Syllabus for
Master of Science (Data Science)
Academic Year  (2021)

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.

CIA - 50%

ESE - 50%

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.

Vector Spaces: Rⁿ and Cⁿ, lists, Fⁿ and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.

DefinitionofLinearMaps-AlgebraicOperationson L(V,W) - Null spaces and Injectivity-RangeandSurjectivity-FundamentalTheoremsofLinearMaps-Representing aLinearMapbyaMatrix-InvertibleLinearMaps-IsomorphicVectorspaces-LinearMap as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces.

Eigenvalues and Eigenvectors - Eigenvectors and Upper Triangular matrices - Eigenspaces and Diagonal Matrices - Inner Products and Norms - Linear functionals on Inner Product spaces.

 Matrix Norms – Least square problem - Singular value decomposition- Householder Transformation and QR decomposition- Non Negative Matrix Factorization – bidiagonalization.

Handwritten digits recognition using simple algorithm - Classification of handwritten digits using SVD bases and Tangent distance - Text Mining using Latent semantic index, Clustering, Non-negative Matrix Factorization and LGK bidiagonalization.

2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.

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.

ESE - 50%

Probability and probability distributions play an essential role in modeling data from the real-world phenomenon. This course will equip students with thorough knowledge in probability and various probability distributions and model real-life data sets with an appropriate probability distribution

Data – types of variables: numeric vs categorical - measures of central tendency – measures of dispersion - random experiment - sample space and random events – probability - probability axioms - finite sample space with equally likely outcomes - conditional probability - independent events - Baye’s theorem

Random variable – data as observed values of a random variable - expectation – moments & moment generating function - mean and variance in terms of moments - discrete sample space and discrete random variable – Bernoulli experiment and Binary variable: Bernoulli and binomial distributions – Count data: Poisson distribution – overdispersion in count data: negative binomial distribution – dependent Bernoulli  trails: hypergeometric distribution.

Continuous sample space - Interval data - continuous random variable – uniform distribution - normal distribution (Gaussian distribution) – modeling lifetime data: exponential distribution, gamma distribution, Weibull distribution.

Joint distribution of vector random variables – joint moments – covariance – correlation - the correlation - independent random variables - conditional distribution – conditional expectation - sampling distributions: chi-square, t, F (central).

Chebychev’s inequality - weak law of large n u mbers (iid): examples - strong law of large numbers (statement only) - central limit theorems (iid case): examples.

2. An Introduction to Probability and Statistics, V.K Rohatgi and Saleh, 3rd Edition, 2015

2. Ross, Sheldon M. Introduction to probability models. 12th Edition, Academic Press, 2019. 

ESE: 50%

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

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.

Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms.

Introduction – Deep Feedforward Networks – Regularization – Optimization of Deep Learning – Convolutional Networks – Recurrent and Recursive Nets – Applications of Deep Learning.

Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools – Creating an interactive dashboard with dc.js-summary.

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.

[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

[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

ESE : 50 %

This course is intended to assist students in planning and carrying out research work.The students are exposed to the basic principles, procedures and techniques of implementing a research project.

To introduce the research concept and the various research methodologies is the main objective. It focuses on finding out the research gap from the literature and encourages lateral, strategic and creative thinking. This course also introduces computer technology and basic statistics required for research and reporting the research outcomes scientifically emphasizing on research ethics.

Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research.

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,On-line Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,H-index.

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation. 

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. 

[2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005. 

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010. 

ESE - 50%

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

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

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.

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.

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

[2]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.

[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.

ESE - 50%

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

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

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

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.

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.

[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, 9th Edition, 2012

ESE: 50%

To Enable the students to excel in the Linux Platform

RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, command-line environments, and documentation - Configure local storage using partitions and logical volumes

Swapping, Extend LVM Partitions,LVM Snapshot - Manage users and groups, including use of a centralized directory for authentication

Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab - Configure firewall settings using firewall config, firewall-cmd, or iptables , Configure key-based authentication for SSH ,Set enforcing and permissive modes for SELinux , List and identify SELinux file and process context ,Restore default file contexts

2.    https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/

ESE:50%

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 relational databases, writing effective queries comprehend data warehouse and NoSQL databases and its types

 Concept & Overview of DBMS, Data Models, Database Languages, Database Administrator, Database Users, Three Schema architecture of DBMS. Basic concepts, Design Issues, Mapping Constraints, Keys, Entity-Relationship Diagram, Weak Entity Sets, Extended E-R features

 Lab Exercises

1. Data Definition,

2. Table Creation

3. Constraints

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, Boyce-Codd Normal Form, 4NF

 Lab Exercises

1. Insert, Select, Update & Delete Commands

2. Nested Queries & Join Queries

3. Views

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

 Lab Exercises:

1. Importing source data structures

2. Design Target Data Structures

3. Create target multidimensional cube

Requirements, ETL Data Structures, Extracting, Cleaning and Conforming, Delivering Dimension Tables, Delivering Fact Tables, Real-Time ETL Systems

 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

Introduction to NOSQL Systems, The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems, Graph databases, Multimedia databases.

