Department of


Syllabus for

1 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I  4  4  100 
MDS131L  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I  4  4  100 
MDS132  PROBABILITY AND DISTRIBUTION THEORY  4  4  100 
MDS132L  PROBABILITY AND DISTRBUTION THEORY  4  4  100 
MDS133  PRINCIPLES OF DATA SCIENCE  4  4  100 
MDS133L  PRINCIPLE OF DATA SCIENCE  4  4  100 
MDS134  RESEARCH METHODOLOGY  2  2  50 
MDS134L  RESEARCH METHODOLOGY  2  2  50 
MDS161A  INTRODUCTION TO STATISTICS  2  2  50 
MDS161B  INTRODUCTION TO COMPUTERS AND PROGRAMMING  2  2  50 
MDS161C  LINUX ADMINISTRATION  2  2  50 
MDS161L  PROBLEM SOLVING AND PROGRAMMING CONCEPTS  2  2  50 
MDS171  DATA BASE TECHNOLOGIES  6  5  150 
MDS171L  DATABASE TECHNOLOGY LABORATORY  6  5  50 
MDS172  INFERENTIAL STATISTICS  6  5  150 
MDS172L  INFERENTIAL STATISTICAL LABORATORY  6  5  150 
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON  6  4  100 
MDS173L  PROGRAMMING FOR DATA SCIENCE IN PYTHON  6  4  100 
2 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MDS231  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II  4  4  100 
MDS231L  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  II  4  4  100 
MDS232  REGRESSION ANALYSIS  4  4  100 
MDS232L  REGRESSION ANALYSIS  4  4  100 
MDS233  DESIGN AND ANALYSIS OF ALGORITHMS  4  4  100 
MDS233L  DESIGN AND ANALYSIS OF ALGORITHMS  4  4  100 
MDS234  MACHINE LEARNING  4  4  100 
MDS234L  MACHINE LEARNING  60  4  100 
MDS241A  MULTIVARIATE ANALYSIS  4  4  100 
MDS241AL  MULTIVARIATE ANALYSIS  4  4  100 
MDS241B  STOCHASTIC PROCESS  4  4  100 
MDS251L  PROGRAMMING FOR DATA SCIENCE IN R  6  4  100 
MDS252AL  HADOOP  6  5  150 
MDS271  PROGRAMMING FOR DATA SCIENCE IN R  6  4  100 
MDS272A  HADOOP  6  5  150 
MDS272B  IMAGE AND VIDEO ANALYTICS  6  5  150 
MDS272C  INTERNET OF THINGS  6  5  150 
MDS281  RESEARCH PROBLEM IDENTIFICATION AND DATA COLLECTION  1  0  0 
 
Assesment Pattern  
CIA  50% ESE  50%  
Examination And Assesments  
CIA  50% ESE  50%  
Department Overview:  
Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.  
Mission Statement:  
Vision
The Department of Computer Science endeavours to imbibe the vision of the University ?Excellence and Service?. The department is committed to this philosophy which pervades every aspect and functioning of the department.
Mission
?To develop IT professionals with ethical and human values?. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the st  
Introduction to Program:  
Data Science is popular in all academia, business sectors, and research and development to make effective decision in day to day activities. MSc in Data Science is a two year programme with four semesters. This programme aims to provide opportunity to all candidates to master the skill sets specific to data science with research bent. The curriculum supports the students to obtain adequate knowledge in theory of data science with hands on experience in relevant domains and tools. Candidate gains exposure to research models and industry standard applications in data science through guest lectures, seminars, projects, internships, etc.  
Program Objective:  
Programme Objective
? To acquire indepth understanding of the theoretical concepts in statistics, data analysis, data mining, machine learning and other advanced data science techniques.
? To gain practical experience in programming tools for data sciences, database systems, machine learning and big data tools.
? To strengthen the analytical and problem solving skill through developing real time applications.
? To empower students with tools and techniques for handling, managing, analyzing and interpreting data.
? To imbibe quality research and develop solutions to the social issues.
Programme Specific Outcomes
PSO1: Abstract thinking: Ability to understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science.
PSO2: Problem Analysis and Design Ability to identify analyze and design solutions for data science problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines.
PSO3: Modern software tool usage: Acquire the skills in handling data science programming tools towards problem solving and solution analysis for domain specific problems.
PSO4: Innovation And Entrepreneurship: Produce innovative IT solutions and services based on global needs and trends.
PSO5: Societal And Environmental Concern: Utilize the data science theories for societal and environmental concerns.
PSO6: Professional Ethics: Understand and commit to professional ethics and  
 
Assesment Pattern  
6040  
Examination And Assesments  
CIA1 CIA2 CIA3 & OR MSE  
Department Overview:  
Department of Data Science of Christ (Deemed to be University), Lavasa is started to shape outstanding Data Scientist and Analytics professionals with ethical and human values. The department offers degrees Bachelors of Science, Master of Science in Data Science and Doctor of Philosophy in the areas of Computer Science and Engineering. The department has rich expertise in the term of faculty resource who are well trained in various fields like Data Science, Data Security, Data Analytics, Artificial Intelligence, Machine learning, Computer Vision, Algorithms Design, Computer Networking, Data mining, BIG DATA, text mining, knowledge representation, soft computing, Cloud computing, etc.. The department has wide variety of labs setup namely Machine learning lab, Data Analytics Lab, Open Source lab, etc... Dedicated for the handson training of the students for their lab curriculum and research.
The department intermittently organize handson workshop on recent technology like Machine learning, Cloud Computing, Hadoop etc. for the students to keep them industry ready. The department equip students with holistic education to be better citizens.  
Mission Statement:  
*Vision
Enrich Ethical Scientific Excellence?
*Mission
1.To develop Data Science professionals with ethical and social values.
2. Divulge stateofart knowledge in the area of Data Science and Analytics.
3. Encourages the research and innovation.?
4. Accustoms the students with current industry practices, team work and entrepreneurship.  
Introduction to Program:  
Data science is an interdisciplinary response to this demand, and in our BSc degree programme students follow a carefully selected curriculum from Computer Science, Mathematics and Statistics.
There are three general steps to becoming a data scientist: Earn a bachelor's degree in IT, computer science, math, physics, or another related field; Earn a master's degree in data or related field; Gain experience in the field you intend to work in (ex: healthcare, physics, business).
The best path to becoming a data scientist depends on an individual's background.
Many people currently working in data science come from backgrounds in math, statistics, or computer science.
MSc Data Science
The MSc Data Science will provide students with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science. The MSc Data Science provides training in data science methods, emphasising statistical perspectives. After the program students will receive a thorough grounding in theory, as well as the technical and practical skills of data science.
Students theoretical learning will be at a high mathematical level, while the technical and practical skills students will gain will enable them to apply advanced methods of data science and statistics to investigate real world questions.  
Program Objective:  
Programme Educational Objectives (PEO)
PEO1: Ability to understand, analyze and design solutions with professional competency for the realworld problems.
PEO2: Ability to develop software solutions for the requirements, based on critical analysis and research.
PEO3: Ability to Function effectively in a team and as an individual in a multidisciplinary / multicultural environment.
PEO4: To provide a learning environment that fosters scientific excellence and promote lifelong learning with understanding of professional responsibilities and obligations to clients and public.
*Programme Specific Outcomes
PSO1: Abstract thinking: Ability to understand the abstract concepts that lead to various data science theories in Mathematics, Statistics and Computer science.
PSO2: Problem Analysis and Design Ability to identify analyze and design solutions for data science problems using fundamental principles of mathematics, Statistics, computing sciences, and relevant domain disciplines.
PSO3: Modern software tool usage: Acquire the skills in handling data science programming tools towards problem solving and solution analysis for domain specific problems.
PSO4: Innovation and Entrepreneurship: Produce innovative IT solutions and services based on global needs and trends.
PSO5: Societal and Environmental Concern: Utilize the data science theories for societal and environmental concerns.
PSO6: Professional Ethics: Understand and commit to professional ethics and cyber regulatio  
MDS131  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Linear Algebra plays a fundamental role in the theory of Data Science. This course aims at introducing the basic notions of vector spaces, Linear Algebra and the use of Linear Algebra in applications to Data Science. 

