descriptions and outcomes of each course. The minor consists of six courses spanning four semesters that cover topics like data management, visualization, programming in R, predictive analytics, big data fundamentals, and cluster computing. The document also discusses career opportunities, industrial applications, special requirements, and contacts for additional information about the minor.

1. Engineering Minors
School of Computer
Science and Engineering

2. What are Engineering
Minors?
• A set of six courses in an engineering
stream to develop a competency within or
outside the discipline area (e.g. a Computer
Science Engineering student choosing
Cyber Security or Machine Learning or a
Mechanical Engineering student taking
courses from Computer Science
Engineering as his/her Engineering Minor
Area).
• These Engineering Minors are being
offered so that each student would have at
least one major skills by the end of 8th
term.
Engineering
Minors
Software
Methodologies
and Testing
Machine
Learning
Cloud
Computing
Full Stack Web
Development
Cyber
Security
Data Science

3. How Engineering Minors can help the students?
Engineering minors (EM) are the elective courses in engineering to enhance
competency in the technical and/or emerging domain.
A student can chose an Engineering minor basket comprising of six courses from
his/her own school or multi-disciplinary area can also be chosen.
The Engineering Minors allows a student to gain interdisciplinary experience and
exposure to concepts and perspectives that may not be a part of their degree
programs thus widening their understanding of the engineering.
Engineering minors also provides the freedom to students for designing their
degree program, choose their course curriculum and decide their future career.

4. Additional Certificate as Minor
A student completing the set of six courses chosen as Engineering minor and earning 18 credits would be
eligible for an additional certificate as minor.
Students can study the specialized courses as per their interest/requirements of industry leading to
attaining of specialized skill set, and the additional minor certificate earned alongwith the degree would
give an edge to the students and would also be beneficial in enhancing employability opportunities.

5. Important points to remember during polling process
Interfaces to be followed Interface not to be followed
2.LPU Touch App
1. UMS
• Polling for Engineering Minor can be done
through both UMS and LPU touch App
Never use UMS through mobile browser
for polling process as it may result in
change in preferences
Note: Link for the UMS pathway and LPU Touch App will be shared with the announcement
for Engineering Minor(s) polling in the near future.

6. Number of Seats
To be allotted on the basis of preference number and CGPA
840
280
770
490
630
280
0
100
200
300
400
500
600
700
800
900
Data Science Software
Methodologies
and Testing
Full Stack Web
Development
Machine Learning Cyber Security Cloud Computing

7. 6 Courses Spanned across 4 semesters
AUG SEP OCT NOV JAN FEB MAR APR AUG SEP OCT NOV JAN FEB MAR APR
1st course
5th Term 6th Term 7th Term
2nd course
3rd course
4th course
5th course
6th course
8th Term

8. Data Science
Data Analytics (DA) refers to the set of quantitative
and qualitative approach in order to derive valuable
insights from data. It involves many processes that
include extracting data, categorizing it in order to
analyze the various patterns, relations, connections
and other such valuable insights from it. DA is the
key technology behind the success of Google,
Amazon and Flipkart.
INT217
Introduction to Data
Management
INT233
Data Visualization
INT232
Data Science Toolbox :
R Programming
INT234
Predictive Analytics
INT312
Big Data Fundamentals
INT315
Cluster Computing

