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BTech IoT and Automation

An industry relevant course to chart your journey in Industry 4.0

Why needed?

We are living in a world where human to device and device to device communication is becoming normal. Devices range from the simple light bulb in our homes to robots and machines talking to each via the internet to perform tasks without human intervention leading to Internet of Things (IoT) and autonomous systems. According to Statista, our world will see more than 32.1 billion IoT devices in 2030. With the human population expected to reach 8.6 billion in 2030 as per United Nations, there would be approximately four IoT devices for each one of us.

IoT is used for automation of industrial production leading to the specialized domain of Industrial Internet of Things (IIoT) thus transforming our industry giving rise to Industry 4.0, also called IR4 or the Fourth Industrial Revolution. Our society too is changing rapidly due to advances in digital technologies leading to Digital Transformation of society. With IoT generating exponential data, AI, ML and Data Science technologies are used to glean meaningful information from the big data. Going forward, IoT and Automation are being increasingly applied to solve challenges such as climate change, water crisis, healthcare and agri as well as supply chain management and smart and sustainable cities.

Given this context, there is a strong need for IoT and Automation design engineers. NIIT University’s (NU) B.Tech in IoT and Automation program is designed to equip students with the knowledge and skills needed to create smart systems and intelligent automated solutions. The program provides a solid foundation in designing Internet of Things (IoT) systems integrated with Artificial Intelligence (AI) and Robotics. Students gain expertise through courses such as IoT and Sensor Networks, Embedded Systems, Automation and Process Control, Artificial Intelligence, and Machine Learning. The program emphasizes an interdisciplinary approach, combining Electronics, Communication, and Computer Science as a horizontal framework, with a specialized focus on IoT and Automation as its vertical core.

NIIT University introduces flexible curricular architecture through Industry Practice and Research Practice

In keeping with NU’s Core Principle of Industry-linked, BTech IoT and Automation students undertake a compulsory six-month  Industry Practice (IP)  at the industry site and are jointly supervised by industry and faculty mentors.

Industry Practice (IP) has been widely appreciated by NU’s industry partners with a majority of students been given a pre-placement offer (PPO).  Industry organisations have also expressed interest in longer duration IP.

Further NEP 2020 (National Education Policy 2020) too actively encourages internships with industry to improve employability of students.  It also suggests offering research internship opportunities with faculty researchers in own institution or other institutions and research organisations.  This has been reflected in UGC and AICTE policy and guidelines.

With this in mind, NU provides undergraduate students with three curricular architectures, including a revolutionary 1-Year Research Practice in the place of a single fixed architecture. This gives flexibility to students to craft their own industry/research engagement keeping their preference and motivation by choosing any one of the three curricular architectures.

The three curricular architectures are as follows:

  1. Curricular architecture 1: 6-month Industry Practice in the final eighth semester
  2. Curricular architecture 2: 1-Year Industry Practice spreading across two semesters in the final year
  3. Curricular architecture 3: 1-Year Research Practice spreading across two semesters in the final year

BTech IoT and Automation – Distinguishing Features

Like all our other flagship programmes, the BTech IoT and Automation course is designed around NU’s core principles of providing industry-linked, technology-based, research-driven and seamless education.

Our BTech IoT and Automation programme places you on a high-growth career journey that is both rewarding and fulfilling. Here’s how:

Industry-linked course architecture/curriculum

The curriculum and architecture aim to provide students dual advantages of academic rigor and real-world industry experiences. The curriculum has been developed with inputs from industry experts and faculty researchers. Students work and interact with industry and research institutions from the first year itself.

Industry-academic synergy for real-life immersive learning

The programme offers an immersive experience. Students of BTech IoT and Automation work on two capstone projects, an R&D (research and development) project, and undertake a 6-month long Industry Practice.

Students opting for 1-Year Industry Practice or Research Practice will undertake one Capstone project and engage in Industry Practice or Research Practice during the last two semesters.