Learning Outcome 

CO1: Understand the properties of Vector spaces CO2: Use the properties of Linear Maps in solving problems on Linear Algebra CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces CO4: Apply mathematics for some applications in Data Science. 
Unit1 
Teaching Hours:15 
INTRODUCTION TO VECTOR SPACES


Vector Spaces: R^{n} and C^{n}, lists, F^{n}and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.  
Unit2 
Teaching Hours:20 
LINEAR MAPS


Definition of Linear Maps  Algebraic Operations on  Null spaces and Injectivity  Range and Surjectivity  Fundamental Theorems of Linear Maps  Representing a Linear Map by a Matrix  Invertible Linear Maps  Isomorphic Vector spaces  Linear Map as Matrix Multiplication  Operators  Products of Vector Spaces  Product of Direct Sum  Quotients of Vector spaces.  
Unit3 
Teaching Hours:10 
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES


Eigenvalues and Eigenvectors  Eigenvectors and Upper Triangular matrices  Eigenspaces and Diagonal Matrices  Inner Products and Norms  Linear functionals on Inner Product spaces.  
Unit4 
Teaching Hours:15 
MATHEMATICS APPLIED TO DATA SCIENCE


Singular value decomposition  Handwritten digits and simple algorithm  Classification of handwritten digits using SVD bases  Tangent distance  Text Mining.  
Text Books And Reference Books: [1] S. Axler, Linear algebra done right, Springer, 2017. [2] Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.  
Essential Reading / Recommended Reading [1] E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012. [2] J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011. [3] D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012. [4] P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press, 2015.  
Evaluation Pattern CIA  50% ESE  50%  
MDS131L  MATHEMATICAL FOUNDATION FOR DATA SCIENCE  I (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course Description The course provides comprehensive understanding of vector spaces and the use of linear algebra for Data Science applications.
Course Objectives Linear Algebra plays a fundamental role in the theory of Data Science. This course aims at introducing the basic notions of vector spaces, Linear Algebra and the use of Linear Algebra in applications to Data Science.


Learning Outcome 

CO1: Understand the properties of Vector spaces CO2: Use the properties of Linear Maps in solving problems on Linear Algebra CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces CO4: Apply mathematics for some applications in Data Science 
Unit1 
Teaching Hours:15 

INTRODUCTION TO VECTOR SPACES


Vector Spaces: R^{n} and C^{n}, lists, F^{n}and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.
 
Unit2 
Teaching Hours:20 

LINEAR MAPS


Definition of Linear Maps  Algebraic Operationson L(V,W)  Null spaces and Injectivity  Range and Surjectivity  Fundamental Theorems of Linear Maps  Representing a Linear MapbyaMatrixInvertibleLinearMapsIsomorphicVectorspacesLinearMapasMatrix MultiplicationOperatorsProductsofVectorSpacesProductofDirectSumQuotientsof Vector spaces.  
Unit3 
Teaching Hours:10 

EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES


Eigenvalues and Eigenvectors  Eigenvectors and Upper Triangular matrices  Eigenspaces and Diagonal Matrices  Inner Products and Norms  Linear functionals on Inner Product spaces.  
Unit4 
Teaching Hours:15 

MATHEMATICS APPLIED TO DATA SCIENCE


Singular value decomposition  Handwritten digits and simple algorithm  Classification of handwritten digits using SVD bases  Tangent distance  Text Mining.  
Text Books And Reference Books:
[1] S. Axler, Linear algebra done right, Springer,2017. [2] EldénLars,Matrixmethodsindataminingandpatternrecognition,SocietyforIndustrial and Applied Mathematics,2007.  
Essential Reading / Recommended Reading
[1] E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012. [2] J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics,2011. [3] D.A.Simovici,Linearalgebratoolsfordatamining,WorldScientificPublishing,2012. [4] P.N.Klein,Codingthematrix:linearalgebrathroughapplicationstocomputerscience, Newtonian Press,2015.  
Evaluation Pattern
 
MDS132  PROBABILITY AND DISTRIBUTION THEORY (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

To enable the students to understand the properties and applications of various probability functions. 