9. Description of Courses
INT217 : INTRODUCTION TO DATA MANAGEMENT
This course introduces different ways by which an organization can manage, store and retrieve
data using different database management tools. This course will enhance the skills of using
spreadsheet by implementing various analytical functions and presenting in graphical way.
Course Outcome:
• Apply the various techniques and functions over spreadsheet for getting various insides of
data.
• Understand the data representation methods like pivot table and power pivoting.
• Understand the need of the graphical representation in the spreadsheet by using various
graphs and charts.
• Comprehend the concepts of multidimensional data and topics like OLAP, OLTP and data
cube.
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10. Description of Courses
INT232 : DATA SCIENCE TOOLBOX : R PROGRAMMING
Covers the basics of data cleaning and how to make data presentable by using analysis
techniques. The course will also cover the components of a complete data set including raw
data, processing instructions, codebooks, and processed data.
Course Outcome:
• Analyze and configure R software for statistical programming environment and describe
generic programming language concepts implemented in a high-level statistical language.
• Establish Program in R environment to create custom analytical models to meet the dynamic
business needs evaluate and verify the analysis findings by conducting various statistical tests
used for hypothesis testing.
• Review advanced data science concepts using predictive analytics fundamentals.
• Visualize the various graphical packages for creating various types of graphs, plots and charts
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11. Description of Courses
INT233 : DATA VISUALIZATION
This course is specifically focused on how data analysis is used in business and provide basic
grounding in concepts such as over fitting, and error rates, analyzing data of customer, product
and sales to make effective decision for business growth.
Course Outcome:
• Experience the role of visualization for analytics in an organization.
• Use data visualization principles to help you to design dashboards that enlighten and support
business decisions.
• Acquire knowledge of data representation and sub-setting techniques for real time datasets.
• Use and customize the various graphical packages for creating various types of graphs, plots
and charts.
• Analyze real life business problems by using various visualization techniques.
• Integrate data to provide mashed-up dashboards.
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12. Description of Courses
INT234 : PREDICTIVE ANALYTICS
The course is about breaking down the data, assess trends over time, compare one
sector/measurement to another and even ask questions about the future.
Course Outcome:
• Review the art and science of predictive analytics to define clear actions that result in
improved decisions and business results.
• Evaluate the use of analytic tools and assist in the selection of industry standard analytics
tools.
• Construct and format data to be most effective to ensure the predictive model meets the
business goals.
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13. Description of Courses
INT312 : BIG DATA FUNDAMENTALS
This course provides basic concepts and terminologies of Big Data and its real-life applications across
industries. One will gain insights on how to improve business productivity by processing large
volumes of data and extract valuable information from them.
Course Outcome:
• Recognize the need and importance of fundamental concepts and principles of Big Data
• Examine internal functioning of different modules of Big Data and Hadoop
• Conceptualize the big data ecosystem and appreciate its key components
• Review quantitative and qualitative understanding of methods and algorithms for processing big data
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14. Description of Courses
INT315 : CLUSTER COMPUTING
This course provides an introduction to one of the most common frameworks, Spark that has
made big data analysis easier and more accessible -- increasing the potential for data to
transform our world!
Course Outcome:
• Review Spark hardware requirements and estimate cluster size
• Gain insight from real-world production use cases
• Tighten security, schedule resources, and fine-tune performance
• Overcome common problems encountered using Spark in production
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15. Career Opportunities
Big Data Engineer
Big Data Architect
Data Administrator
Data Warehouse Manager
Database Manager
Data Modeller
Business Intelligence Analyst

16. Industrial Applications
Data Science is best tool for decision making. It is widely used in industries for targeted Advertising
and re-targeting, Recommender Systems, Image Recognition, Speech Recognition, Gaming, Price
Comparison Websites, Airline Route Planning, Fraud and Risk Detection and Self Driving Cars.

17. Special Requirements
Laptop
Basic knowledge of Databases
2 hours class scheduling

18. Doubts ?
Ms. Mrinalini Rana| mrinalini.22138@lpu.co.in | 25-202
Dr. Amritpal Singh | amritpal.17673@lpu.co.in | 25-202

19. Software Methodologies
and Testing
This Engineering Minor enables the students to learn the
concepts of Advance Software Engineering, the basics of Software
Testing along with types of testing and to apply the testing
techniques successfully on the software projects.
The courses of this minor emphasis on providing the training in
testing of the software. After completing all the courses of this
minor, students can start their carrier in the field of software
testing.
CSE374
Advance Software
Engineering
CSE375
Software Testing
CSE376
Automated Testing
CSE377
Advance Testing
Technologies
CSE378
CSE379
Web Services API
Automation based
Testing
Mobile Automated
Testing