 Lab Exercises:

1. MongoDB Exercise - 1

2. MongoDB Exercise - 2

[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

ESE: 50%

Statistical inference plays an important role in modeling data and decision-making from the real-world phenomenon. This course is designed to impart the knowledge of testing of hypothesis and estimation of parameters for real-life data sets.

Population and Statistics – Finite and Infinite population – Parameter and Statistics – Types of sampling - Sampling Distribution – Sampling Error - Standard Error – Test of significance –concept of hypothesis – types of hypothesis – Errors in hypothesis-testing – Critical region – level of significance - Power of the test – p-value.

Lab Exercise:

1. Calculation of sampling error and standard error

2. Calculation of probability of critical region using standard distributions

3. Calculation of power of the test using standard distributions.

Concept of large and small samples – Tests concerning a single population mean for known σ – equality of two means for known σ – Test for Single variance - Test for equality of two variance for normal population – Tests for single proportion – Tests of equality of two proportions for the normal population.

Lab Exercise:

4. Test of the single sample mean for known σ.

5. Test of equality of two means when known σ

6. Tests of single variance and equality of variance for large samples

7. Tests for single proportion and equality of two proportion for large samples.

Students t-distribution and its properties (without proofs) – Single sample mean test – Independent sample mean test – Paired sample mean test – Tests of proportion (based on t distribution) – F distribution and its properties (without proofs) – Tests of equality of two variances using F-test – Chi-square distribution and its properties (without proofs) – chisquare test for independence of attributes – Chi-square test for goodness of fit.

Lab Exercise:

8. Single sample mean test

9. Independent and Paired sample mean test

10. Tests of proportion of one and two samples based on t-distribution

11. Test of equality of two variances

12. Chi-square test for independence of attributes and goodness of fit.

Meaning and assumptions - Fixed, random and mixed effect models - Analysis of variance of one-way and two-way classified data with and without interaction effects – Multiple comparison tests: Tukey’s method - critical difference.

Lab Exercise:

13. Construction of one-way ANOVA

14. Construction of two-way ANOVA with interaction

15. Construction of two-way ANOVA without interaction

16. Multiple comparision test using Tukey’s method and critical difference methods

Concept of Nonparametric tests - Run test for randomness - Sign test and Wilcoxon Signed Rank Test for one and paired samples - Run test - Median test and Mann-Whitney-Wilcoxon tests for two samples.

Lab Exercise:

17. Test of one sample using Run and sign tests

18. Test of paried sample using Wilcoxon signed rank test

19. Test of two samples using Run test and Median test

20. Test for two samples using Mann-Whitney-Wilcoxon tests

2. Brian Caffo, Statistical Inference for Data Science, Learnpub, 2016. 

2. John V, Using R for Introductory Statistics, 2nd edition, CRC Press, Boca Raton, 2014.

3. Rajagopalan M and Dhanavanthan P, Statistical Inference, PHI Learning (P) Ltd, New Delhi, 2012.

4. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, JohnWiley & Sons Inc, New Jersey, 2015.

ESE:50%

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

Structure of Python Program-Underlying mechanism of Module Execution-Branching and Looping-Problem Solving Using Branches and Loops-Functions - Lists and Mutability- Problem Solving Using Lists and Functions

Lab Exercises

1.      Demonstrate usage of branching and loopingstatements

2.      Demonstrate Recursivefunctions

3.      DemonstrateLists

Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-Inheritance- Exceptional Handling-Introduction to Regular Expressions using “re” module.

Lab Exercises

1.      Demonstrate Tuples andSets

2.      DemonstrateDictionaries

3.      Demonstrate inheritance and exceptionalhandling

4.      Demonstrate use of“re”

Basics of NumPy-Computation on NumPy-Aggregations-Computation on Arrays- Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-Structured Data: NumPy’s Structured Array.

Lab Exercises

1.      DemonstrateAggregation

2.      Demonstrate Indexing andSorting

Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas- Handling Missing Data-Hierarchical Indexing - Combining Data Sets

Lab Exercises

1.      Demonstrate handling of missingdata

2.      Demonstrate hierarchicalindexing

Aggregation and Grouping-Pivot Tables-Vectorized String Operations -Working with Time Series-High Performance Pandas- and query()

Lab Exercises

1.      Demonstrate usage of Pivottable

2.      Demonstrate use of andquery()

Basic functions of matplotlib-Simple Line Plot, Scatter Plot-Density and Contour Plots- Histograms, Binnings and Density-Customizing Plot Legends, Colour Bars-Three- Dimensional Plotting in Matplotlib.

Lab Exercises

1.      DemonstrateScatterPlot

2.      Demonstrate3Dplotting

[2].   Zhang.Y   ,An   Introduction   to    Python   and   Computer   Programming,   Springer Publications,2016

[2]. T.R.Padmanabhan, Programming with Python,SpringerPublications,2016

ESE: 50%

This course aims at introducing data science related essential mathematics concepts such as fundamentals of topics on Calculus of several variables, Orthogonality, Convex optimization and Graph Theory.