Learning Outcome 

CO1: Demonstrate the random variables and its functions CO2: Infer the expectations for random variable functions and generating functions. CO3: Demonstrate various discrete and continuous distributions and their usage 
Unit1 
Teaching Hours:10 
ALGEBRA OF PROBABILITY


Algebra of sets  fields and sigma  fields, Inverse function Measurable function – Probability measure on a sigma field – simple properties  Probability space  Random variables and Random vectors – Induced Probability space – Distribution functions –Decomposition of distribution functions.  
Unit2 
Teaching Hours:10 
EXPECTATION AND MOMENTS OF RANDOM VARIABLES


Definitions and simple properties  Moment inequalities – Holder, Jenson Inequalities – Characteristic function – definition and properties – Inversion formula. Convergence of a sequence of random variables  convergence in distribution  convergence in probability almost sure convergence and convergence in quadratic mean  Weak and Complete convergence of distribution functions – Helly  Bray theorem  
Unit3 
Teaching Hours:10 
LAW OF LARGE NUMBERS


Khintchin's weak law of large numbers, Kolmogorov strong law of large numbers (statement only) – Central Limit Theorem – Lindeberg – Levy theorem, Linderberg – Feller theorem (statement only), Liapounov theorem – Relation between Liapounov and Linderberg –Feller forms – Radon Nikodym theorem and derivative (without proof) – Conditional expectation – definition and simple properties.  
Unit4 
Teaching Hours:10 
DISTRIBUTION THEORY


Distribution of functions of random variables – Laplace, Cauchy, Inverse Gaussian, Lognormal, Logarithmic series and Power series distributions  Multinomial distribution  Bivariate Binomial – Bivariate Poisson – Bivariate Normal  Bivariate Exponential of Marshall and Olkin  Compound, truncated and mixture of distributions, Concept of convolution  Multivariate normal distribution (Definition and Concept only)  
Unit5 
Teaching Hours:10 
SAMPLING DISTRIBUTION


Sampling distributions: Non  central chi  square, t and F distributions and their properties  Distributions of quadratic forms under normality independence of quadratic form and a linear form  Cochran’s theorem.  
Unit6 
Teaching Hours:10 
ORDER STATISTICS


Order statistics, their distributions and properties  Joint and marginal distributions of order statistics  Distribution of range and mid range Extreme values and their asymptotic distributions (concepts only)  Empirical distribution function and its properties – Kolmogorov  Smirnov distributions – Life time distributions Exponential and Weibull distributions  Mills ratio – Distributions classified by hazard rate  
Text Books And Reference Books: [1]. Modern Probability Theory, B.R Bhat, New Age International, 4^{th} Edition, 2014. [2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3^{rd} Edition, 2015.  
Essential Reading / Recommended Reading ]1]. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3^{rd} Edition (Reprint), 2017. [2]. Order Statistics, H.A David and H.N Nagaraja, John Wiley & Sons, 3^{rd} Edition, 2003.  
Evaluation Pattern CIA  50% ESE  50%  
MDS132L  PROBABILITY AND DISTRBUTION THEORY (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To enable the students to understand the properties and applications of various probability functions. 

Learning Outcome 

CO1: Demonstrate the random variables and its functions CO2: Infer the expectations for random variable functions and generating functions. CO3: Demonstrate various discrete and continuous distributions and their usage 
Unit1 
Teaching Hours:10 
ALGEBRA OF PROBABILITY


Algebra of sets  fields and sigma  fields, Inverse function Measurable function – Probability measure on a sigma field – simple properties  Probability space  Random variables and Random vectors – Induced Probability space – Distribution functions – Decomposition of distribution functions.  
Unit2 
Teaching Hours:10 
EXPECTATION AND MOMENTS OF RANDOM VARIABLES


Definitions and simple properties  Moment inequalities – Holder, Jenson Inequalities – Characteristic function – definition and properties – Inversion formula. Convergence of a sequence of random variables  convergence in distribution  convergence in probability almost sure convergence and convergence in quadratic mean  Weak and Complete convergence of distribution functions – Helly  Bray theorem.  
Unit3 
Teaching Hours:10 
LAW OF LARGE NUMBERS


Khintchin's weak law of large numbers, Kolmogorov strong law of large numbers (statement only) – Central Limit Theorem – Lindeberg – Levy theorem, Linderberg – Feller theorem (statement only), Liapounov theorem – Relation between Liapounov and Linderberg –Feller forms – Radon Nikodym theorem and derivative (without proof) – Conditional expectation – definition and simple properties.  
Unit4 
Teaching Hours:10 
DISTRIBUTION THEORY


Distribution of functions of random variables – Laplace, Cauchy, Inverse Gaussian, Lognormal, Logarithmic series and Power series distributions  Multinomial distribution  Bivariate Binomial – Bivariate Poisson – Bivariate Normal  Bivariate Exponential of Marshall and Olkin  Compound, truncated and mixture of distributions, Concept of convolution  Multivariate normal distribution (Definition and Concept only)  
Unit5 
Teaching Hours:10 
SAMPLING DISTRIBUTION


Sampling distributions: Non  central chi  square, t and F distributions and their properties  Distributions of quadratic forms under normality independence of quadratic form and a linear form  Cochran’s theorem  
Unit6 
Teaching Hours:10 
ORDER STATISTICS


Order statistics, their distributions and properties  Joint and marginal distributions of order statistics  Distribution of range and mid range Extreme values and their asymptotic distributions (concepts only)  Empirical distribution function and its properties – Kolmogorov  Smirnov distributions – Life time distributions Exponential and Weibull distributions  Mills ratio – Distributions classified by hazard rate  
Text Books And Reference Books: Modern Probability Theory, B.R Bhat, New Age International, 4th Edition, 2014. 2. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015.  
Essential Reading / Recommended Reading Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3rd Edition (Reprint), 2017. Order Statistics, H.A David and H.N Nagaraja, John Wiley & Sons, 3rd Edition, 2003.  
Evaluation Pattern CIA1 MSE CIA2 ESE  
MDS133  PRINCIPLES OF DATA SCIENCE (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To provide strong foundation for data science and application area related to it and understand the underlying core concepts and emerging technologies in data science. 