20. Description of Courses
CSE374: ADVANCE SOFTWARE ENGINEERING
This is the first course of EM which includes the advance topics of software engineering. This
course palys an improtant role in making the students aware about advance concepts of
software engineering.
Course Outcome:
• Students will be able to learn and explain various advance concepts of software engineering.
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21. Description of Courses
CSE375 : SOFTWARE TESTING
This is the second course of EM which includes the basic concepts of software testing and
various types of software testing. This course helps the students in understanding the testing
concepts which can be applied while testing the software.
Course Outcome:
• Students will be able to learn and explain the basics of software testing.
• Students will be capable of designing the test cases.
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22. Description of Courses
CSE376 : AUTOMATED TESTING
This is the third course of EM which includes hands-on practices on various software testing tools
like Eclipse, Selinium etc. Students will test various applications using the tools.
Course Outcome:
• Students will be able to perform the software testing using Selinium and Eclipse.
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23. Description of Courses
CSE377 : ADVANCE TESTING TECHNOLOGIES
This is the fourth course of EM which includes advance technologies related to software testing.
Course Outcome:
• Students will be able to understand various advance testing technologies and apply them for
testing the software.
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24. Description of Courses
CSE378 : WEB SERVICES API AUTOMATION TESTING
This is the fifth course of EM which includes the techniques of webservices API testing. RESTful
API services will also be the main part of the course contents
Course Outcome:
• Students will be able to perform web services API testing
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25. Description of Courses
CSE379 : MOBILE AUTOMATED TESTING
This is the sixth course of EM in which techniques of mobile automated testing will be taught. The
testing will include both Android and IOS apps. Students will get hands-on practice on Appium.
Course Outcome:
• Students will be able to perform mobile automated testing using Appium
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26. Career Opportunities
QA Analyst
Software Tester
Software Test Engineer

27. Industrial Applications
The courses of this EM are applicable in all software development companies
because testing is one of the important phase of development life cycle and this
Engineering Minor track trains the students on different testing tools.

28. Special Requirements
Laptop
Software Engineering course
2 hours class scheduling

29. Doubts ?
Dr. Sudhanshu P. Tiwari | sudhanshu.15813@lpu.co.in |26-202

30. The selection criteria for allocation of Engineering Minor will be based on:
a) First come First Serve basis
b) Order of preferences and CGPA
c) Test

31. FULL Stack
Web Development
.
The tem full stack web development means complete
website development including front-end, back-end and
database. On this track students will learn different
technologies related to web development like HTML, CSS,
JavaScript, Node.js, React.js, PHT, Laravel, flask etc
• Can you believe your life without Internet?
• Are you fascinated after seeing websites which tempt
you to buy from online market places?
• This Minor offers all stuff to build efficient and
effective websites. Design your own website!.
INT219
Front End Web Developer
INT252
Web App Development
With REACTJS
INT222
Advanced Web
Development
INT220
Server Side
Scripting
INT253
Web Development in
Python using DJANGO
INT221
MVC Programming

