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

          -       Useful phrases to ensure the quality of the paper

Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011

Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook.

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 

Disaster: Definition, Factors And Significance; Difference Between Hazard And Disaster; Disaster and Hazard characteristics (Physical dimensions)

Repercussions of Disasters and Hazards: Economic Damage, Loss Of Human And Animal Life, Destruction Of Ecosystem. Disaster and Hazard typologies and their applications in Engineering. 

Study Of Seismic Zones; Areas Prone To Floods And Droughts, Landslides And Avalanches; Areas Prone To Cyclonic And Coastal Hazards With Special Reference To Tsunami; Post-Disaster Diseases And Epidemics

Preparedness: Monitoring Of Phenomena Triggering A Disaster Or Hazard; Evaluation Of Risk: Application Of Remote Sensing, Data From Meteorological And Other Agencies, Media Reports: Governmental And Community Preparedness. 

Concept And Elements, Disaster Risk Reduction, Global And National Disaster Risk Situation. Techniques Of Risk Assessment, Global Co-Operation In Risk Assessment And Warning, People’s Participation In Risk Assessment. Strategies for Survival.

Disaster Mitigation Meaning, Concept And Strategies Of Disaster Mitigation, Emerging Trends In Mitigation. Structural Mitigation And Non-Structural Mitigation, Programs Of Disaster Mitigation In India.

T1. Coppola, D, “Introduction to International Disaster Management “. Elsevier, 2015.

W1. http://www.training.fema.gov/emiweb/edu/ddemtextbook.asp

W2. https://www.weadapt.org/

W3. https://nagt.org/nagt/search_nagt.html?search_text=hazards&search=Go

W4. https://www.unisdr.org/

W5. https://emdat.be/

W6. http://bhuvan.nrsc.gov.in/bhuvan_links.php

W7. https://www.usgs.gov/

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

Social values and individual attitudes, Work ethics, Indian vision of humanism, Moral and non- moral valuation. Standards and principles. Value judgements

Sense of duty. Devotion, Self-reliance. Confidence, Concentration, Truthfulness, Cleanliness, Honesty, Humanity. Power of faith, National Unity, Patriotism. Love for nature , Discipline

Soul and Scientific attitude, Positive Thinking. Integrity and discipline, Punctuality, Love and Kindness, Avoid fault Thinking, Free from anger, Dignity of labour, Universal brotherhood and religious tolerance, True friendship, Happiness Vs suffering, love for truth, Aware of self-destructive habits, Association and Cooperation, Doing best for saving nature, Character and Competence –Holy books vs Blind faith, Self-management and Good health, Science of reincarnation, Equality, Nonviolence ,Humility, Role of Women, all religions and same message, Mind your Mind, Self-control, Honesty, Studying effectively

Chakroborty, S.K. “Values and Ethics for organizations Theory and practice”, Oxford University Press, New Delhi, 1999

Chakraborty S K, "Ethics in Management: Vedantic Perspectives", Oxford University Press, New Delhi, India, 1997

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.

History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)

Philosophy of the Indian Constitution: Preamble Salient Features, ∙Contours of Constitutional Rights & Duties: ∙ Fundamental Rights ∙ Right to Equality ∙ Right to Freedom ∙ Right against Exploitation ∙ Right to Freedom of Religion ∙ Cultural and Educational Rights ∙ Right to Constitutional Remedies ∙ Directive Principles of State Policy ∙ Fundamental Duties.