Functions of Several Variables: Functions of two, three variables - Limits and continuity in HIgher Dimensions: Limits for functions of two variables, Functions of more than two variables - Partial Derivatives: partial derivative of functions of two variables, partial derivatives of functions of more than two variables, partial derivatives and continuity, second order partial derivatives - The Chain Rule: chain rule on functions of two, three variables, chain rule on functions defined on surfaces - Directional Derivative and Gradient vectors: Directional derivatives in a plane, Interpretation of directional derivative, calculation and gradients, Gradients and tangents to level curves.

Perpendicular vectors and Orthogonality - Inner Products and Projections onto lines - Projections of Rank one - Projections and Least Squares Approximations - Projection Matrices - Orthogonal Bases, Orthogonal Matrices and Gram-Schmidt orthogonalization

Affine and Convex Sets: Lines and Line segments, affine sets, affine dimension andrelative interior, convexsets, cones - Hyperplanes and half-spaces - Euclidean balls and ellipsoids- Norm balls and Norm cones - polyhedra - simplexes, Convex hull description of polyhedra - The positive semidefinitecone.

Graph Classes: Definition of a Graph and Graph terminology, isomorphism of graphs, Completegraphs, bipartite graphs, complete bipartite graphs-Vertex degree: adjacency and incidence, regular graphs - subgraphs, spanning subgraphs, induced subgraphs, removing or adding edges of a graph, removing vertices from graphs - Graph Operations: Graph Union, intersection, complement, self complement, Paths and Cycles, Connected graphs, Eulerian and HamiltonianGraphs.

Matrix Representation of Graphs, Adjacency matrices, Incidence Matrices, Trees and its properties, Bridges (cut-edges), spanning trees, weighted Graphs, minimal spanning tree problems, Shortest path problems, cut vertices, cuts, vertex and edge connectivity,  Graph Algorithms - Applications of Graph Theory

2.     G Strang, Linear Algebra and its Applications, 4th ed., Cengage, 2006. (Unit 2)

3.     S. P. Boyd and L.Vandenberghe, Convex optimization.Cambridge Univ. Pr., 2011.(Unit 3)

4.     J Clark, D A Holton, A first look at Graph Theory, Allied Publishers India, 1995. (Unit 4)

2.S. Sra, S. Nowozin, and S. J. Wright, Optimization for machine learning. MIT Press, 2012.

3.D. Jungnickel, Graphs, networks and algorithms. Springer, 2014.

4.D Samovici, Mathematical Analysis for Machine Learning and Data Mining, World Scientific Publishing Co. Pte. Ltd, 2018

5.P. N. Klein, Coding the matrix: linear algebra through applications to computer science. Newtonian Press, 2015.

6.K H Rosen, Discrete Mathematics and its applications, 7th ed., McGraw Hill, 2016

ESE :50%

This course aims to provide the grounding knowledge about the regression model building of simple and multiple regression.

Introduction to regression analysis: Modelling a response, overview and applications of regression analysis, major steps in regression analysis. Simple linear regression (Two variables): assumptions, estimation and properties of regression coefficients, significance and confidence intervals of regression coefficients, measuring the quality of the fit.

Multiple linear regression model: assumptions, ordinary least square estimation of regression coefficients, interpretation and properties of regression coefficient, significance and confidence intervals of regression coefficients.

Mean Square error criteria, R2 and  criteria for model selection; Need of the transformation of variables; Box-Cox transformation; Forward, Backward and Stepwise procedures.

Residual analysis, Departures from underlying assumptions, Effect of outliers, Collinearity, Non-constant variance and serial correlation, Departures from normality, Diagnostics and remedies.

Introduction to nonlinear regression, Least squares in the nonlinear case and estimation of parameters, Models for binary response variables, estimation and diagnosis methods for logistic and Poisson regressions. Prediction and residual analysis.

[2]. S. Chatterjee and AHadi, Regression Analysis by Example, 4^th Ed., John Wiley and Sons, Inc, 2006

[3].Seber, A.F. and Lee, A.J. (2003) Linear Regression Analysis, John Wiley, Relevant sections from chapters 3, 4, 5, 6, 7, 9, 10.

[2]. P. McCullagh, J.A. Nelder, Generalized Linear Models, Chapman & Hall, 1989.

ESE - 50%

 This course lays the foundation of Multivariate data analysis. The exposure provided to multivariate data structure, multinomial and multivariate normal distribution, estimation and testing of parameters, various data reduction methods would help the students in having a better understanding of research data, its presentation and analysis.

Basic concepts on multivariate variable. Multivariate normal distribution, Marginal and conditional distribution, Concept of random vector: Its expectation and Variance-Covariance matrix. Marginal and joint distributions. Conditional distributions and Independence of random vectors. Multinomial distribution. Sample mean vector and its distribution.

Sample mean vector and its distribution. Likelihood ratio tests: Tests of hypotheses about the mean vectors and covariance matrices for multivariate normal populations. Independence of sub vectors and sphericity test.

Multivariate analysis of variance (MANOVA) of one and two- way classified data. Multivariate analysis of covariance.  Wishart distribution, Hotelling’s T2 and Mahalanobis’ D2 statistics, Null distribution of Hotelling’s T2. Rao’s U statistics and its distribution.