32. Description of Courses
INT219 : FRONT END WEB DEVELOPER
Introduces an open source language for building and enhancing the performance of web applications
on the server side along with other client side technologies like HTML5, JAVASCRIPT, CSS and web
hosting.
Course Outcome:
• Design dynamic website using HTML5, CSS and Advanced JavaScript.
• Explore the enhanced techniques used by web professionals for creating dynamic web pages.
• Apply the principles and tools that are used to develop Web applications.
• Implement JavaScript, jQuery, AngularJS and Bootstrap in web pages.
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33. Description of Courses
INT222 : ADVANCED WEB DEVELOPMENT
Covers the concepts of Node.js for building web sites with better user interface and responsive web
pages. A hands on exposure on Node.js technology will be offered in a form of project.
Course Outcome:
• Understand the role of server side JavaScript in web application development.
• Explore, how Node.js is architectured to allow high scalability wth asynchronous code build
an HTTP Server using the core modules in Node.js.
• Create basic web based applications using Node.js.
• work with MongoDB database using Node.js.
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34. Description of Courses
INT252 : WEB APP DEVELOPMENT WITH REACTJS
This course serves as an introduction to modern web application development with JavaScript. The main
focus is on building single page applications with ReactJS.
Course Outcome:
Through this course students should be able to
• understand advanced javascript concepts and create a pure react app
• develop JSX components and use props in React app
• compose and manipulate states and should develop an understanding of events & Hooks
• use forms with state and validating the form for errors and display errors
• make a react app by using HTTP methods and routing the pages
• validate or debug the react app and deploy app onto the server
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35. Description of Courses
INT220 : SERVER SIDE SCRIPTING
Introduces basic concepts of PHP & MySQL technology for building and enhancing the performance
of web applications on server-side along with other client-side technologies like HTML5, JAVASCRIPT,
CSS etc.
Course Outcome:
• Understand process of executing a PHP-based script on a web server
• Process the data provided on the form by a user in a PHP-based script
• Utilize paradigm for dealing with form-based data and how they are accessed inside a PHP-
based script.
• Implement the syntax and use of PHP object-oriented classes.
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36. Description of Courses
INT221 : MVC PROGRAMMING
Covers in depth knowledge of MVC framework which is an advanced framework based technology
with three components that are database components (Models), client-side frames (View) and
Server-Side components (Controller) which are used in web building.
Course Outcome:
• Design flexible, user-friendly and clean web applications.
• Develop highly versatile and stable web programs based on Laravel structure framework.
• Provide well-organized, reusable and maintainable code.
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37. Description of Courses
INT253 : WEB DEVELOPMENT IN PYTHON USING DJANGO
Covers the Web Development in Python using Django. Python Django is a web framework that
allows to quickly create efficient web pages.
Course Outcomes:
Through this course students should be able to
• Describe Python Programming, Flask Framework And Django Framework
• Discuss the concepts of views, models and session for the framework.
• Use variables to store, retrieve and calculate information
• Explain the core programming tools such as functions and loops
• Assess and build a data model in Django and Flask, applying model query and template
tags/code
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38. Career Opportunities
MEAN Stack Trainer
MEAN Stack Web Developer
Web Developer
Web Designer
Entrepreneur

39. Industrial Applications
Publishing
Advertising
E-Commerce
Social Networking Sites
Websites

40. Special Requirements
Laptop
Basic knowledge of Computers
2 hours class scheduling

41. Doubts ?
Dr. Navneet Kaur | navneet.24950@lpu.co.in | 34-203
Dr. Balwinder Kaur | balwinder.25673@lpu.co.in | 34-203

42. What are the interfaces through which the student can participate in giving
preferences of Engineering minor(s)?
a) Only through UMS
b) Only through LPU Touch
c) Both through UMS and LPU Touch
d) Through any mobile browser

43. Machine Learning
Machine Learning (ML) is an application of Artificial Intelligence (AI)
that provides systems the ability to automatically learn and improve
from experience without being explicitly programmed. Machine
Intelligence represents a key evolution in the fields of artificial
intelligence, data analysis, and software engineering. This area will
teach you ABC of Machine Learning, and help you in applying
predictive models to massive data sets in fields like finance,
healthcare, education, and more.
INT254
Fundamentals of Machine
Learning
INT354
Machine Learning-I
INT344
Natural Language Processing
INT423
Machine Learning-II
Computer Vision
Deep Learning
INT345
INT422