Organs of Governance: ∙ Parliament ∙ Composition ∙ Qualifications and Disqualifications ∙ Powers and Functions ∙ Executive ∙ President ∙ Governor ∙ Council of Ministers ∙ Judiciary, Appointment and Transfer of Judges, Qualifications ∙ Powers and Functions

Local Administration: ∙ District’s Administration head: Role and Importance, ∙ Municipalities: Introduction, Mayor and role of Elected Representative, CEO of Municipal Corporation. ∙Pachayati raj: Introduction, PRI: ZilaPachayat. ∙ Elected officials and their roles, CEO Zila Panchayat: Position and role. ∙ Block level: Organizational Hierarchy (Different departments), ∙ Village level: Role of Elected and Appointed officials, ∙ Importance of grass root democracy

Election Commission: ∙ Election Commission: Role and Functioning. ∙ Chief Election Commissioner and Election Commissioners. ∙ State Election Commission: Role and Functioning. ∙ Institute and Bodies for the welfare of SC/ST/OBC and women.

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. 

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. 

Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography. 

A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student. 

The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student. 

A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated. 

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).

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables

Introduction to Normalization using Functional and Multivalued Dependencies: Informal design guidelines for relation schema, Functional Dependencies, Normal Forms based on Primary Keys, Second and Third Normal Forms 

Simple queries in SQL, queries involving more than one relation, sub queries, full relational operations, Database modifications, defining a relational schema in SQL, view definitions.

Keys and foreign keys, constraints on attributes and tuples, modification of constraints, schema level constraints and Triggers.

Transaction Processing: Introduction to Transaction Processing, Transaction and System concepts, Desirable properties of Transactions, Characterizing schedules based on recoverability, Characterizing schedules based on Serializability, Transaction support in SQL.

Concurrency Control in Databases: Two-phase locking techniques for Concurrency control, Concurrency control based on Timestamp ordering, Multiversion Concurrency control techniques, Validation Concurrency control techniques, Granularity of Data items and Multiple Granularity Locking.

Recovery Concepts, NO-UNDO/REDO recovery based on Deferred update, Recovery techniques based on immediate update, Shadow paging, Database backup and recovery from catastrophic failures.

Introduction to OQL, Additional Forms of OQL Expressions, Object Assignment and Creation in OQL, User-Defined Types in SQL, Operations on Object-Relational Data.

Database Systems: The Complete Book by  Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom, Pearson Education India; 2nd edition (1 January 2013)

50% ESE

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

ESE 50 %

Total Marks= 50

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

Time and Space complexity of algorithms, asymptotic analysis, average and worst case analysis, asymptotic notation, the importance of efficient algorithms. 

The Queue ADT: Definition, Array representation of queue, Types of queues: Simple queue, circular queue, double ended queue (de-queue) priority queue, operations on all types of Queues. The List ADT: singly linked list implementation, insertion, deletion and searching operations on linear list, circular linked list implementation, Double linked list implementation, insertion, deletion and searching operations. Applications of Linked List

Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap

Insertion Sort, Selection sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting

Introduction to Graphs, Definitions –DFS, BFS, Minimum Spanning Tree – Prim’s and Kruskal's Algorithm. Single-Source Shortest Paths – Bellman-Ford algorithm and Dijkstra’s Algorithm – – Applications of Graphs

2. " Data Structures and Algorithm in Python", Micheal T.Goodrich, Roberto Tamassia, Michael H. Golwasser, Wiley Publication, Edition

2.  Introduction to Programming using Python , Liang Y.Daniel ,  Pearson Publication.

End Semester Examination - 50 Marks

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. 

Demonstrate the concepts  of  discrete  random  variables  and probability.

Illustrate the concepts of continuous random variables , Conduct the experiment on joint probability distribution, Discuss the fundamental concepts of statistical intervals, Describe  the  basic  concepts  of  single  sample  and  two  samples  in statistical methods. Case Study to apply the  concepts  of  discrete  random  variables  and probability and Illustrate the concepts of continuous random variables in a real life scenario aligned with the LRNG needs by using modern tools like python / R/ Matlab/SPSS.

Continuous Random Variables, Probability Distributions and Probability Density Functions, Cumulative Distribution Functions, Mean and Variance of a Continuous Random Variable, Continuous Uniform Distribution, Normal Distribution, Normal Approximation to the Binomial and Poisson Distributions, Exponential Distribution, Weibull Distribution. Case study to apply the concepts using modern tools like python / R/ Matlab/ SPSS.

