Available courses

Its a Graduate level  course gives an Introduction to R and Python.  

The course provides foundations in two of the most popular programming languages used for data analytics – viz. R and Python. After having completed the course, the students should be knowledgeable in the principles of programming in R for the purpose of data management, visualisation of data and basic statistical calculations. Augmented to this, the course enables the student to acquire skills in Python programming for processing text based data and managing the results.

Modules 1 and 2 give a general introduction to Python programming, and students will learn and practice to write small Python programs to tackle problems in data analytics.

In the last 3 modules, students learn and practice R programming needed for data analysis, in particular for large dataset. The student will learn how to take a large dataset, break it up into manageable pieces and use a range of qualitative and quantitative methods to bring out the insight embedded into the data. They also learn tools that help to communicate the findings using R visualization packages.

Applications of the Analytics are span across various fields, some of them are listed below

  1. Marketing
  2. Retail
  3. Finance
  4. Sales
  5. Operations
  6. Manufacturing
  7. Consumer Goods
  8. Crime Analysis
  9. Travel and Hospitality
  10. Education
  11. Healthcare
  12. Telecom
  13. Agriculture
  14. HR
  15. Energy  ......................

Course Outcomes

1. Explain the research process and research problem

2. Understand the design of the research

3. Discuss the methods and techniques of data collection

4. Explain the fieldwork in research and data processing

5. Understand different forms of IPR

Course Outcomes

1. Explain the relevance of standards and specifications

2. Distinguish the processability tests used for thermoplastics, thermosets and elastomers

3. Discuss the thermal, electrical & optical properties of plastics and rubbers

4. Summarise the various test methods for evaluating the mechanical properties of the polymers

5. Outline various techniques used for characterizing polymers

6. Distinguish polymer, blends & composites using the test results of characterization

7. Explain the test procedures for latex and dry rubber products

8. Summarise the specification test methods of various plastics products

Course Outcomes

1. Describe the characteristics of NR latex

2. Explain the significance and methods of latex specification tests

3. Explain the basic principles of latex stability and destabilization

4. Get an insight into the various ingredients used for latex compounding

5. Summarise the different types of dipping techniques

6. Identify the role of latex in miscellaneous applications

7. Design suitable formulations for different latex-based products

8. Illustrate and compare various latex product manufacturing methods

Course Objectives

1. Build up a good understanding of basics and theories of adhesion

2. Summarise types of adhesives and recommend suitable formulation for various applications

3. Understand pigment properties and prepare paint dispersions 

4. Design appropriate paint formulations

5. Understand the mechanism of film formation and evaluate the paint properties

MTech second semester Seminar

Course Outcomes: -

At the end of this course, students will demonstrate the ability to:

CO1: Understand the fundamental theory and concepts of neural networks

CO2: Illustrate the soft computing techniques like neural network and fuzzy logic and their roles in building intelligent systems.

CO2: Illustrate and implement the various learning rules

CO3: Comprehend the fuzzy logic and the concept of fuzziness involved in various systems and fuzzy set theory.

CO4: Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning, fuzzy inference systems, and fuzzy logic

CO5: Design and Implement real life examples using fuzzy logic and genetic algorithms

Course Outcomes: -

At the end of this course, students will be able to

CO1: Recognize the elementary applications and techniques for data fusion in military and civilian systems.

CO2: Summarize the different data fusion models.

CO3: Explain the taxonomy of algorithms for multi sensor data fusion systems

CO4: Use Kalman filter in Data Fusion.

CO5: Illustrate the data information filters.

CO6: Illustrate high performance data structures.

CO7: Design and implement data fusion systems.

Course Outcomes:

On completion of the course, students will be able to:

CO1: Explain the concept of signals and systems, their classifications and analysis using differential/ difference equations (Understand)

CO2: Explain the concept of impulse response and perform convolution (Understand)

CO3: Analyse LTI systems using Laplace transforms /Z transform (Apply).

CO4: Outline and evaluate the frequency response of LTI systems (Understanding)

CO5: Analyse systems in complex frequency domain (Apply).

CO6: Explain the need for sampling and reconstruction and Sampling theorem (Understand).

21-473-0101 INTELLIGENT TECHNIQUES IN INSTRUMENTATION

Course Outcomes

On completion of this course the student will be able to:

CO1:  Describe and Illustrate the fundamental theory and concepts of neural networks, Identify different             neural network architectures, algorithms, applications and their limitations.

CO2:  Select appropriate learning rules for each of the architectures and recognise several neural network             paradigms and its applications

CO3:  Examine the fuzzy logic and the concept of fuzziness involved in various systems and fuzzy set                 theory.

CO4:  Illustrate the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate                    reasoning, fuzzy inference systems, and fuzzy logic.

CO5:  Outline the concept of genetic algorithm and its applications.

CO6:  Illustrate and Compare the various Swarm Intelligent Techniques its applications.

CO7:  Construct different applications of these models to solve engineering and other problems.

  1. Course Description : This course educates students on designing embedded systems to interface with peripherals for performing various functionalities.