44. Description of Courses
INT254 : FUNDAMENTALS OF MACHINE LEARNING ALGORITHMS
This course focuses on mathematical concepts of ML such as how linear algebra relates to data
and Multivariate Calculus optimizes fitting functions to get good fits to data. Hands on practice will
be done using python libraries for analyzing and visualization of the data and performing feature
engineering so that ML models can be applied over it. It also covers different swarm intelligence
algorithms that are inspired by natural systems and genetic algorithms.
 describe the soft computing techniques in building the intelligent machines.
 explain different neural networks for classification and clustering problems.
 use fuzzy logic and reasoning to handle uncertainty and solve engineering problems.
 compare and contrast genetic algorithms and swarm intelligence for optimization problems.
 justify the performance and time complexity of hybrid systems.
 develop the optimal models using available soft computing tools to solve real world problems.
Course Outcome:
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45. Description of Courses
INT354 : MACHINE LEARNING-I
In this course, students will create classifiers such as like logistic regression, decision tree,
boosting, SVM that provide state-of-the-art performance on a variety of tasks. .This course is
hands-on, action-packed, and full of visualizations and illustrations of how these techniques will
behave on real data. Evaluate models using precision-recall metrics and tune hyper parameters of
the models. Also, various regression models will be discussed to predict continuous values.
Course Outcome:
• describe the concepts of classification and regression algorithms.
• examine meaningful features from a given dataset by learning preprocessing skills
• apply the validated machine learning models in given situation for an available dataset
• identify the dimensionality reduction using lda, pca and kpca.
• evaluate the problem that categorize into supervised, unsupervised and reinforcement
learning
• develop a machine learning model to solve a real-world problem
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46. Description of Courses
INT344 : NATURAL LANGUAGE PROCESSING
This course introduces Natural Language Processing through the use of python and the Natural
Language Tool Kit. It mainly focuses on text classification, language modelling, sequence tagging,
word embeddings etc. Through a practical approach, students will get hands on experience working
with and analyzing text. Students will be able to formulate solutions to text-based problems.
Course Outcome:
• explore and gain broad understanding of text data
• analyze sentiments of text document
• use NLP methods to perform topic modeling
• implement projects based on natural language processing
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47. Description of Courses
INT423 : MACHINE LEARNING-II
This course focuses on various clustering techniques such as K-Means, hierarchal clustering and
density-based clustering. Through a practical approach, students will get hands on experience using
python on real world dataset. This course also introduces the concept of reinforcement learning and
formalizes the problems as Markov decision processes. The students will learn and implement value
functions concepts for optimizing decision making processes.
Course Outcome:
• categorize the machine learning problems based on learning rules.
• apply the key concepts that form the core of machine learning.
• develop the key algorithms for the system that are intelligent enough to make the
decisions.
• contrast the statistical, computational and game-theoretic models for learning.
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48. Description of Courses
INT422 : DEEP LEARNING
The Deep Learning course provides a pathway for student's to take the definitive step in the world of AI
by helping them gain the knowledge and skills to level up their career. This course mainly focuses on
fundamental of deep learning, convolutional neural network, recurrent neural network, autoencoders,
generative adversial networks etc. Hands on experience will be done using Tensorflow and Keras.
Students will be able to build and train deep neural networks, identify key architecture parameters,
implement convolutional and recurrent neural networks and deep learning to applications
• describe the deep learning algorithms which are more appropriate for various types of learning
tasks in various domains.
• compare detection and recognition tasks using convolution and adversarial neural networks.
• use dropout regularization, gradient descent, Batch normalization, and optimization algorithms
with convergence
• examine recurrent neural networks for modelling sequential data.
• assess the different learning models and prioritize their appropriate hyper parameters
• construct artificial neural networks with Tensorflow and Keras
Course Outcome:
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49. Description of Courses
INT345 :COMPUTER VISION
This course introduces computer vision, including fundamentals of image formation, camera imaging
geometry, feature detection and matching, stereo, motion estimation and tracking, image classification,
and scene understanding. Hands on experience will be done using python and openCV.
Course Outcome:
• understand models and methods in the field of computer vision
• solve problems in image processing and computer vision
• analysis of existing computer vision system
• design and development of a working computer vision-based system
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50. Career Opportunities
Machine Learning Engineer
AI Data Analyst
Business Intelligence Developer
Data Scientist
Computational Linguist
Human Centered Machine Learning Designer
Robotics Scientist

51. Industrial Applications
� ML in Finance
� ML in Healthcare
� Dynamic Price Control in Travel Industries
� Machine Learning in social media
� ML for language translation and text generation
� ML in Retail/Logistics
� ML in Automation/Robotics Systems
� ML in Pipelining/Production
� ML in Smart Agriculture/Forecasting
� ML in Academics/Personalized Recommendation