Two Discrete Random Variables, Multiple Discrete Random Variables, Two Continuous Random Variables, Multiple Continuous Random Variables, Covariance and Correlation, Bivariate Normal Distribution, Linear Combinations of Random Variables. Case study to apply the concepts using modern tools like python / R/ Matlab/ SPSS.

Data Summary and Display, Random Sampling, Stem-and-Leaf Diagrams, Frequency Distributions   and   Histograms,   Box   Plots,   Time   Sequence   Plots,   Probability   Plots, Introduction to Statistical Intervals for a Single Sample, Confidence Interval on the Mean of a Normal Distribution, Variance Known, Confidence Interval on the Mean of a Normal Distribution, Variance Unknown, Confidence Interval on the Variance and Standard Deviation of a Normal Distribution, A Large-Sample Confidence Interval for a Population Proportion,  A Prediction Interval for a Future Observation. Case study to apply the concepts using modern tools like python / R/ Matlab / SPSS.

Hypothesis Testing, Tests on the Mean of a Normal Distribution, Variance Known, Tests on the Mean of a Normal Distribution, Variance Unknown, Tests on the Variance and Standard Deviation of a Normal Distribution, Tests on a Population Proportion, Inference For a Difference in Means of Two Normal Distributions, Variances Known, Inference For a Difference in Means of Two Normal Distributions, Variances Unknown, Inference on the Variances of Two Normal Distributions. Case Study to apply the  concepts  of mathematics and statistics essential for data science application prototype development  using a real life scenario aligned with the LRNG needs / skill development needs/ employability needs/ cross cutting needs

2.   NinaZumel, John Mount, “Practical Data Science with R‖”, Manning Publications, 2022(reprint)

2.      Ronald E Walpole, Raymond H Myers, Sharon L Myers, Keying E Ye, ― Probability and  Statistics for Engineers and Scientists‖, Ninth Edition, Pearson Education, 2022 (reprint)

ESE Marks 50

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.

Introduction to digital data and its types – structured, semi structured, unstructured, Introduction to OLTP and OLAP (MOLAP, ROLAP, HOLAP), BI Definitions & Concepts. BI Framework, Data Warehousing concepts and its role in BI, BI Infrastructure Components – BI Process, BI Technology, BI Roles & Responsibilities, Business Applications of BI, BI best practices.

Extraction Transformation Loading) Concepts of data integration, needs and advantages of using data integration, introduction to common data integration approaches, Meta data - types and sources. Extraction Transformation Loading) Introduction to data quality, Data profiling concepts and applications, introduction to ETL using Power BI data Integration.

Data Modeling Introduction to data and dimension modeling, multidimensional data model, ER Modeling vs. multi dimensional modeling, concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake schema.

Visualization, Motivations for visualization, general concept, Techniques-Stem and Leaf Plots, Histogram, Two-Dimensional Histograms, Box Plots, Pie Chart, Percentile Plots and Empirical Cumulative Distribution Functions, Scatter Plots, Extending Two- and Three-Dimensional Plots, Visualizing Spatio-temporal Data, Contour Plots, Surface Plots, Vector Field Plots, Lower-Dimensional Slices, Animation, Visualizing Higher-Dimensional Data, ACCENT Principles.

Introduction to business metrics and KPIs, creating cubes using Microsoft Excel. A typical enterprise, Malcolm Baldrige - quality performance framework, balanced scorecard, enterprise dashboard, balanced scorecard vs. enterprise dashboard.

2.  Introduction to data mining by  Pang-Ning Tan , Michael Steinbach , Vipin Kumar, Pearson Education; First Edition  2016. 

·         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)

Components of the CIA

 CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 CIA II  :   Mid Semester Examination (Theory)                                      : 25 marks

 CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 Attendance                                                                                               : 05 marks

 Total                                                                                                         : 50 marks

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.