Learning Outcome 

CO1: Understand the fundamental concepts of data science CO2: Evaluate the data analysis techniques for applications handling large data CO3: Demonstrate the various machine learning algorithms used in data science process CO4: Understand the ethical practices of data science CO4:Visualize and present the inference using various tools CO5:Learn to think through the ethics surrounding privacy, data sharing and algorithmic decisionmaking 
Unit1 
Teaching Hours:10 
INTRODUCTION TO DATA SCIENCE


Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Data Scientist  Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation.  
Unit2 
Teaching Hours:10 
BIG DATA


Problems when handling large data – General techniques for handling large data – Case study – Steps in big data – Distributing data storage and processing with Frameworks – Case study.  
Unit3 
Teaching Hours:10 
MACHINE LEARNING


Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.  
Unit4 
Teaching Hours:10 
DEEP LEARNING


Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.  
Unit5 
Teaching Hours:10 
DATA VISUALIZATION


Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.jssummary.  
Unit6 
Teaching Hours:10 
ETHICS AND RECENT TRENDS


Data Science Ethics – Doing good data science – Owners of the data  Valuing different aspects of privacy  Getting informed consent  The Five Cs – Diversity – Inclusion – Future Trends.  
Text Books And Reference Books: [1]. Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning Publications Co., 1^{st} edition, 2016 [2]. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1^{st} edition, 2013 [3]. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1^{st} edition, 2016 [4]. Ethics and Data Science, D J Patil, Hilary Mason, Mike Loukides, O’ Reilly, 1^{st} edition, 2018  
Essential Reading / Recommended Reading [1]. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1^{st} edition, 2015 [2]. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’ Reilly, 1^{st} edition, 2013 [3]. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2^{nd} edition, 2014  
Evaluation Pattern CIA  50% ESE  50%  
MDS133L  PRINCIPLE OF DATA SCIENCE (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To provide strong foundation for data science and application area related to it and understand the underlying core concepts and emerging technologies in data science. 

Learning Outcome 

CO1:Explore the fundamental concepts of data science CO2:Understand data analysis techniques for applications handling large data CO3:Understand various machine learning algorithms used in data science process CO4:Visualize and present the inference using various tools CO5:Learn to think through the ethics surrounding privacy, data sharing and algorithmic decisionmaking 
Unit1 
Teaching Hours:10 
INTRODUCTION TO DATA SCIENCE


Definition – Big Data and Data Science Hype – Why data science – Getting Past the Hype – The Current Landscape – Who is Data Scientist?  Data Science Process Overview – Defining goals – Retrieving data – Data preparation – Data exploration – Data modeling – Presentation  
Unit2 
Teaching Hours:10 
BIG DATA


Problems when handling large data – General techniques for handling large data – Case study – Steps in big data – Distributing data storage and processing with Frameworks – Case study.  
Unit3 
Teaching Hours:10 
MACHINE LEARNING


Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.  
Unit4 
Teaching Hours:10 
DEEP LEARNING


Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.  
Unit5 
Teaching Hours:10 
DATA VISUALIZATION


Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.jssummary.  
Unit6 
Teaching Hours:10 
ETHICS AND RECENT TRENDS


Data Science Ethics – Doing good data science – Owners of the data  Valuing different aspects of privacy  Getting informed consent  The Five Cs – Diversity – Inclusion – Future Trends.  
Text Books And Reference Books: [1]. Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning Publications Co., 1st edition, 2016 [2]. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1st edition, 2013 [3]. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 1st edition, 2016 [4]. Ethics and Data Science, D J Patil, Hilary Mason, Mike Loukides, O’ Reilly, 1st edition, 2018  
Essential Reading / Recommended Reading [1]. Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st edition, 2015 [2]. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’ Reilly, 1st edition, 2013 [3]. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014  
Evaluation Pattern 6040  
MDS134  RESEARCH METHODOLOGY (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

This course is intended to assist students in planning and carrying out research. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and leads through the various methodologies involved in the research process. It focus on finding out the research gap from the literature using computer technology,introduces basic statistics required for research and report the research outcomes scientifically with emphasis on research ethics. 

Learning Outcome 

CO1: Understand the essense of research and the necessity of defining a research problem. CO2: Apply research methods and methodology including research design, data analysis, and interpretation. CO3: Create scientific reports according to specified standards. 
Unit1 
Teaching Hours:8 
RESEARCH METHODOLOGY


Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem Ethics in Research.  
Unit2 
Teaching Hours:8 
RESEARCH DESIGN


Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,Online Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,Hindex.  
Unit3 
Teaching Hours:7 
RESEARCH DATA


Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.  
Unit4 
Teaching Hours:7 
REPORT WRITING


Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, Text, Tables, Figures, Equations, Citations, Referencing, and Templates (IEEE style), Paper writing for international journals, Writing scientific report.  
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4^{th}ed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.  
Evaluation Pattern CIA  50% ESE  50%  
MDS134L  RESEARCH METHODOLOGY (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The research methodology module is intended to assist students in planning and carrying out research projects. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and carries through the various methodologies involved. It continues with finding out the literature using computer technology, basic statistics required for research and ends with linear regression. 

Learning Outcome 

CO1: Define research and describe the research process and research methods CO2: Understand and apply basic research methods including research design, data analysis, and interpretation 
Unit1 
Teaching Hours:8 
RESEARCH METHODOLOGY


Defining research problem  selecting the problem  necessity of defining the problem  techniques involved in defining a problem  Ethics in Research.  
Unit2 
Teaching Hours:8 
RESEARCH DESIGN


Principles of experimental design Working with Literature: Importance, finding literature, using your resources, managing the literature, keep track of references, using the literature, literature review. Online Searching: Database – SCIFinder – Scopus  Science Direct  Searching research articles  Citation Index  Impact Factor  Hindex etc.  
Unit3 
Teaching Hours:7 
RESEARCH DATA


Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.  
Unit4 
Teaching Hours:7 
REPORT WRITING


Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates (IEEE style), paper writing for international journals, Writing scientific report.  
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.  
Evaluation Pattern CIA1 Evaluated out of = 20 Marks Converted to = 10 CIA2 Evaluated out of = 50 Marks Converted to = 25 CIA3 Evaluated out of = 20 Marks Converted to = 10
Total CIA marks after conversion = 45 Attendance Marks = 5 ESE final Marks = 50
 
MDS161A  INTRODUCTION TO STATISTICS (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To enable the students to understand the fundamentals of statistics to apply descriptive measures and probability for data analysis. 

Learning Outcome 

CO1: Demonstrate the history of statistics and present the data in various forms. CO2: Infer the concept of correlation and regression for relating two or more related variables. CO3: Demonstrate the probabilities for various events. 
Unit1 
Teaching Hours:8 
ORGANIZATION AND PRESENTATION OF DATA


Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions  
Unit2 
Teaching Hours:8 
DESCRIPTIVE STATISTICS


Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis.  
Unit3 
Teaching Hours:7 
CORRELATION AND REGRESSION


Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties.  
Unit4 
Teaching Hours:7 
BASICS OF PROBABILITY


Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications  
Text Books And Reference Books: [1]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015. [2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.  
Essential Reading / Recommended Reading [1]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. [2]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. [3]. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. [4]. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.  
Evaluation Pattern CIA  50% ESE  50%  
MDS161B  INTRODUCTION TO COMPUTERS AND PROGRAMMING (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To enable the students to understand the fundamental concepts of problem solving and programming structures. 