Bayes, minimax, and Fisher’s criteria for discrimination between two multivariate normal populations. Sample discriminant function. Tests associated with discriminant functions. Probabilities of misclassification and their estimation. Discrimination for several multivariate normal populations

Principal components, sample principal components asymptotic properties. Canonical variables and canonical correlations: definition, estimation, computations. Test for significance of canonical correlations.

Factor analysis: Orthogonal factor model, factor loadings, estimation of factor loadings, factor scores.  Applications

[2]. Everitt B, Hothorn T, 2011. An Introduction to Applied Multivariate Analysis with R, Springer.

[3]. Barry J. Babin, Hair, Rolph E Anderson, and William C. Blac, 2013,  Multivariate Data Analysis, Pearson New International Edition, 

[2] Chatfield, C. and Collins, A.J. 1982. Introduction to Multivariate analysis. Prentice Hall

[3] Srivastava, M.S. and Khatri, C.G. 1979. An Introduction to Multivariate Statistics. North Holland

ESE - 50%

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 non-parametric tests and its applications.

Classification of Stochastic Processes, Markov Processes – Markov Chain - Countable State Markov Chain. Transition Probabilities, Transition Probability Matrix. Chapman - Kolmogorov's Equations, Calculation of n - step Transition Probability and its limit.

Classification of States, Recurrent and Transient States - Transient Markov Chain, Random Walk and Gambler's Ruin Problem. Continuous Time Markov Process:, Poisson Processes, Birth and Death Processes, Kolmogorov’s Differential Equations, Applications.

Branching Processes – Galton – Watson Branching Process - Properties of Generating Functions – Extinction Probabilities – Distribution of Total Number of Progeny. Concept of Weiner Process.

Renewal Processes – Renewal Process in Discrete and Continuous Time – Renewal Interval – Renewal Function and Renewal Density – Renewal Equation – Renewal theorems: Elementary Renewal Theorem. Probability Generating Function of Renewal Processes.

Stationary Processes: Discrete Parameter Stochastic Process – Application to Time Series. Auto-covariance and Auto-correlation functions and their properties. Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average Processes. Basic ideas of residual analysis, diagnostic checking, forecasting.

[2]. Stochastic Processes, S.M Ross, Wiley India Pvt. Ltd, 2008.

[2]. Introduction to Probability and Stochastic Processes with Applications, B..C. Liliana, A Viswanathan, S. Dharmaraja, Wiley Pvt. Ltd, 2012.

ESE - 50%

Categorical data analysis deals with the study of information captured through expressions or verbal forms. This course equips the students with the theory and methods to analyse and categorical responses.

Categorical response data - Probability distributions for categorical data - Statistical inference for discrete data

Probability structure for contingency tables - Comparing proportions with 2x2 tables - The odds ratio - Tests for independence - Exact inference - Extension to three-way and larger tables

Components of a generalized linear model - GLM for binary and count data - Statistical inference and model checking - Fitting GLMs

Interpreting the logistic regression model - Inference for logistic regression - Logistic regression with categorical predictors - Multiple logistic regression - Summarising effects - Building and applying logistic regression models - Multicategory logit models

Loglinear models for two-way and three-way tables - Inference for Loglinear models - the log-linear-logistic connection - Independence graphs and collapsibility - Models for matched pairs: Comparing dependent proportions, Logistic regression for matched pairs - Comparing margins of square contingency tables - symmetry issues

 2. Agresti, A. (2010). Analysis of ordinal categorical. John Wiley & Sons.

 3. Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using SAS. SAS Institute.

 4. Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.

 5. Bilder, C. R., & Loughin, T. M. (2014). Analysis of categorical data with R. Chapman and Hall/CRC.

ESE:50%

Theobjectiveofthiscourseistoprovideintroductiontotheprinciplesanddesignofmachine learning algorithms. The course is aimed at providing foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems.

MachineLearning-ExamplesofMachineApplications-LearningAssociations-Classification- Regression-UnsupervisedLearning-Reinforcement Learning.Supervised Learning: Learning class from examples- Probably Approach Correct(PAC) Learning-Noise-Learning Multiple classes. Regression-Model Selection and Generalization.

IntroductiontoParametricmethods-MaximumLikelihood Estimation:Bernoulli Density- Multinomial Density-Gaussian Density, Nonparametric Density Estimation: Histogram Estimator-Kernel Estimator-K-Nearest NeighbourEstimator.

 Lab Exercise:

1.      Data Exploration using parametric methods

2.      Data Exploration using non-parametric methods

3.      Regression analysis

Dimensionality Reduction: Introduction- Subset Selection-Principal Component Analysis, Feature Embedding-Factor Analysis-Singular Value Decomposition-Multidimensional Scaling-Linear Discriminant Analysis- Bayesian Decision Theory.

Lab Exercise:

1.      Data reduction using Principal ComponentAnalysis

2.      Data reduction using multi-dimensional scaling

Multilayer Perceptron:

Introduction, training a perceptron- learning Boolean functions- multilayer perceptron- backpropogation algorithm- training procedures.

Combining Multiple Learners

Rationale-Generating diverse learners- Model combination schemes- voting, Bagging- Boosting- fine tuning an Ensemble.