1. Write Python programs for implementing the following searching techniques. a. Linear search b. Binary search c. Fibonacci search

2. Write Python programs for implementing the following sorting techniques to arrange a list of integers in ascending order. a. Bubble sort b. Insertion sort c. Selection sort d. Quick sort e. Merge sort

3. Write Python programs to a. Design and implement Stack and its operations using List. b. Design and implement Queue and its operations using List.

4. Write Python programs for the following: a. Uses Stack operations to convert infix expression into postfix expression. b. Uses Stack operations for evaluating the postfix expression.

5. Write Python programs for the following operations on Single Linked List. (i) Creation (ii) insertion (iii) deletion (iv) traversal b. To store a polynomial expression in memory using single linked list.

6. Write Python programs for the following operations on Circular Linked List. (i) Creation (ii) insertion (iii) deletion (iv) traversal

7. Write Python programs for the following: Uses functions to perform the following operations on Double Linked List. (i) Creation (ii) insertion (iii) deletion (iv) traversal in both ways.

8. Write a Python program to implement Stack using linked list.

9. Write a Python program to implement Linear Queue using linked list.

10. Write Python programs to implement graph traversal algorithms: a. Depth-first search. b. Breadth-first search.

11. Write a Python program to perform the following: a. Create a binary search tree. b. Traverse the above binary search tree recursively in pre-order, post-order and in-order. c. Count the number of nodes in the binary search tree.

Records                                                                                 : 05 marks

Mid semester examination                                                    : 10 marks

 Class work                                                                            : 10 marks

Total                                                                                      : 50 marks

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.

Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan. 

Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis. 

Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external. Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools. 

Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems.

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check.

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables.

T2. Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.

T3. Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992.

R2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013.

R3. James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.

End Semester Examination - 50%

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.

Aims and rationale, Policy background, Conceptual framework and terminology ∙ Theories of learning, Curriculum, Teacher education. ∙ Conceptual framework, Research questions. ∙ Overview of methodology and Searching.

 Pedagogical practices are being used by teachers in formal and informal classrooms in developing countries.  Curriculum, Teacher education.

 Methodology for the in-depth stage: quality assessment of included studies. ∙ How can teacher education (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy? ∙ Theory of change. ∙ Strength and nature of the body of evidence for effective pedagogical practices. ∙ Pedagogic theory and pedagogical approaches. ∙ Teachers’ attitudes and beliefsand Pedagogic strategies.

Alignment with classroom practices and follow-up support ∙ Peer support ∙Support from the head teacher and the community. ∙ Curriculum and assessment ∙ Barriers to learning: limited resources and large class sizes.

Research design ∙ Contexts ∙ Pedagogy ∙ Teacher education ∙ Curriculum and assessment ∙ Dissemination and research impact.

ii.              Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher education research project (MUSTER) country report 1. London: DFID.

iii.            Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal Educational Development, 33 (3): 272–282.

iv.             Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education. Oxford and Boston: Blackwell.

v.               Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign.

www.pratham.org/images/resource%20working%20paper%202.pdf.

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

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.

MAKEUPPOLICY:  All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.

NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.

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.

A multidimensional Data Model, Data preprocessing, Data cleaning, Data integration and Transformation, Correlation analysis and Data Reduction

Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Data Matrix versus Dissimilarity Matrix, Proximity Measures for Nominal Attributes, Binary Attributes, Numeric Data, Ordinal Attributes,Dissimilarity for Attributes of Mixed Types.

Mining Frequent Patterns-basic concepts-apriori principle, Pattern Mining in Multilevel, Multidimensional Space, Constraint-Based Frequent Pattern Mining, Mining High-Dimensional Data and Colossal Patterns.