Learning Outcome 

CO1: Demonstrate the systematic approach for problem solving using computers. CO2: Apply different programming structure with suitable logic for computational problems. 
Unit1 
Teaching Hours:10 
COMPUTERS AND DIGITAL BASICS


Number Representation – Decimal, Binary, Octal, Hexadecimal and BCD numbers – Binary Arithmetic – Binary addition – Unsigned and Signed numbers – one’s and two’s complements of Binary numbers – Arithmetic operations with signed numbers  Number system conversions – Boolean Algebra – Logic gates – Design of Circuits – K  Map  
Unit2 
Teaching Hours:5 
GENERAL PROBLEM SOLVING CONCEPTS


Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer  Functions – Operators – Expressions and Equations  
Unit3 
Teaching Hours:5 
PLANNING FOR SOLUTION


Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle.  
Unit4 
Teaching Hours:10 
PROBLEM SOLVING


Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure. Problem solving with decisions – the decision logic structure – straight through logic – positive logic – negative logic – logic conversion – decision tables – case logic structure  examples.  
Text Books And Reference Books: [1] Thomas L.Floyd and R.P.Jain,“Digital Fundamentals”,8th Edition, Pearson Education,2007. [2] Peter Norton “Introduction to Computers”,6th Edition, Tata Mc Graw Hill, New Delhi,2006. [3] Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9^{th} Edition, 2012  
Essential Reading / Recommended Reading [1]. E Balagurusamy, Fundamentals of Computers, TMH, 2011  
Evaluation Pattern CIA  50% ESE  50%  
MDS161C  LINUX ADMINISTRATION (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To Enable the students to excel in the Linux Platform 

Learning Outcome 

CO1: Demostrate the systematic approach for configure the Liux environment CO2: Manage the Linux environment to work with open source data science tools 
Unit1 
Teaching Hours:10 
Unit I


RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, commandline environments, and documentation  Configure local storage using partitions and logical volumes  
Unit2 
Teaching Hours:10 
UNIT II


Unit3 
Teaching Hours:10 
UNIT  III


Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab  Configure firewall settings using firewall config, firewallcmd, or iptables , Configure keybased authentication for SSH ,Set enforcing and permissive modes for SELinux , List and identify SELinux file and process context ,Restore default file contexts  
Text Books And Reference Books: https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/  
Essential Reading / Recommended Reading https://access.redhat.com/documentation/enUS/Red_Hat_Enterprise_Linux/7/  
Evaluation Pattern CIA  50% ESE  50%  
MDS161L  PROBLEM SOLVING AND PROGRAMMING CONCEPTS (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To enable the students to understand the fundamental concepts of problem solving and programming structures. 

Learning Outcome 

CO1: Demonstrate the systematic approach for problem solving using computers. CO2: Apply different programming structure with suitable logic for computational problems. 
Unit1 
Teaching Hours:8 
GENERAL PROBLEM SOLVING CONCEPTS


Types of Problems – Problem solving with Computers – Difficulties with problem solving – problem solving concepts for the Computer – Constants and Variables – Rules for Naming and using variables – Data types – numeric data – character data – logical data – rules for data types – examples of data types – storing the data in computer  Functions – Operators – Expressions and Equations  
Unit2 
Teaching Hours:8 
PLANNING FOR SOLUTION


Communicating with computer – organizing the solution – Analyzing the problem – developing the interactivity chart – developing the IPO chart – Writing the algorithms – drawing the flow charts – pseudocode – internal and external documentation – testing the solution – coding the solution – software development life cycle.  
Unit3 
Teaching Hours:7 
PROBLEM SOLVING  I


Introduction to programming structure – pointers for structuring a solution – modules and their functions – cohesion and coupling – problem solving with logic structure.  
Unit4 
Teaching Hours:7 
PROBLEM SOLVING  II


 
Text Books And Reference Books: [1]. Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9th Edition, 2012  
Essential Reading / Recommended Reading E Balagurusamy, Fundamentals of Computers, TMH, 2011  
Evaluation Pattern CIA1 CIA2 CIA3  
MDS171  DATA BASE TECHNOLOGIES (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

The main objective of this course is to fundamental knowledge and practical experience with, database concepts. It includes the concepts and terminologies which facilitate the construction of database tables and write effective queries. Also, to Comprehend Data warehouse and its functions. 

Learning Outcome 

CO1: Design conceptual models of a database using ER modeling CO2: Create and populate a RDBMS for a real life application, with constraints and keys, using SQL CO3: Retrieve any type of information from a data base by formulating complex queries in SQL CO4: Demonstrate various databases CO5: Distinguish database from data warehouse and examine ETL process 
Unit1 
Teaching Hours:16 
INTRODUCTION


Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, EntityRelationship Diagram, Weak Entity Sets, Extended ER features Lab Exercises 1. Data Definition, 2. Table Creation 3. Specification of Constraints  
Unit2 
Teaching Hours:16 
RELATIONAL MODEL AND DATABASE DESIGN


SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization : using functional dependencies, BoyceCodd Normal Form, 4NF, 5NF Lab Exercises 1. Insert, Select, Update & Delete Commands 2. Nested Queries & Join Queries 3. Views  
Unit3 
Teaching Hours:10 
INTELLIGENT DATABASES


Active databases, Deductive Databases, Knowledge bases, Multimedia Databases, Multidimensional Data Structures, Image Databases, Text/Document Databases, Video Databases, Audio Databases, Multimedia Database Design.  
Unit4 
Teaching Hours:16 
DATA WAREHOUSE: THE BUILDING BLOCKS


Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars  
Unit5 
Teaching Hours:16 
REQUIREMENTS, REALITIES, ARCHITECTURE AND DATA FLOW


Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables (CH:1,2,3,4,5,6) Lab Exercises: 1. Importing source data structures 2. Design Target Data Structures 3. Create target structure 4. Design and build the ETL mapping  
Unit6 
Teaching Hours:16 
IMPLEMENTATION, OPERATIONS AND ETL SYSTEMS:


Development, Operations, Metadata, RealTime ETL Systems. (CH:7,8,9,11)
Lab Exercises: 1. Perform the ETL process and transform into data map 2. Create the cube and process it 3. Generating Reports 4. Creating the Pivot table and pivot chart using some existing data  
Text Books And Reference Books: [1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002  
Essential Reading / Recommended Reading [1] LiorRokach and OdedMaimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.  
Evaluation Pattern CIA  50% ESE  50%  
MDS171L  DATABASE TECHNOLOGY LABORATORY (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:50 
Credits:5 
Course Objectives/Course Description 

The main objective of this course is to fundamental knowledge and practical experience with, database concepts. It includes the concepts and terminologies which facilitate the construction of database tables and write effective queries. Also, to Comprehend Data warehouse and its functions. 