Lab Exercise

1.  Classification using MLP

2.  Ensemble Learning

Clustering

Introduction-Mixture Densities, K-Means Clustering- Expectation-Maximization algorithm- Mixtures of Latent Varaible Models-Supervised Learning after Clustering-Spectral Clustering- Hierachial Clustering-Clustering- Choosing the number of Clusters.

Lab Exercise

1.  K means clustering

2.  Hierarchical clustering

ESE: 50%

The subject is intended to give the knowledge of Big Data evolving in every real-time applications and how they are manipulated using the emerging technologies. This course breaks down the walls of complexity in processing Big Data by providing a practical approach to developing Java applications on top of the Hadoop platform. It describes the Hadoop architecture and how to work with the Hadoop Distributed File System (HDFS) and HBase in Ubuntu platform.

Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications, Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce.

Apache Hadoop– Moving Data in and out of Hadoop – Understanding inputs and outputs ofMapReduce - Data Serialization, Problems with traditional large-scale systems-Requirements for a new approach-Hadoop – Scaling-Distributed Framework-Hadoop v/s RDBMS-Brief history of Hadoop.

Lab Exercise

1. Installing and Configuring Hadoop

Hadoop Processes (NN, SNN, JT, DN, TT)-Temporary directory – UI-Common errors when running Hadoop cluster, solutions.

Setting up Hadoop on a local Ubuntu host: Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudo-distributed mode, HDFS directory, NameNode, Examples of MapReduce, Using Elastic MapReduce, Comparison of local versus EMR Hadoop.

Understanding MapReduce:Key/value pairs,TheHadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output.

Developing MapReduce Programs: Using languages other than Java with Hadoop, Analysing a large dataset.

Lab Exercise

1.      1. Word count application in Hadoop.

2.      2. Sorting the data using MapReduce.

3.      3. Finding max and min value in Hadoop.

Simple, advanced, and in-between Joins, Graph algorithms, using language-independent data structures.

Hadoop configuration properties - Setting up a cluster, Cluster access control, managing the NameNode, Managing HDFS, MapReduce management, Scaling.

Lab Exercise: 

1. Implementation of decision tree algorithms using MapReduce.

 2. Implementation of K-means Clustering using MapReduce.

3. Generation of  Frequent Itemset using MapReduce. 

Hadoop Streaming  -   Streaming  Command  Options - Specifying  a  Java  Class  as  the  Mapper/Reducer - Packaging Files With Job Submissions - Specifying Other Plug-ins for Jobs.

Lab Exercise: 

1.      1. Count the number of missing and invalid values through joining two large given datasets.

2.      2. Using hadoop’s map-reduce, Evaluating Number of Products Sold in Each Country in the online shopping portal. Dataset is given.

3.      3. Analyze the sentiment for product reviews, this work proposes a MapReduce technique provided by Apache Hadoop.

Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Pig, Use case of Pig, Pig Components, Data Model, Pig Latin.

Lab Exercise

1. Trend Analysis based on Access Pattern over Web Logs using Hadoop.

2. Service Rating Prediction by Exploring Social Mobile Users Geographical Locations.

RDBMS VsNoSQL, HBasics, Installation, Building an online query application – Schema design, Loading Data, Online Queries, Successful service.

Hands On: Single Node Hadoop Cluster Set up in any cloud service provider- How to create instance.How to connect that Instance Using putty.InstallingHadoop framework on this instance. Run sample programs which come with Hadoop framework.

Lab Exercise:

1.      1. Big Data Analytics Framework Based Simulated Performance and Operational Efficiencies Through Billons of Patient Records in Hospital System.

[2] Tom White, Hadoop: The Definitive Guide, O’Reilly Media Inc., 2015.

[3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.

[2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop Real-World Solutions Cookbook, Packt Publishing, 2013.

[3] Tom White, HADOOP: The definitive Guide, O Reilly, 2012.

ESE - 50%

This course will provide a basic foundation towards digital image processing and video analysis. This course will also provide brief introduction about various Object Detection, Recognition, Segmentation and Compression methods which will help the students to demonstrate real-time image and video analytics applications.

Digital image representation, Sampling and Quantization, Types of Images, Basic Relations between Pixels - Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations, Introduction to Digital Video, Sampled Video, Video Transmission.

Gray-Level Processing: Image Histogram, Linear and Non-linear point operations on Images, Arithmetic Operations between Images, Geometric Image Operations, Image Thresholding, Region labeling, Binary Image Morphology.

Lab Programs:

1.  Program to perform Resize, Rotation of binary, Gray-scale and color images using various methods.

2. Program to implement contrast stretching.

Spatial domain-Linear and Non-linear Filtering, Introduction to Fourier Transform and the frequency Domain– Filtering in Frequency domain, Homomorphic Filtering, Brief introduction towards Wavelets, Wavelet based image denoising, A model of The Image Degradation / Restoration, Noise Models and basic methods for image restoration. Blotch detection and Removal.

Lab Programs:

3.  Program to implement various image enhancement techniques using Built-in and user defined functions.

4. Program to implement Non-linear Spatial Filtering using Built-in and userdefined functions.

Image Compression: Huffman Coding, Run length Coding, LZW Coding, Basics of Wavelets based image compression.