Bayesian Belief Networks, Classification by Backpropagation, Support Vector Machines, kNearest-Neighbour Classifiers, Genetic Algorithms, Rough Set Approach, Fuzzy Set, Model Evaluation and Selection, Approaches, Techniques to Improve Classification Accuracy.

k-Means: A Centroid-Based Technique, k-Medoids, Hierarchical Methods, Probabilistic Model-Based Clustering, Clustering High-Dimensional Data, Clustering Graph and Network Data, Evaluation of Clustering.

Proximity-Based Methods, and Clustering-Based Methods, Outlier Detection in HighDimensional Data.

Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis.

·         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)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                       : 10 marks

CIA III : Quizzes/Seminar/Case Studies/Project Work           : 10 marks

Attendance                                                                        : 05 marks

Total                                                                                 : 50 marks

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.

Linear algebra and matrices Vector space, eigen analysis, Elements of probability theory, Elementary multivariable calculus.

Simplex method, Introduction to linear programming model, Duality, Karmarkar's method.

Conjugate direction and quasi-Newton methods, Gradient-based methods, One-dimensional search methods

Lagrange theorem, First order necessary condition, Second order necessary condition, Second order sufficient condition

Projection methods, Karush-Kuhn-Tucker conditions, Non-linear constrained optimization models

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.

What is big data – why big data –Data,  Evolution of Big data; Characteristics of Big Data; Types of data; Sources of data; Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, Types of analytics; Domain Specific Examples of Big Data; Analytics Flow for Big Data;

Experiment 1:  Illustrate a python program to read and analyze the big data using EDA. 

Getting Started with NoSQL and MongoDB: Introducing NoSQL and MongoDB, Installing and Configuring MongoDB;  Implementing NoSQL in MongoDB: Configuring User Accounts and Access ControlManaging Databases and Collections from the MongoDB Shell;  Finding Documents in the MongoDB Collection from the MongoDB Shell; Additional Data-Finding Operations Using the MongoDB Shell; Manipulating MongoDB Documents in a Collection; Utilizing the Power of Grouping, Aggregation, and Map Reduce; Implementing MongoDB in Python Applications;

Experiment 2:  

1. Construct databases, collections and perform CRUD operations in MongoDB shell.

1. Apply MongoDB in Python Applications;

History of Hadoop; HDFC concepts; Design of HDFS; Hadoop Distributed File System and its Features; Components of Hadoop; HDFS Commands;  Analyzing the Data with Hadoop;  Scaling Out ; Hadoop Streaming; Reading and writing data in Hadoop; Directories; Querying the File system; Deleting data; Data flow: Anatomy of a File Read and File Write;

Experiment 3: 

1. Experiment with various Hadoop commands in Hadoop environment.

1. Develop python/java programs Reading and writing data in Hadoop.

Anatomy of a YARN Application Run – Resource Requests, Application Lifespan, Building YARN Applications; Scheduling in YARN; YARN Distributed-ShellStructure of YARN Applications; 

Experiment 4: Build YARN Applications.

The Configuration API; Setting Up the Development Environment; Writing a Unit Test with MRunit – mapper and reducer; Running Locally on Test Data; Running on a cluster; MapReduce workflows ; Anatomy of MapReduce job run; Failures; shuffle and sort; MapReduce types – input formats – output formats;

Experiment 5: Analyze various map-reduce java/python applications in Hadoop platform.

Hbase – data model and implementations –,Hbase clients – Hbase examples –praxis. 

Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study.

Apache Spark – Limitations of Hadoop; Overcoming the limitations of Hadoop; Theoretical Concepts in Spark;

Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study

Core Components in Spark; The architecture of spark;Experiment 6: Develop hbase and hive programs to manage big data.Apache Spark – Limitations of Hadoop; Overcoming the limitations of Hadoop; Theoretical Concepts in Spark;Core Components in Spark; The architecture of spark;Experiment 6: Develop hbase and hive programs to manage big data.