Top-notch faculty

Our faculty comes with rich prior work experience in teaching, research, industry and the government. Their research has been published in several international journals and conference proceedings. Our faculty members have been preparing industry-ready AI, Data Science, CSE, Cybersecurity, ECE specialists for several years.

State-of-the-art infrastructure

True to its core principle of Technology-based, NU offers high-tech laboratories with all the necessary software and equipment to assist students in their experiential learning journey.

Unique teaching methodology

Several unique teaching methods are integrated into the project-based learning approach at NU. This approach helps students to develop critical thinking skills to engage deeply with subjects, interdisciplinary skills to see connections between subjects, collaborative skills to work as a team to solve a real-life problem. They become independent and self-directed learners who can chart their own learning journey successfully.

Read more about how NU students gain a distinct competitive edge.

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Programme outcomes

NU’s undergraduate programmes in Engineering and Management are designed to provide thorough grounding in the respective disciplines, offer a course of work that prepares them for either a professional career or advanced degrees.

NU expects that graduates of the  undergraduate Engineering programmes  will demonstrate the following programme outcomes as defined by NBA (National Board of Accreditation).

PO1

Engineering knowledge

Apply the knowledge of Mathematics, Science, fundamentals of Engineering and an engineering specialisation to the solution of complex engineering problems.

PO2

Problem analysis

Identify, formulate, review research literature, and analyse complex engineering problems to reach substantiated conclusions using first principles of Mathematics, Natural Sciences, and Engineering Sciences.

PO3

Design/Develop solutions

Design solutions for complex engineering problems and system components or processes that meet specified needs with appropriate consideration for public health and safety, and cognisant of cultural, societal, and environmental considerations.

PO4

Conduct investigations of complex problems

Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesise information to provide valid conclusions.

PO5

Modern tool usage

Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.

PO6

The engineer and society

Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues, and the consequent responsibilities relevant to the professional engineering practice.

PO7

Environment and sustainability

Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

PO8

Ethics

Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

PO9

Individual and teamwork

Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO10

Communication

Communicate effectively on complex engineering activities with the engineering community and with society at large. This includes being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.

PO11

Project management and finance

Demonstrate knowledge and understanding of engineering and management principles and apply these to one’s own work, as a member and/or leader in a team, to manage projects in multidisciplinary environments.

PO12

Life-long learning

Recognise the need for, and have the preparation and ability to, engage in independent and life-long learning in the broadest context of technological change.

Meet our faculty

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Prof Debashis Sengupta

Professor and Area Director

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Dr Achintya Roy

Assistant Professor

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Dr Dinesh Kumar

Assistant Professor

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Dr Jayraj Singh

Assistant Professor

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Prof Ratna Sanyal

Professor and Dean-Research

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Dr Shweta R Malwe

Assistant Professor

BTech Artificial Intelligence and Data Science – Course Outline

Students at NU’s BTech Artificial Intelligence and Data Science programme must complete a total of 177 credits spread over core, professional and elective courses, Capstone Projects, R&D Project and Industry Practice/Research Practice in AI, Data Science and related subject areas to obtain a BTech degree in Artificial Intelligence and Data Science.

Course categoryCredits
Mathematics and Basic Sciences (MBS)20
Engineering Sciences (ESC)14
Humanities and Social Sciences (HSS)18
Professional Core Course (PCC)28
Artificial Intelligence and Data Science Core Course (DS)32
Professional Electives Course (PEC)20
Open Electives Course (OEC)12
Project Work, Internship and Industry Practice (PRJ)32
Environmental Sciences (EVS)Audit Course
Total credits176

List of Professional Elective Courses in Artificial Intelligence and Data Science

01.
Social Media Analytics
02.
Cloud Computing Concepts
03.
Modeling & Simulation
04.
Multimodal data processing & analysis
05.
Numerical Methods for Data Science
06.
Dimensional and NoSQL Databases
07.
Cognitive Computing
08.
Data Stream Mining
09.
Data Integrity and Privacy
10.
Deep Learning
11.
Statistical Machine Learning
12.