Learning Outcome 

CO1: Design conceptual models of a database using ER modeling CO2: Create and populate a RDBMS for a real life application, with constraints and keys, using SQL CO3: Retrieve any type of information from a data base by formulating complex queries in SQL CO4: Demonstrate various databases CO5: Distinguish database from data warehouse and examine ETL process 
Unit1 
Teaching Hours:14 
INTRODUCTION


Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, EntityRelationship Diagram, Weak Entity Sets, Extended ER features  
Unit1 
Teaching Hours:14 
LAB EXERCISES


1. Data Definition 2. Table Creation 3. Constraints  
Unit2 
Teaching Hours:16 
RELATIONAL MODEL AND DATABASE DESIGN


SQL and Integrity Constraints, Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null Values, Domain Constraints, Referential Integrity Constraints, assertions, views, Nested Subqueries, Functional Dependency, Different anomalies in designing a Database, Normalization : using functional dependencies, BoyceCodd Normal Form, 4NF, 5NF  
Unit2 
Teaching Hours:16 
LAB EXERCISES


1. Insert, Select, Update & Delete Commands 2. Nested Queries & Join Queries 3. Views
 
Unit3 
Teaching Hours:14 
INTELLIGENT DATABASES


Active databases, Deductive Databases, Knowledge bases, Multimedia Databases, Multidimensional Data Structures, Image Databases, Text/Document Databases, Video Databases, Audio Databases, Multimedia Database Design.  
Unit4 
Teaching Hours:16 
DATA WAREHOUSE: THE BUILDING BLOCKS


Defining Features, Data Warehouses and Data Marts, Architectural Types, Overview of the Components, Metadata in the Data warehouse, Data Design and Data Preparation: Principles of Dimensional Modeling, Dimensional Modeling Advanced Topics From Requirements To Data Design, The Star Schema, Star Schema Keys, Advantages of the Star Schema, Star Schema: Examples, Dimensional Modeling: Advanced Topics, Updates to the Dimension Tables, Miscellaneous Dimensions, The Snowflake Schema, Aggregate Fact Tables, Families Oo Stars  
Unit5 
Teaching Hours:14 
REQUIREMENTS, REALITIES, ARCHITECTURE AND DATA FLOW


ETL Data Structures, Extracting, Cleaning and Conforming, Delivering
Dimension Tables, Delivering Fact Tables (CH:1,2,3,4,5,6)  
Unit5 
Teaching Hours:14 
LAB EXERCISES


1. Importing source data structures 2. Design Target Data Structures 3. Create target structure 4. Design and build the ETL mapping  
Unit6 
Teaching Hours:16 
IMPLEMENTATION, OPERATIONS AND ETL SYSTEMS


Development, Operations, Metadata, RealTime ETL Systems. (CH:7,8,9,11)  
Unit6 
Teaching Hours:16 
LAB EXERCISES


1. Perform the ETL process and transform into data map 2. Create the cube and process it 3. Generating Reports 4. Creating the Pivot table and pivot chart using some existing data  
Text Books And Reference Books: [1]. Henry F. Korth and Silberschatz Abraham, “Database System Concepts”, Mc.Graw Hill. [2]. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007. [3]. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd John Wiley & Sons, Inc. New York, USA, 2002  
Essential Reading / Recommended Reading [1] Lior Rokach and Oded Maimon, Data Mining and Knowledge Discovery Handbook, Springer, 2nd edition, 2010.  
Evaluation Pattern CIA 50% ESE  50%
 
MDS172  INFERENTIAL STATISTICS (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

This course is designed to introduce the concepts of theory of estimation and testing of hypothesis. This paper also deals with the concept of parametric tests for large and small samples. It also provides knowledge about nonparametric tests and its applications. 

Learning Outcome 

CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples. CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses. CO3: Infer the concept of nonparametric tests for single sample and two samples. 
Unit1 
Teaching Hours:15 
SUFFICIENT STATISTICS


Neyman  Fisher Factorisation theorem  the existence and construction of minimal sufficient statistics  Minimal sufficient statistics and exponential family  sufficiency and completeness  sufficiency and invariance. Lab Exercise
 
Unit2 
Teaching Hours:15 
UNBIASED ESTIMATION


Minimum variance unbiased estimation  locally minimum variance unbiased estimators  Rao Blackwell – theorem – Completeness: Lehmann Scheffe theorems  Necessary and sufficient condition for unbiased estimators  Cramer Rao lower bound  Bhattacharya system of lower bounds in the 1parameter regular case  Chapman Robbins inequality Lab Exercise
 
Unit3 
Teaching Hours:15 
MAXIMUM LIKELIHOOD ESTIMATION


Computational routines  strong consistency of maximum likelihood estimators  Asymptotic Efficiency of maximum likelihood estimators  Best Asymptotically Normal estimators  Method of moments  Bayes’ and minimax estimation: The structure of Bayes’ rules  Bayes’ estimators for quadratic and convex loss functions  minimax estimation  interval estimation. Lab Exercise
 
Unit4 
Teaching Hours:15 
HYPOTHESIS TESTING


Uniformly most powerful tests  the NeymanPearson fundamental Lemma  Distributions with monotone likelihood ratio  Problems  Generalization of the fundamental lemma, two sided hypotheses  testing the mean and variance of a normal distribution. Lab Exercise
 
Unit5 
Teaching Hours:15 
MEAN TESTS


Unbiasedness for hypotheses testing  similarity and completeness  UMP unbiased tests for multi parameter exponential families  comparing two Poisson or Binomial populations  testing the parameters of a normal distribution (unbiased tests)  comparing the mean and variance of two normal distributions  Symmetry and invariance  maximal invariance  most powerful invariant tests. Lab Exercise
 
Unit6 
Teaching Hours:15 
SEQUENTIAL TESTS


SPRT procedures  likelihood ratio tests  locally most powerful tests  the concept of confidence sets  non parametric tests. Lab Exercise
 
Text Books And Reference Books: [1]. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012. [2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015.  
Essential Reading / Recommended Reading [1]. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3rd Edition (Reprint), 2017. [2]. Linear Statistical Inference and its Applications, Rao C.R, Willy Publications, 2nd Edition, 2001.  
Evaluation Pattern CIA  50% ESE  50%  
MDS172L  INFERENTIAL STATISTICAL LABORATORY (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

Course Objectives This course is designed to introduce the concepts of theory of estimation and testing of hypothesis. This paper also deals with the concept of parametric tests for large and small samples. It also provides knowledge about nonparametric tests and its applications.