52. Special Requirements
Laptop
Basic knowledge of Python
2 hours class scheduling

53. Doubts ?
Dr. Usha Mittal | usha.20339@lpu.co.in | 33-203
Ms. Pooja Rana | pooja.20992@lpu.co.in | 33-203

54. Cyber Security
Cyber security transform into a frightening new reality, one
where corporate and government organizations seem
helpless to stop cyber incursions that leads to cyber risk. To
mitigate the cyber risk we need knowledge of cryptographic
techniques, traffic monitoring and ethical hacking
knowledge for better understanding of security issues to
protect our network.
INT242
Cyber Security Essentials
INT249
System
Administration
INT250
Digital Evidence
Analysis
INT244
Secure Computing
Systems
INT245
INT251
Penetration
Testing
Malware Analysis and
Cyber Defence

55. Description of Courses
INT242 : CYBER SECURITY ESSENTIALS
It covers the concepts of cyber security with its importance and identifies various vulnerabilities and
security flaws in the operating systems and networks. It helps to analyze risks, security threats,
implementing host/software/network security, understanding various types of network attacks and also
focuses on network security programming (using python).
CO1 :: illustrate the concept of information security, threats and vulnerabilities
CO2 :: identify the basic concept of cryptography , authentication and account management control
CO3 :: discuss the security appliances and protocols to secure the networks
CO4 :: analyze how to secure the mobile system, application concept and cloud solutions
CO5 :: examine the procedures for incident response , digital forensics, cyber security and physical security
CO6 :: apply the port scanning, socket creation and web crawling using python programming
Course Outcome:
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56. Description of Courses
INT249 : SYSTEM ADMINISTRATION
This course covers the in-depth practical administration of Windows and Linux server operating systems in
which the focus shall be on file and folders management, Disk management, resource management,
network management, user management, authentication management and software package
management. The tools used are Port scanners, Sniffers, Partitioning tools, Disk property tools etc.
CO1 :: discuss the installation process of windows server and virtual environment
CO2 :: administer the basic server configurations and perform implementation of storage solutions
CO3 :: apply server hardening techniques
CO4 :: enumerate the process of managing, permissions and storage in linux
CO5 :: examine different files, directories, kernel modules and boot process
CO6 :: analyze devices, networks and packages in linux operating system
Course Outcome:
L T P CREDITS
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57. Description of Courses
INT250 : DIGITAL EVIDENCE ANALYSIS
This course covers the in-depth understanding and practical demonstrations of digital forensic and incident
response methods. The course includes incident response handling, forensic duplication, live data
collection, network evidence collection and evidence handling. The course also focuses on data analysis
from system memory and network-based evidence. The tools used are the FBI toolkit, Netcat, Cryptcat,
RAM dump, Encase, TCPdump, FTK imager, FTK, Autopsy etc.
Course Outcome:
CO1 :: describe the fundamentals of incident response handling process.
CO2 :: discuss the methodology of detecting an incident and responding to it in case of a security breach.
CO3 :: examine the process of live data collection and forensic duplication during forensic investigations.
CO4 :: outline the network and host-based evidence collection during the evidence handling process.