Data Mining

13.

Computer Vision

14.

Business Analytics

15.

Predictive Modelling for Data Science

16.

Big Data Concepts

17.

Artificial Neural Network

18.

Machine Learning

19.

Information Retrieval

20.

Web Intelligence and Algorithms

BTech AI and Data Science – Course Architecture – 6 month IP (Industry Practice)

Year I (Semester I & Semester II)

A systematic exposure to scientific, mathematical and engineering principles will be given to the students during the first two semesters. In each semester, students will take one course each in Physics, Chemistry, Mathematics, Electronics, Foundation of Computer Programming, Workshop Practice, Engineering Graphics, Data Structures, along with Technical English.
# Course code Course L T P C

1

MAT 112 Calculus 3 1 0 4

2

Science – I 3 0 2 4

3

EL 111 Fundamentals of Electronics 3 0 2 4

4

TA 111 Fundamentals of Computer Programming 2 0 4 4

5

TA 202/ TA 212 Engineering Graphics / Workshop Practice 2/1 0 2/4 3

6

TA 102 Communication Skills 2 0 2 3

7

NU 111 Community Connect 0 0 2 1
Total Semester L-T-P-C 13 1 12 23
# Course code Course L T P C
1 MAT 101 Algebra and Differential Equations 3 1 0 4
2 Science – II 3 0 2 4
3 CS 102 Data Structures 3 0 2 4
4 EL 101 Digital Logic and Circuit 3 0 2 4
5 TA 212/ TA 202 Workshop Practice/Engineering Graphics 2/1 0 4/2 3
6 HSSM-I 3 0 0 3
7 NU 111 Community Connect 0 0 2 1
8 Total Semester L-T-P-C 15 1 10 24

Year II (Semester III & Semester IV)

At the beginning of the third semester, each student will enter his/her chosen area (AI and Data Science). Students are required to complete 46 credits in Year II (Semester III & Semester IV).

# Course code Course L T P C
1 MAT 221 Probability & Random Process 3 1 0 4
2 CS 122 Computer Architecture and Organization 3 0 2 4
3 CS 201 Design and Analysis of Algorithms 3 0 2 4
4 CS 232 Discrete Mathematics 3 1 0 4
5 CS 251 Object Oriented Programming 2 0 4 4
6 CS 322 Artificial Intelligence 3 0 2 4
7 NU 211 Community Connect 0 0 2 1
Total semester L-T-P-C 17 2 12 25
# Course code Course L T P C
1 CS 231 Database Management Systems 3 0 2 4
2 EL 302 Digital Image Processing 3 0 2 4
3 CS 211 Operating Systems 3 0 2 4
4 DS 412 Inferential Statistics for Data Science 3 0 2 4
5 CS 212 Computer Networks and Data Communication 3 0 2 4
6 CS 4131 Machine Learning 3 0 2 4
7 NU 212 Community Connect 0 0 2 1
Total semester L-T-P-C 18 0 14 25

Year III (Semester V & Semester VI)

In their third year of study, each student will have a choice of selecting one open elective course in Semester V and two ‘Data Science’ related professional elective courses in Semester VI, along with one Capstone Project-I and one R & D Project. Students are required to complete 46 credits in their third year (Semester V & Semester VI).
# Course code Course L T P C
1 DS 401 Numerical Methods for Data Science 3 1 0 4
2 CS 3132 Cloud Computing Concepts (from CSE PE – Sem-VI) 3 0 2 4
3 CS 4261 Natural Language Processing & Text Analytics 3 0 2 4
4 CS 4101 Introduction to Linear and Non-linear Optimization (OE) 3 1 0 4
5 DS 432 Predictive Modeling for Data Science 3 0 2 4
6 HSSM-II 3 0 0 3
7 NU 311 Community Connect 0 0 2 1
Total semester L-T-P-C 18 2 8 24
# Course code Course L T P C
1 CS 3102 Dimensional and NoSQL Databases 2 0 4 4
2 Professional Elective – I 3 0 2 4
3 Professional Elective – II 3 0 2 4
4 CS 392 Capstone Project – I 2 0 4 4
5 NU 302 R & D Project 1 0 6 4
6 HSSM-III 3 0 0 3
7 NU 312 Community Connect 0 0 2 1
Total semester L-T-P-C 14 0 20 24