Video Compression: Basic Concepts and Techniques of Video compression, MPEG-1 and MPEG-2 Video Standards.

Lab Programs: 

5.     Program to implement homomorphic Filtering

6.     Extraction of frames from videos and analyzing frames

Introduction to feature detectors, descriptors, matching and tracking, Basic edge detectors – canny, sobel, prewitt etc., Image Segmentation - Region Based Segmentation – Region Growing and Region Splitting and Merging, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.

Lab Programs:

7.     Implement multi-resolution image decomposition and reconstruction using wavelet. 

8.     Implement image compression using wavelets.

Descriptors: Boundary descriptors - Fourier descriptors - Regional descriptors - Topological descriptors - moment invariants

Object detection and recognition in image and video: Minimum distance classifier, K-NN classifier and Bayes, Applications in image and video analysis, object tracking in videos.

 Lab Programs:

9.   Extracting feature descriptors from the image dataset.

10. Implement image classification using extracted relevant features.

 [2] Alan Bovik, Handbook of Image and Video Processing, Second Edition, Academic Press, 2005.

[2] RichardSzeliski,ComputerVision–AlgorithmsandApplications,Springer,2011.

[3] Oge Marques, Practical Image and Video Processing Using MatLab, Wiley, 2011.

[4] John W. Woods, Multidimensional Signal, Image, Video Processing and Coding, Academic Press, 2006.

ESE: 50%

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home. Wireless Sensor Networks form the basis of the Internet of Things. To latch on to the applications in the field of IoT of the recent times, this course provides a deeper understanding of the underlying concepts of IoT and Wireless Sensor Networks.

1.   1. Introduction to ICs and Sensors. A basic program can be shown which makes use of logic gates IC s for understanding the basics of sensor nodes. Different sensors which find application in IoT projects can be shown,their working explained.

2.    2.Introduction to Arduino/Raspberry Pi. Sample sketches or code can be selected from theArduinosoftwareandexecuted,making use of different sensors.

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introductiontomeasurethephysicalquantities,IoTEnablingTechnologies-WirelessSensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and DeploymentTemplates.

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Raspberry Pi,Arduino)andIDE-CaseStudy:WeatherMonitoring-LogicalDesignusingPython, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest forIoT.

3. Use of sensors to detect the temperature/humidity in a room and having appropriate actions performed such as changing the LED color and turning the speaker on as an alarm and using serial monitor to see these values.

4. A basic parking system making use of multiple IR sensors, Ultrasonic Sensors, LED bulbs, Speakers etc, to identify if a slot is empty or full and using the LED and speakers to alert the user about the availability.

Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture, Industry- Health and Lifestyle- IoT and M2M.

5. An Agricultural System (Greenhouse System) that makes use of sensors like humidity, temperature etc, to identify the current situation of the agricultural area and taking necessary measures such as activating the water spraying motor, the alarm system (to indicate if there is excess heat) etc.

6. Create a basic sound system by making use of knobs, speakers, LED bulbs etc., to mimic the sound produced by a race car, ambulance, siren etc.

7. A basic obstacle avoiding robot by making use of Ultrasonic sensors, dc motors, and the chassis kit for robotic car.

SensingandSensors-WirelessSensorNetworks,ChallengesandConstraints-Applications: Structural Health Monitoring, Traffic Control, Health Care - Node Architecture - Operating system.

8. Making use of GSM for communication in the obstacle avoiding robot. Using sensors such as flame sensors, PIR human motion sensor, IR sensor, LED bulbs etc for better inputs regarding the environment.

9. A garbage level indicator which makes use of IR proximity sensors, WiFi modules etc to detect the rising amount of garbage and sending data to a server and channelling that data to the owner of the module. Can be introduced as the application IoT. If needed, IoT introduction can be done much earlier and the sharing of data can be shown, for better functionality of later projects.

10. Elderly care: We want to monitor very senior citizens whether they had a sudden fall. If a very senior citizen falls suddenly while walking, due to stroke or slippery ground etc, a notification should be sent out so that he/she can get immediate medical attention. shown, for better functionality of later projects. 

Introduction – Fundamentals of MAC Protocols – MAC protocols for WSN – Sensor MAC CaseStudy–RoutingChallengesandDesignIssues–RoutingStrategies–TransportControl Protocols–TransportProtocolDesignIssues–PerformanceofTransportProtocols

11. Smart street lights: The street lights should increase or decrease their intensity based on the actual requirements of the amount of light needed at that time of the day. This will save a lot of energy for the municipal corporation.

12. Implement 3-bit Binary Counter using 3 LED Module. 

a. Glow RED if the Binary bit is '0'. Glow GREEN if the binary bit is '1'

i. For example:

ii. 000 = 0 (all LED should be RED)

iii. 001 = 1 (Two LEDs Should be RED , and one LED should be GREEN)

iv. If Button is pressed in between, Reset the counter and Re-start from 0.

Theft prevention system for night: When the room is dark and Board is moved or tilted (say around 90 degree), it should alarm.

[2]   Kazem Sohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010.

[3]    Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010.