CO5 :: classify various data analysis techniques for network and system evidence data.
CO6 :: evaluate the process of extracting critical data from windows systems and routers
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58. Description of Courses
INT244 : SECURING COMPUTING SYSTEMS
The course covers the basic concepts of ethical hacking along with the analysis of various vulnerabilities and
security flaws exist in the operating systems, web servers, and network equipment through the techniques
used by hackers. The course provides the understanding about cryptography, footprinting, scanning,
enumeration, system hacking, malware, sniffers, social engineering, DoS, session hijacking, SQL injection,
mobile & cloud device security. It also helps to clarify & learn SIEM and SOC solutions.
Course Outcome:
CO1 :: define the basic concepts of operating systems, cryptography and ethical hacking
CO2 :: demonstrate various methods of performing footprinting and scanning the target systems
CO3 :: illustrate the process of enumerating and compromising a target system
CO4 :: examine the usage of sniffers, social engineering techniques and denial of service attacks for compromising the
target
CO5 :: analyze the functionality of session hijacking, web applications and SQL injection in testing the security of target
CO6 :: list the process of identifying the threats to WiFi, Bluetooth, mobile devices, cloud services and implementing
SOC and SIEM solutions
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59. Description of Courses
INT245 : PENETRATION TESTING
It covers the tools and technologies used by penetration testers in order to identify weaknesses in
information system and recommend mitigation measures to increase the resistance of the information
technology (IT) infrastructure to unauthorized access.
Course Outcome:
CO1 :: outline methods of penetration testing used by security analyst
CO2 :: list various regulatory compliances related to penetration testing responsibilities
CO3 :: identify pre-penetration testing requirements and footprinting through social engineering
techniques
CO4 :: understand vulnerabilities assessment tools and techniques to evaluate the security of IT
infrastructure
CO5 :: determine steps to conduct internal network and external penetration testing
CO6 :: illustrate different testing deliverables out of penetration testing reports and suggest post
corrective actions
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60. Description of Courses
INT251 : MALWARE ANALYSIS AND CYBER DEFENCE
This course will provide a depth understanding of different types of malwares, like viruses, worms, and
Trojans. Students will gain hands-on experience in analyzing malicious files, identifying malicious
processes, and more. It also covers new techniques and tools of malware analysis which help speed up
analysis and transition students from malware analyst to reverse engineer and how to perform dynamic
and static analysis. The tools used are Pestudio, Process Monitor, Wireshark, Autoruns, Cuckoo Sandbox
etc.
CO1 :: understand the tools and techniques to extract metadata information from the malicious binary
CO2 :: analyze the behavior of the malware and its interaction with the system
CO3 :: understand basics of assembly Language and the necessary skills required to perform code
analysis
CO4 :: analyze the stealth techniques used by advanced malware to hide from Forensic tools
Course Outcome:
L T P CREDITS
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61. Career Opportunities
Cyber Security Consultant
Penetration Tester
Malware Analyst
Cyber Threat Management
Cyber Intelligent Analyst
Information Security Analyst
Freelancer

