Syllabus for
Master of Technology (Data Science)
Academic Year  (2023)

M. Tech in Data Science is a two year, four semester post-graduate programme with an objective to impart the knowledge on methodologies, techniques and concepts related to data science which includes mathematics, statistics, data warehousing, data mining, machine learning and visualization techniques. The main objective of this program is to provide one of the best post graduate educations to students so that they can meet the growing regional, national and international need for highly qualified personnel in the fields of data science, natural language processing and artificial intelligence. The curriculum is framed by the experienced academic and industrial expertise, by considering current as well as future demands of enterprises.  By looking at the multidisciplinary nature of data science, the curriculum offers many interdisciplinary subjects and also encourages students to do their Dissertation in a multidisciplinary environment.  The programme enables the students to apply the knowledge of data science and computer science in the field of natural language processing, Big data as well as many emerging technologies for solving the real world problems encountered during day-to-day life. Students will get a good exposure to interpret, manage as well as evaluate the large amount of heterogeneous data in the real time environment. In addition to this the department offers  a dedicated research centre as well as specialized labs for this program. During the Dissertation phase, students are encouraged to do their research in this specialized lab under the supervision of a dedicated supervisor or in the industries to make them industry or research ready. The programme consists of the modules to be learnt as compulsory electives along with core subjects of data science as well as computer science. Few of them include:

•    Advanced Database Management systems
•    Advance artificial intelligence
•    Advance Data Mining
•    Statistical foundation for data science
•    Big Data analytics
•    Machine Learning.
•    Data Visualisation Techniques
•    Massive graph analysis
•    Scientific Computing
•    Matrix Computations
•    Predictive analytics
•    Image and Video Analysis
•    Bioinformatics

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Analyze complex engineering problems critically, apply independent judgment for synthesizing information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context.

PO3: Think laterally and originally, conceptualize and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise.

PO4: Apply basic and advanced Data Science knowledge that prepares for efficiency, leadership roles in a variety of career paths and integrates ethics.

PO5: Develop domain knowledge in mathematical, statistical, Data Science and AI techniques to create modelling, analysis and processing of large multidimensional data sets.

Assessment is based on the performance of the student throughout the semester.

Assessment of each paper

• Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

• End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Course description:

The course is designed to equip the necessary awareness and command on the use of English language in writing a research paper starting from how to compile an appropriate title, language to use at different stages of a paper to make it effective and meaningful. 

Course objectives:

• Understand that how to improve your writing skills and level of readability
• Learn about what to write in each section.
• Understand the skills needed when writing a Title and ensure the good quality of paper at very first-time submission

Course Description

Disaster Management (DM) is an emerging discipline which addresses all facets, namely, Mitigation, Preparedness, Response and Recovery. Global and national policies urge to consider its application in all branches of engineering, science, management and social sciences. The course would help the students to appreciate the importance of disaster science and its applications in reducing risks so as to contribute to national development. It would help the students to enhance critical thinking and to understand interdisciplinary approaches in solving complex problems of societies to reduce the risk of disasters.

Course Objectives

1.    To  demonstrate a critical understanding of key concepts in disaster risk reduction and humanitarian response 

2.    To critically evaluate disaster risk reduction and humanitarian response policy and practice from multiple perspectives. 

3.     To develop an understanding of standards of humanitarian response and practical relevance in specific types of disasters and conflict situations.

4.     To critically understand the strengths and weaknesses of disaster management approaches, planning and programming in different countries, particularly their home country or where they would be working 

Course intends to highlight the value of education and self- development which would enable students to imbibe good values and understand the importance of character

Students will be able to:

 1. Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective.

 2. To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of Indian nationalism.

 3. To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution.

SUBJECT OBJECTIVE:  

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn. 

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines. 

The course will broadly cover the following aspects:     Teaching skills     Laboratory skills and other professional activities     Research methodology For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal. 

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline. 

Data-driven decision making is becoming more common in organizations and businesses. In fact, database systems are at the center of the information systems strategies of most organizations. Users at any level of an organization can expect to work with and use database systems often. So, the ability to use these systems, which includes knowing what they can do and what they can't do, figuring out whether to access data directly or through technical experts, and knowing how to find and use the information well, became essential in every industry. Also, being able to design new systems and applications for them is a clear advantage and a necessity in the modern world. One type of database system that is widely used and the main focus of this course is the Relational Database Management System (RDBMS).

Course will give students a chance to use what they learn in the lectures, homework, SQL assignments, and a database implementation project.

Understand the basic concept of data structures for storing and retrieving ordered or unordered data. Data structures include arrays, linked lists, binary trees, heaps, and hash tables.

1.  Analyze the asymptotic performance of algorithms.

2.  Demonstrate their familiarity with major data structures, rule to manipulate those, and their canonical applications

3.  Construct efficient algorithms for some common computer engineering design problems

Course Description

This course is an introduction to the field of statistics and how engineers use statistical methodology as part of the engineering problem-solving process. Mathematical and Statistical Skills for Data Science Course aligns with LRNG (√) / Skill Develop (√) / Entrup / Emplyobilty (√) / Cross-Cutting Needs.