Spark MLib; H2O ;  Clustering algorithms ;Big Data Classification algorithms ; Big Data Regression algorithms;

Experiment 7: Predict big data insights by applying ML algorithms using Apache Spark

1. Arshdeep Bahga, Vijay Madisetti, “Big Data Science & Analytics: A Hands-on Approach”, hands-on-books-series.com. India, 2020

2. .Brad Dayley  Sams Teach Yourself NoSQL with MongoDB in 24 Hours.

References (Text / Online Ref).

3.Andreas Meier, Michael Kaufmann, “SQL & NoSQL Databases: Models, Languages, Consistency Options and Architectures for Big Data Management”, Springer,2019.

4.Douglas Eadline, “Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem”, 1st Edition, Pearson Education, 2016.

5.Tom White, “Hadoop: The Definitive Guide”, 4th Edition, O’Reilly Media, 2015.

1. Raj Kamal, Preeti Saxena, “BIG DATA ANALYTICS: Introduction to Hadoop, Spark, and Machine-Learning”, McGraw Hill Education, 2019.

2. Andreas Meier, Michael Kaufmann, “SQL & NoSQL Databases: Models, Languages, Consistency Options and Architectures for Big Data Management”, Springer,2019.
   

3. Boris Lublinsky, Kevin T. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, John Wiley & Sons, 2013.

ESE=50

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.

Introduction, Digital Image Fundamentals, Intensity Transformation and Spatial Filtering- Intensity Transformation Functions, Histogram Processing, Spatial Filtering, Types and Enhancement methods.

Image Compression- Fundamentals, Huffmann Coding, Golomb Coding, Arithmetic Coding, LZW Coding, Run-length Coding, Symbol-based Coding, Bit-Plane Coding, Block Transformation Coding, Predictive Coding,, Wavelet Coding.

Fundamentals, Point, line and Edge Detection, Thresholding, Segmentation by Region Growing and Region Splitting and Merging, Region Segmentation Using Clustering and Superpixels, Region Segmentation Using Graph Cuts, Segmentation Using Morphological Watersheds, Use of Motion in Segmentation.

Fundamentals, Boundary Preprocessing, Boundary Feature Descriptors, Region Feature Descriptors, , Principal Components as Feature Descriptors, Whole-Image Features, Scale-Invariant Feature Transforms

Fundamentals, Patterns and Pattern Classes, Pattern Classification by Prototype Matching, Optimum Statistical Classifiers, Neural Networks and Deep Learning, Deep Convolution Networks, Additional details of Information.

2.Gonzalez.R.C& Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd 2011 (Asia).

2.John C.Russ, “The Image Processing Handbook”, CRC Press,2007.

3.Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.

4.Ardeshir Goshtasby, “ 2D and 3D Image registration for Medical, Remote Sensing and Industrial Applications”, John Wiley and Sons,2005.

5.Rick S.Blum,Zheng Liu,“ Multisensor image fusion and its Applications“,Taylor & Francis,2006.

ESE - 50%

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

Blockchain- A history of Blockchain-how the computation environment evolved, What a blockchain is, Problems with centralized systems, Centralized vs decentralized vs distributed, Blockchain as Public Ledgers, Bitcoin and Blockchain, Technology behind bitcoin—The Blockchain, Blockchain 2.0 and Smart Contracts, Block in a Blockchain- securing data, Structure of a Block, Block Header, The blockchain Replicas, Distributed Consensus, Permission less consensus and Permissioned Model of Blockchain 2.0, Cryptographically secured Hash Function, Cryptographically secured chain of blocks, Properties of a hash function-Hash pointer, Merkle tree and its use.

A basic crypto primitives: Digital signature, reducing signature size, introduction to cryptocurrency using digital signature and hashchain, What is bitcoin, Creation of bitcoins, Payments and double spending, FORTH– How FORTH works, Bitcoin Scripts , Bitcoin P2P Network, Transaction in Bitcoin Network , Block Mining inbitcoin network, Block Flooding, Block propagation and block relay.