62. Industrial Applications
� Intelligence agencies
� Defense
� Automation systems
� Retail
� Banking
� Internet of Things
� Automotive sector
� E-commerce

63. Special Requirements
Laptop
2 hours class scheduling
Basic knowledge of Linux and Networking

64. Doubts ?
Mr. Atul Malhotra | atul.18011@lpu.co.in | 26-202
Ms. Harpreet Kaur| harpreet.23521@lpu.co.in | 26-202

65. Cloud Computing
INT330
Managing Cloud Solutions
INT362
Cloud Architecture and
implementation-I
INT364
Cloud Architecture and
implementation-II
INT327
Cloud Infrastructure and
Resource Management
INT363
INT328
Cloud
Microservices
Network virtualization
and Cloud Security

66. Description of Courses
INT330:MANAGING CLOUD SOLUTIONS
Course Outcome:
Describe cloud services offered by cloud providers
Plan for cloud service implementations
Understand cloud services offered by cloud provider
Analyze various Compute Engine services in Public cloud
Understand Storage Volume and Elastic Compute Cloud creation and its management.
Discover the benefits of Auto Scaling and Elastic Load Balancer feature in Public cloud
This course is to teach advantages and working with public cloud infrastructure, A public cloud is an IT model
where public cloud service providers make computing services—including compute and storage, develop-and-
deploy environments, and applications—available on-demand to organizations and individuals over the public
internet.
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67. Description of Courses
INT362:CLOUD ARCHITECTURE AND IMPLEMENTATION-I
Course Outcome:
This course allows the user to understand structure in private cloud and add features to it. Apache CloudStack is
an open source infrastructure as a service (IaaS) platform that allows IT service providers to offer public cloud
services. CloudStack can also be used by businesses that want to provide their own private cloud and hybrid
clouds services on-premises.
The basic components of CloudStack—management servers, types of storage,
networking architecture, and so on
Various deployment models of CloudStack
The different types of installations of CloudStack
The use cases for different types of deployment of CloudStack
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68. Description of Courses
INT363: CLOUD MICROSERVICES
Course Outcome:
Understand the benefits of containers
Build and run a container image
Understand Kubernetes architecture
Write a YAML deployment file
Expose deployment as a service
Manage applications with Kubernetes
This course introduces you to containers and explains how containers differ from virtual machines. It also
covers the importance of containers in cloud computing as well as the emerging ecosystem of related
technologies such as Docker, Kubernetes, OpenShift, and Istio. Containers and Cloud Native are the most
significant invention in IT since the introduction of virtualization. Everyone from a small startup to a large
multinational corporation is transitioning to this technology and they are looking for people who have the skills.
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69. Description of Courses
INT364:CLOUD ARCHITECTURE AND IMPLEMENTATION-II
Course Outcome:
The process of configuring the CloudStack environment
Adding IT infrastructure to CloudStack. Role-based access mechanisms for managing users in
the CloudStack
Various terminologies used in CloudStack such as domains, accounts,
projects, and so on
The working of domains, accounts, projects, and users
This course will give you the core skills needed to build and operate your CloudStack private cloud. The course is a
combination of instructor led learning and hands-on labs. Using hands-on labs, participants will be guided through
the process of creating a new zone, system VM’s, configuring service Offerings, domains, accounts and virtual
private clouds. The instructor will also share extensive knowledge on CloudStack deployments, offering advice on
best practises and tips on how to design their own deployment.
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70. Description of Courses
INT327: CLOUD INFRASTRUCTURE AND RESOURCE MANAGEMENT
Course Outcome:
classify the key technologies and capabilities required for working with virtualization and cloud
computing infrastructure
measure the need of cloud based infrastructure using VMware virtualized platforms
examine the concepts of cloud based infrastructure in software defined data-centers
analyze the business benefits of deploying the cloud data centers
identify how VMware products such as vSphere,vCenter server and vRealize operations help in
understanding the cloud environment
This course allows you to understand resource management which refers to the operations used to
control how capabilities provided by Cloud resources and services are made available to other
entities, whether users, applications, or services.
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71. Description of Courses
INT328:NETWORK VIRTUALIZATION AND CLOUD SECURITY
Course Outcome:
identify the key requirements for working in the NSX platform
examine the need of network virtualization and security in modern data centers
classify the security requirements for on premises and public cloud environments
analyze the impact of AppDefense in security of endpoints
understand how the networking functions and services are implemented within the NSX platform
This course you will learn the fundamentals of cloud computing and understand the standard of expertise for
cloud security, this gives you a cohesive and vendor-neutral understanding of how to secure data in the cloud. It
also allows you to understand the better practices implemented in preserving privacy and coping with other
security based issues in cloud.
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72. Career Opportunities
Cloud administrator
Cloud developer
Cloud engineer
Cloud architect
DevOps Engineer
Cloud security professional
Chief cloud officer

73. Industrial Applications
One of the most beneficial cloud computing uses for many engineers is creating
the best possible testing and development environments. Creating such an
environment would have previously necessitated a huge investment of time,
money, and human resources. Because this type of setting is not cost-effective, it
will put a burden on a project's budget and deadline. Cloud computing, on the
other hand, allows you to access these resources instantaneously and at a
substantially lower cost. Using this function, it automatically allocates virtual and
physical resources.

74. Special Requirements
Laptop with Good configuration
Basic knowledge of Networking
Basic knowledge of Operating systems

75. Doubts ?
Dr. Gurpreet Singh | gurpreet.176726-2051@lpu.co.in | 26-205
Gunseerat Kaur | gunseerat.22157@lpu.co.in | 26-205
Madhuri | madhuri.23525@lpu.co.in | 26-205

76. Internet of Things – 144
Other Engineering Minors – Offered by SECE

77. UMS Navigation-------->Feedback & Surveys-------->Online Survey
Survey on Engineering Minor Presentation
Mandatory Feedback
Dear Students,
Provide the feedback for this session on Myclass as well as on UMS at
below given link:

78. Thank you