[2]   Michael Miller, The Internet of Things, Pearson Education, 2015.

[3]    Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005.

[4]   Erdal Çayırcı and Chunming Rong, SecurityinWirelessAdHocandSensorNetworks,John Wiley and Sons, 2009.

[5]   Carlos De MoraisCordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011.

[6]   Adrian Perrig and J.D.Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.

ESE - 50%

This lab is designed to introduce implementation of practical machine learning algorithms using R programming language. The lab will extensively use datasets from real life situations.

Approaches to missing data – Data imputation – Multiple imputation – Classification and Regression Tress (CART) – CART with Cross Validation – Predictions from CART – ROC curve for CART – Random Forests – Building many trees – Parameter selection – K-fold Cross Validation – Recitation – A minimum of 3 data sets for practice

Support Vector Machines – Gradient Boosting – Naive Bayes - Bayesian GLM – GLMNET - Ensemble modeling – Experimenting with all of the above approaches (Units 1-5) with and without data imputation and assessing predictive accuracy – Recitation – min 3 data sets for practice PROJECT – A concluding project work carried out individually for a common data set

[2]. R for everyone, Jared Lander, Pearson, 1^st Edition, 2014

ESE - 50%

The main aim of this course is to provide fundamental knowledge of neural networks and deep learning. On successful completion of the course, students will acquire fundamental knowledge of neural networks and deep learning, such as Basics of neural networks, shallow neural networks, deep neural networks, forward & backward propagation process and build various research projects

Neural Networks-Application Scope of Neural Networks- Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN. 

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning RuleArchitecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.

Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.

Introduction - Components of CNN Architecture - Rectified Linear Unit (ReLU) Layer - Exponential Linear Unit (ELU, or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN. 

Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN.

Introduction - Features of Auto encoder Types of Autoencoder Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM.

2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019. 

2. Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017

3. John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019. 

ESE: 50%

This course covers applied statistical methods pertaining to time series and forecasting techniques. Moving average models like simple, weighted and exponential are dealt with. Stationary time series models and non-stationary time series models like AR, MA, ARMA and ARIMA are introduced to analyse time series data.

Introduction to time series and stochastic process, graphical representation, components and classical decomposition of time series data.Auto-covariance and auto-correlation functions, Exploratory time series analysis, Test for trend and seasonality, Smoothing techniques such as Exponential and moving average smoothing, Holt- Winter smoothing, Forecasting based on smoothing.

Wold representation of linear stationary processes, Study of linear time series models: Autoregressive, Moving Average and Autoregressive Moving average models and their statistical properties like ACF and PACF function.

Estimation of ARMA models: Yule- Walker estimation of AR Processes, Maximum likelihood and least squares estimation for ARMA Processes, Residual analysis and diagnostic checking.

Concept of non-stationarity, general unit root tests for testing non-stationarity; basic formulation of the ARIMA Model and their statistical properties-ACF and PACF; forecasting using ARIMA models

Filtering, smoothing and forecasting using state space models, Kalman smoother, Maximum likelihood estimation, Missing data modifications

2. Montgomery D.C, Jennigs C. L and Kulachi M,Introduction to Time Series analysis  and Forecasting, 2nd Edition,John Wiley & Sons, Inc., New Jersey, 2016.

2.      Shumway R.H and Stoffer D.S, Time Series Analysis and its Applications with R Examples, Springer, 2011. 

3.      P. J. Brockwell and R. A. Davis, Times series: Theory and Methods, 2nd Edition, Springer-Verlag, 2009. 

4.      S.C. Gupta and V.K. Kapoor, Fundamentals of Applied Statistics, 4th Edition, Sultan Chand and Sons, 2008.

ESE: 50%

To equip the students with the knowledge of conceptual, computational, and practical methods of Bayesian data analysis.

Basics on minimaxity: subjective and frequents probability, Bayesian inference, Bayesian estimation , prior distributions, posterior distribution, loss function, principle of minimum expected posterior loss, quadratic and other common loss functions, Advantages of being a Bayesian HPD confidence intervals, testing, credible intervals, prediction of a future observation.

Robustness and sensitivity, classes of priors, conjugate class, neighbourhood class, density ratio class different methods of objective priors: Jeffrey’s prior, probability matching prior, conjugate priors and mixtures, posterior robustness: measures and techniques

Basics of decision theory, multi-parameter models, Multivariate models, linear regression, asymptotic approximation to posterior distributions

Selection criteria and testing of hypothesis based on objective probabilities and Bayes’ factors, large sample methods: limit of posterior distribution, consistency of posterior distribution, asymptotic normality of posterior distribution.

Analytic approximation, E- M Algorithm, Monte Carlo sampling, Markov Chain Monte Carlo Methods, Metropolis – Hastings Algorithm, Gibbs sampling, examples, convergence issues

2. Bolstad W. M. and Curran, J.M. (2016) Introduction to Bayesian Statistics 3rd Ed. Wiley, New York

3. Christensen R. Johnson, W. Branscum A. and Hanson T.E. (2011) Bayesian Ideas and data analysis : A introduction for scientist and Statisticians, Chapman and Hall, London 

4. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin (2004). Bayesian Data Analysis, 2nd Ed. Chapman & Hall

2. Ghosh, J.K. Delampady M. and T. Samantha (2006). An Introduction to Bayesian Analysis: Theory and Methods, Springer, New York.