Learning Outcome 

Course Learning Outcomes CO1: Demonstrate the concepts of point and interval estimation of unknown parameters and their significance using large and small samples. CO2: Apply the idea of sampling distributions of difference statistics in testing of hypotheses. CO3: Infer the concept of nonparametric tests for single sample and two samples. 
Unit1 
Teaching Hours:15 

SUFFICIENT STATISTICS


Neyman  Fisher Factorisation theorem  the existence and construction of minimal sufficient statistics  Minimal sufficient statistics and exponential family  sufficiency and completeness  sufficiency and invariance  
Unit2 
Teaching Hours:15 

UNBIASED ESTIMATION


Minimum variance unbiased estimation  locally minimum variance unbiased estimators  Rao Blackwell – theorem – Completeness: Lehmann Scheffe theorems  Necessary and sufficient condition for unbiased estimators  Cramer Rao lower bound  Bhattacharya system of lower bounds in the 1parameter regular case  Chapman Robbins inequality  
Unit3 
Teaching Hours:15 

MAXIMUM LIKELIHOOD ESTIMATION


Computational routines  strong consistency of maximum likelihood estimators  Asymptotic Efficiency of maximum likelihood estimators  Best Asymptotically Normal estimators  Method of moments  Bayes’ and minimax estimation: The structure of Bayes’ rules  Bayes’ estimators for quadratic and convex loss functions  minimax estimation  interval estimation.  
Unit4 
Teaching Hours:15 

HYPOTHESIS TESTING


Uniformly most powerful tests  the NeymanPearson fundamental Lemma  Distributions with monotone likelihood ratio  Problems  Generalization of the fundamental lemma, two sided hypotheses  testing the mean and variance of a normal distribution.  
Unit5 
Teaching Hours:15 

MEAN TESTS


Unbiased ness for hypotheses testing  similarity and completeness  UMP unbiased tests for multi parameter exponential families  comparing two Poisson or Binomial populations  testing the parameters of a normal distribution (unbiased tests)  comparing the mean and variance of two normal distributions  Symmetry and invariance  maximal invariance  most powerful invariant tests.  
Unit6 
Teaching Hours:15 

SEQUENCTIAL TESTS


SPRT procedures  likelihood ratio tests  locally most powerful tests  the concept of confidence sets  non parametric tests.  
Text Books And Reference Books: Essential Reading [1]. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012. [2]. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015.  
Essential Reading / Recommended Reading Recommended Reading [1]. Introduction to the theory of statistics, A.M Mood, F.A Graybill and D.C Boes, Tata McGrawHill, 3rd Edition (Reprint), 2017. [2]. Linear Statistical Inference and its Applications, Rao C.R, Willy Publications, 2nd Edition, 2001.  
Evaluation Pattern 1) CIA COMPONENTS – EVALUATION RUBRICS CIA 1:Component 1 Assignment Title: Multiple Choice question test for basics of interval estimation and point estimation Assignment type: Individual No of Test  3 Assignment details: 1. Each learner will be given 20 questions in the classroom 2. Quiz will be taken on Moodle. 3. Each learner will be able to take up the test only once. A retest will be conducted only for absentees, who have a genuine reason to justify. 4. Maximum time limit for answering the questions is 30 minutes.
Tentative Date: 3rd week of August 2019 Venue: Classroom Submission Format: To be handwritten in A4 size answer sheets. Assignment Learning Objectives: 1. To enable the learner understand the point and interval estimation concept. 2. To enable learner to critically examine the sampling distributions. 3. To enable learner to understand how to obtain estimate of standard errors. Assessment Strategies aligned to LO: 1. The correct answers will be evaluated and marks will be given to each question. 2. Evaluation of the various scenarios included in problem solving.
CIA 1: Component 2 Assignment Title: Theory assignment on unbiased estimation and Maximum likelihood estimation. No of Assignment  6 Assignment type: Individual Assignment details: 1. Each learner will be given 10 questions in the classroom edium problem solving. 2. The learners are expected to answer in an A4 size sheet and hand it over to the course instructor before the timeline.
Tentative Date: Last week of September 2019 Submission Format: To be handwritten in A4 size sheets. Assignment Learning Objectives: 1. To enable the learners to critically examine a real time problem and depict it in the form of sampling. 2. To analyze the interpretation capability of the problem and its explanation capability by discussing all the scenarios.
Assessment Strategies aligned to LO: 1. Learner will be evaluated as per the following criteria. 2. Understanding and explanation of each concept is analyzed.
CIA 3: Component 1 Assignment Title: Multiple Choice question test for basics of interval estimation and point estimation Assignment type: Individual No of Test  3 Assignment details: 1. Each learner will be given 20 questions in the classroom 2. Quiz will be taken on Moodle. 3. Each learner will be able to take up the test only once. A retest will be conducted only for absentees, who have a genuine reason to justify. 4. Maximum time limit for answering the questions is 30 minutes.
Tentative Date: 3rd week of August 2019 Venue: Classroom Submission Format: To be handwritten in A4 size answer sheets. Assignment Learning Objectives: 1. To enable the learner understand the point and interval estimation concept. 2. To enable learner to critically examine the sampling distributions. 3. To enable learner to understand how to obtain estimate of standard errors. Assessment Strategies aligned to LO: 1. The correct answers will be evaluated and marks will be given to each question. 2. Evaluation of the various scenarios included in problem solving.
CIA 3: Component 2 Assignment Title: Theory assignment on unbiased estimation and Maximum likelihood estimation. No of Assignment  6 Assignment type: Individual Assignment details: 1. Each learner will be given 10 questions in the classroom edium problem solving. 2. The learners are expected to answer in an A4 size sheet and hand it over to the course instructor before the timeline.
Tentative Date: Last week of September 2019 Submission Format: To be handwritten in A4 size sheets. Assignment Learning Objectives: 1. To enable the learners to critically examine a real time problem and depict it in the form of sampling. 2. To analyze the interpretation capability of the problem and its explanation capability by discussing all the scenarios.
Assessment Strategies aligned to LO: 1. Learner will be evaluated as per the following criteria. 2. Understanding and explanation of each concept is analyzed.
Laboratory Practices :( coding) : ( 2hrs/Week)
1. Drawing random samples using random number tables . 2. Point estimation of parameters and obtaining estimates of standard errors. 3. Comparison of estimators by plotting mean square error. 4. Computing maximum likelihood estimates 1 5. Computing maximum likelihood estimates  2 6. Computing moment estimates 7. Constructing confidence intervals based on large samples. 8. Constructing confidence intervals based on small samples. 9. Generating random samples from discrete distributions. 10. Generating random samples from continuous distributions. 11. Evaluation of probabilities of TypeI and TypeII errors and powers of tests. 12. MP test for parameters of binomial and Poisson distributions. 13. MP test for the mean of a normal distribution and power curve. 14. Tests for mean, equality of means when variance is (i) known, (ii) unknown under normality (small and large samples) 15. Tests for single proportion and equality of two proportions. 16. Tests for variance and equality of two variances under normality 17. Tests for correlation and regression coefficients. 18. Tests for the independence of attributes, analysis of categorical data and tests for the goodness of fit.(For uniform, binomial and Poisson distributions) 19. Nonparametric tests. 20. SPRT for binomial proportion and mean of a normal distribution..
Tentative Date: Venue: Classroom and Laboratory Laboratory: coding ( 2 hrs/week ) Submission Format: Program to be executed. Laboratory : Through observation and Record Assignment Learning Objectives: 1. To recognize and apply the sampling distribution in the given sample. 2. To understand and implement a problem logically. 3. To critically examine a problem and infer the correct inference. Assessment Strategies aligned to LO: 1. The usage of concepts to evaluate and solve a specific problem is assessed. Inference of the results to be accurately provided. The accuracy and the relevance of results yielded is assessed. Technology and Tools used: LMS to upload the screenshot of the result 1. Evaluation Rubrics
OBSERVATION : 25 marks
 