Introduction to Consensus, Distributed consensus, Consensus in a Bitcoin network, Proof of Work (PoW)-Cryptographic Hash as PoW, Hashcash PoW, Bitcoin PoW, Tempering of PoW- Sybil attacks, DoS attacks, PoW power consumption, monopoly problem- Proof of Stake, Proof of Burn, Proof of Elapsed Time. Basics of PoET, Mining bitcoin, Difficulty in mining, Hash rate vs difficulty, Mining Pool, Permissioned model of blockchain and use cases, Design issues for Permissioned Blockchains, State machine replication, smart contract state machine – crowd funding, Distributed state machine replication

Consensus algorithm- RAFT Consensus, PAXOS consensus, Byzantine general model, Byzantine general problem, Lamport-Shostak-Pease, Practical Byzantine Fault Tolerance. Introduction to hyper ledger fabric v1.1, Architecture of Hyperledger fabric v1.1, Ethereum: Ethereum network, EVM, Transaction fee, Mist Browser, Ether, Gas, Solidity, Smart contracts, Truffle-Design and issue Cryptocurrency, Mining, DApps, DAO.

Understanding business problems, understanding the participants, Building communities in block chain networks, Block chain in Financial services, Supply chain management, revolutionizing global trade.

ESE 50 marks

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.

1.Han J. & Kamber, M, “Data Mining: Concepts and Techniques”, Third Edition, Morgan Kaufmann, 2012

2. Mohammed J.Zaki, Wagneermeira, “Data Mining and Analysis: Fundamental concepts and algorithms”, First Edition, Cambridge University Press India, 2015.

3. Ian H. Witten, &Eibe Frank, “Data Mining –Practical Machine Learning Tools and Techniques”, 3rd Edition, Elesvier, 2011.

            Records                                                                 : 10 marks

            Mid semester examination                                      : 25 marks

            Class work                                                            : 15 marks

            Total                                                                    : 100 marks

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

1.The student shall undergo an Internship for 30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  7th semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

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.

Different flavors of video and image processing, General Framework, Energy, Optical System, Image Sensor, Digital Image.

Grey Level & Non Linear Grey level mapping, Image Histogram & Thresholding, Logical operations and Image arithmetic, Median Filters & Correlation.

Hit, Fit, Dilation ,erosion, Compound operations, BLOB extractions, Features & Classifications.

Video Acquisition, Detecting changes in videos, Background substration and Image diffenencing, Tracking by detection & Prediction, Tracking multiple objects, Affine transformations, Backward mapping &Interpolation, Homography.

Visual effects based on pixel manipulation, Visual effects based on geometric transformations, Application examples-Edutainment Game, Coins Sorting using robot.

2. Introduction to Video & Image Processing: Building real systemns& Applications, by Thomas B Moeslund, Published by Springer,2012

2. Introduction to Video & Image Processing: Building real systemns& Applications, by Thomas B Moeslund, Published by Springer,2012

ESE- 50

CIA-1 will be quiz, CIA-3 will be assignment, CIA-2 will be Mid Sem.

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.

Project Work 

▪          Continuous Internal Assessment:100 Marks

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

Presentation assessed by Panel Members

♦        Guide

♦        End semester Project Report

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.

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

2. David Salomon : Data Compression – The Complete Reference, Springer Verlag New York Inc., 4th Edition, 2006

3. Yun Q.Shi, HuifangSun : Image and Video Compression for Multimedia Engineering -  Fundamentals, Algorithms & Standards, CRC press, 2008

4.Jan Vozer : Video Compression for Multimedia, AP Profes, NewYork, 1995.

5. William Stallings, “Cryptography and Network Security”, 6th. Ed, Prentice Hall of India, New Delhi ,2013

6. William Stallings, “Network Security Essentials”, 5^thed. Prentice Hall of India, New Delhi

ESE-50

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.

Project Work 

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

▪ Dissertation (Exclusive assessment of Project Report): 100 Marks

♦ Internal Review : 50 Marks

♦ External review : 50 Marks