Course Objectives

·         To understand the fundamentals of Engineering and Statistical thinking methods.

·         To learn the Continuous Uniform and Probability distributions.

·         To study the various Normal distribution and Random variable concepts.

·         To understand the random sampling and hypothesis tests. 

This course is a source of information that can be used to teach business intelligence in one semester. It will be a good place to start for people who are learning for the first time, especially those in engineering and management. You can't just study one part of Business Intelligence. The subject gives a complete look at BI, starting with an enterprise context and going on to explain how to use tools to learn more. It also talks about a few areas where BI is used and the problems it can help solve. It covers the whole life cycle of a BI/Analytics project, including operational/transactional data sources, data transformation, data mart/warehouse design-build, analytical reporting, and dashboards.

The course will allow students to use what they learn in the lectures, homework, Data Structure and Algorithms assignments, and a Data Structure and Algorithms project.

1. To implement the basic concepts of linear and non-linear data structures.

2. To provide the students with various kinds of searching and sorting Mechanism.

3. To work with different tree traversal techniques.

The aim of the course is to introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research.

Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers. Identify critical evidence gaps to guide the development.

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:

• Review and increasetheir understandingof thespecific topics tested.
• Improvetheir abilityto communicate that understandingto thegrader.
• Increasetheeffectiveness with which theyusethelimited examinationtime.

 students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

• Teachingskills
• Laboratoryskills andother professional activities
• Research methodology

Data mining is one of the most advanced fields of Computer Science and Engineering. This field makes use of the applications of Mathematics, Statistics and Information Technology in discovering and prediction of new information and knowledge from largely available data. It is a new evolving interdisciplinary area of research and development which has created interest among scientists of various disciplines like Computer Science, Mathematics, Statistics, and Information Technology and so on. This course titled, “Advanced Data Mining,” involves learning a collection of techniques for extracting and discovering new patterns and trends in large amounts of data. This course will also provide a hands-on introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management.

Course Description: 

Introduction to optimization techniques use both linear and non-linear programming. The focus of the course is on convex optimization though some techniques will be covered for non-convex function optimization too. After an adequate introduction to linear algebra and probability theory, students will learn to frame engineering minima maxima problems in the framework of optimization problems.

Course Objective: 

Be able to model engineering minima/maxima problems as optimization problems. 

Be able to implement optimization algorithms.

This course will teach you about the characteristics of Big Data and how to use it in Big Data Analytics. You will learn about the features, benefits, limitations, and applications of various Big Data processing tools. You'll learn how Hadoop, Hive, Apache Spark can help you reap the benefits of Big Data while overcoming some of its challenges. At the end of completing this course students will get job opportunities in the field of data engineering. 

• To optimize business decisions and create competitive advantage with Big Data analytics.

• To explore the fundamental concepts of big data analytics. 

• To learn to analyze the big data using intelligent techniques. 

• To understand and analyze the applications using Map Reduce Concepts. 

• To introduce programming tools PIG & HIVE in Hadoop echo system.

Course Description:

The course will help the students understand the fundamental digital image processing concepts. The students will also gain knowledge of image compression techniques followed by image segmentation. The course will also help the students to use Deep Learning techniques for feature extraction and image pattern classification.

Course Objective:

1. The students will learn the fundamental concepts of Image Processing.

2. The students will learn image compression and segmentation techniques.

3. The students will study the feature extraction and pattern classification techniques.

It is to introduce students to blockchain technology along with its different properties and applications. 

Course will give students a chance to use what they learn in the lectures, homework, Data Mining algorithms using python assignments, and a implementation project on Data visualization using data mining algorithms.

Course Description: 

Optimization techniques use both linear and non-linear programming. The focus of the course is on convex optimization though some techniques will be covered for non-convex function optimization too. After an adequate introduction to linear algebra and probability theory, students will learn to frame engineering minima maxima problems in the framework of optimization problems.

Course Objective:

1. Be able to model engineering minima/maxima problems as optimization problems. 
2. Be able to implement optimization algorithms.

Internships are short-term work experiences that will allow  a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices.   More specifically, doing internships is beneficial because they provide the opportunity to:

▪        Get an inside view of an industry and organization/company

▪        Gain valuable skills and knowledge

▪        Make professional connections and enhance student's network

▪        Get experience in a field to allow the student  to make a career transition

This course is aimed to cover the topics of how image and video analysis is done. The topics include image acquisition, color images, point processing, neighborhood processing, morphology, BLOB analysis, Segmentation in Video data, Tracking, Geometric transformation and visual effects.

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

This course aims at making the students get an understanding of the compression techniques available for multimedia applications and also get an understanding of the encryption that can be implemented along with the compression.

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.