MDS173  PROGRAMMING FOR DATA SCIENCE IN PYTHON (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The objective of this course is to provide comprehensive knowledge of python programming paradigms required for Data Science. 

Learning Outcome 

CO1: Demonstrate the usage of builtin objects in Python CO2: Analyze the significance of python program development environment by working on real world examples CO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLib modules. 
Unit1 
Teaching Hours:17 
INTRODUCTION TO PYTHON


Structure of Python ProgramUnderlying mechanism of Module ExecutionBranching and LoopingProblem Solving Using Branches and LoopsFunctions  Lists and Mutability Problem Solving Using Lists and Functions Lab Exercises 1. Demonstrate usage of branching and looping statements 2. Demonstrate Recursive functions 3. Demonstrate Lists  
Unit2 
Teaching Hours:17 
SEQUENCE DATATYPES AND OBJECTORIENTED PROGRAMMING


Sequences, Mapping and Sets Dictionaries Classes: Classes and InstancesInheritanceExceptional HandlingIntroduction to Regular Expressions using “re” module. Lab Exercises 1. Demonstrate Tuples and Sets 2. Demonstrate Dictionaries 3. Demonstrate inheritance and exceptional handling 4. Demonstrate use of “re”.  
Unit3 
Teaching Hours:13 
USING NUMPY


Basics of NumPyComputation on NumPyAggregationsComputation on ArraysComparisons, Masks and Boolean ArraysFancy IndexingSorting ArraysStructured Data: NumPy’s Structured Array. Lab Exercises 1. Demonstrate Aggregation 2. Demonstrate Indexing and Sorting  
Unit4 
Teaching Hours:13 
DATA MANIPULATION WITH PANDAS I


Introduction to Pandas ObjectsData indexing and SelectionOperating on Data in PandasHandling Missing DataHierarchical Indexing  Combining Data Sets Lab Exercises 1. Demonstrate handling of missing data 2. Demonstrate hierarchical indexing  
Unit5 
Teaching Hours:17 
DATA MANIPULATION WITH PANDAS II


Aggregation and GroupingPivot TablesVectorized String Operations Working with Time SeriesHigh Performance Pandas and query() Lab Exercises 1. Demonstrate usage of Pivot table 2. Demonstrate use of and query()  
Unit6 
Teaching Hours:13 
VISUALIZATION AND MATPLOTLIB


Basic functions of matplotlibSimple Line Plot, Scatter PlotDensity and Contour PlotsHistograms, Binnings and DensityCustomizing Plot Legends, Colour BarsThreeDimensional Plotting in Matplotlib. Lab Exercises 1. Demonstrate Scatter Plot 2. Demonstrate 3D plotting  
Text Books And Reference Books: [1]. Jake VanderPlas ,Python Data Science Handbook  Essential Tools for Working with Data, O’Reily Media,Inc, 2016 [2]. Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications,2016  
Essential Reading / Recommended Reading [1]. Joel Grus ,Data Science from Scratch First Principles with Python, O’Reilly Media,2016 [2]. T.R.Padmanabhan, Programming with Python,Springer Publications,2016  
Evaluation Pattern CIA 100%  
MDS173L  PROGRAMMING FOR DATA SCIENCE IN PYTHON (2019 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The objective of this course is to provide knowledge of python programming paradigms required for Data Science. 

Learning Outcome 

CO1: Understand and demonstrate the usage of builtin objects in Python CO2:Analyze the significance of python program development environment and apply it to solve real world applications CO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLib modules. 
Unit1 
Teaching Hours:17 
INTRODUCTION TO PYTHON


Structure of Python ProgramUnderlying mechanism of Module ExecutionBranching and LoopingProblem Solving Using Branches and LoopsFunctions  Lists and Mutability Problem Solving Using Lists and Functions  
Unit2 
Teaching Hours:17 
SEQUENCE DATATYPES AND OBJECTORIENTED PROGRAMMING


Sequences, Mapping and Sets Dictionaries Classes: Classes and InstancesInheritance Exceptional HandlingIntroduction to Regular Expressions using “re” module.  
Unit3 
Teaching Hours:13 
USING NUMPY


Basics of NumPyComputation on NumPyAggregationsComputation on Arrays Comparisons, Masks and Boolean ArraysFancy IndexingSorting ArraysStructured Data: NumPy’s Structured Array.  
Unit4 
Teaching Hours:13 
DATA MANIPULATION WITH PANDAS I