Year IV (Semester VII &Semester VIII)

In Semester VII, students of the BTech Data Science programme have a choice of selecting three professional elective courses and two open elective courses, along with Capstone project II. Students are required to complete 44 credits in their Year IV (Semester VII and Semester VIII). In the final semester, the students are required to complete Industry Practice.

BTech Artificial Intelligence and Data Science – Course Architecture – 1 Year IP/RP (Industry Practice/Research Practice)

# Course code Course L T P C
1 Open Elective – I 3 0 2 4
2 Open Elective – II 3 0 2 4
3 Industry Practice-I/Research Practice-I 0 0 24 12
Total semester L-T-P-C 6 0 28 20
# Course Code Course Title L T P C
1 NU 402 Industry Practice-II/Research Practice-II 0 0 40 20
Total semester L-T-P-C 0 0 40 20

Programme outcomes

NU’s undergraduate programmes in Engineering and Management are designed to provide thorough grounding in the respective disciplines, offer a course of work that prepares them for either a professional career or advanced degrees.
NU expects that graduates of the undergraduate Engineering programmes will demonstrate the following programme outcomes as defined by NBA (National Board of Accreditation).

PO1

Engineering knowledge

Apply the knowledge of Mathematics, Science, fundamentals of Engineering and an engineering specialisation to the solution of complex engineering problems.

PO2

Problem analysis

Identify, formulate, review research literature, and analyse complex engineering problems to reach substantiated conclusions using first principles of Mathematics, Natural Sciences, and Engineering Sciences.

PO3

Design/Develop solutions

Design solutions for complex engineering problems and system components or processes that meet specified needs with appropriate consideration for public health and safety, and cognisant of cultural, societal, and environmental considerations.

PO4

Conduct investigations of complex problems

Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesise information to provide valid conclusions.

PO5

Modern tool usage

Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.

PO6

The engineer and society

Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues, and the consequent responsibilities relevant to the professional engineering practice.

PO7

Environment and sustainability

Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

PO8

Ethics

Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

PO9

Individual and teamwork

Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO10

Communication

Communicate effectively on complex engineering activities with the engineering community and with society at large. This includes being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.

PO11

Project management and finance

Demonstrate knowledge and understanding of engineering and management principles and apply these to one’s own work, as a member and/or leader in a team, to manage projects in multidisciplinary environments.

PO12

Life-long learning

Recognise the need for, and have the preparation and ability to, engage in independent and life-long learning in the broadest context of technological change.

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Programme Specific Outcomes for AI and Data Science

PSO1

Understand, analyse and develop essential proficiency in the areas related to AI and Data Science and underlying statistical and computational principles, Optimisation techniques and apply the knowledge to solve practical problems
PSO2
Ability to implement AI and Data Science techniques along with Artificial Intelligence inferential statistics, predictive modeling, neural networks, natural language processing, machine learning, data visualisation and big data analytics for solving a problem and designing novel algorithms for successful career and entrepreneurship
PSO3
Use modern tools, technologies, and programming languages in the area of AI and Data Science
PSO4
Apply the concepts and practical knowledge in analysis, design and development of data driven decision making systems and applications to solve multi-disciplinary problems
PSO5
Ability to develop solutions for prediction and forecasting to industry and societal needs in a rapid changing technological environment and communicate with clients as an entrepreneur
PSO6
To provide a concrete foundation and enrich their abilities to qualify for employment, higher studies and research in AI and Data Science with ethical values
PSO7
Pursue higher studies and continue to learn by participating in conferences, seminars and by doing individual and group research in AI and Data Science and related areas
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