3. Lee P.M. (2012) Bayesian Statistics: An Introduction-4th Ed. Hodder Arnold, New York.

4. Rao C.R. Day D. (2006) Bayesian Thinking, Modeling and Computation, Handbook of Statistics, Vol.25.

ESE: 50%

The course is designed to impart the learning of principles of econometric methods and tools. This is expected to improve student’s ability to understand of econometrics in the study of economics and finance. The learning objective of the course is to provide students to get the basic knowledge and skills of econometric analysis, so that they should be able to apply it to the investigation of economic relationships and processes, and also understand the econometric methods, approaches, ideas, results and conclusions met in the majority of economic books and articles. Introduce the students to the traditional econometric methods developed mostly for the work with cross-sections data.

Introduction to Econometrics- Meaning and Scope – Methodology of Econometrics – Nature and Sources of Data for Econometric analysis – Types of Econometrics

Aitken’s Generalised Least Squares(GLS) Estimator, Heteroscedasticity, Auto-correlation, Multicollinearity, Auto-Correlation, Test of Auto-correlation, Multicollinearity, Tools for Handling Multicollinearity

Linear Regression with Stochastic Regressors, Errors in Variable Models and Instrumental Variable Estimation, Independent Stochastic linear Regression, Auto regression, Linear regression, Lag Models

Simultaneous Linear Equations Model : Structure of Linear Equations Model, Identification Problem, Rank and Order Conditions, Single Equation and Simultaneous Equations, Methods of Estimation- Indirect Least squares, Least Variance Ratio and Two-Stage Least Square

2.  Gujarathi, D., and Porter, D. (2008). Basic Econometrics, Fifth Edition, McGraw-Hill

2.  Theil, H. (1971). Principles of Econometrics, John Wiley.

3.  Walters, A. (1970). An Introduction to Econometrics, McMillan and Co.

ESE : 50%

This course provides an understanding of various statistical methods in describing and analyzing biological data. Students will be equipped with an idea about the applications of statistical hypothesis testing, related concepts and interpretation in biological data.

Presentation of data - graphical and numerical representations of data - Types of variables, measures of location - dispersion and correlation - inferential statistics - probability and distributions - Binomial, Poisson, Negative Binomial, Hyper geometric and normal distribution.

Parametric methods - one sample t-test - independent sample t-test - paired sample t-test - one-way analysis of variance - two-way analysis of variance - analysis of covariance - repeated measures of analysis of variance - Pearson correlation coefficient - Non-parametric methods: Chi-square test of independence and goodness of fit - Mann Whitney U test - Wilcoxon signed-rank test - Kruskal Wallis test - Friedman’s test - Spearman’s correlation test.

Review of simple and multiple linear regression - introduction to generalized linear models - parameter estimation of generalized linear models - models with different link functions - binary (logistic) regression - estimation and model fitting - Poisson regression for count data - mixed effect models and hierarchical models with practical examples.

Introduction to epidemiology, measures of epidemiology, observational study designs: case report, case series correlational studies, cross-sectional studies, retrospective and prospective studies, analytical epidemiological studies-case control study and cohort study, odds ratio, relative risk, the bias in epidemiological studies.

Introduction to demography, mortality and life tables, infant mortality rate, standardized death rates, life tables, fertility, crude and specific rates, migration-definition and concepts population growth, measurement of population growth-arithmetic, geometric and exponential, population projection and estimation, different methods of population projection, logistic curve, urban population growth, components of urban population growth.

2. David Moore S. and George McCabe P., (2017) Introduction to practice of statistics, 9th Edition, W. H. Freeman.

3. Sundar Rao and Richard J., (2012) Introduction to Biostatistics and research methods, PHI Learning Private limited, New Delhi

2. Gordis Leon (2018), Epidemiology, 6th Edition, Elsevier, Philadelphia

3. Ram, F. and Pathak K. B., (2016): Techniques of Demographic Analysis, Himalaya Publishing house, Bombay.

4. Park K., (2019), Park's Text Book of Preventive and Social Medicine, Banarsidas Bhanot, Jabalpur. 

ESE:50%

The objective of this course is to explore the basics of cloud analytics and the major cloud solutions. Students will learn how to analyze extremely large data sets, and to create visual representations of that data. Also aim to provide students with hands-on experience working with data at scale.

Introduction to cloud computing - Major benefits of cloud computing - Cloud computing deployment models - Private cloud - Public cloud - Hybrid cloud - Types of cloud computing services -Infrastructure as a Service – PaaS – SaaS - Emerging cloud technologies and services - Different ways to secure the cloud - Risks and challenges with the cloud - What is cloud analytics? Parameters before adopting cloud strategy - Technologies utilized by cloud computing

1.Creating Virtual Machines using Hypervisors

2.IaaS: Compute service - Creating and running Virtual Machines

Virtualization - Load Balancing - Scalability & Elasticity – Deployment –Replication – Monitoring - Software Defined Networking - Network Function Virtualization – MapReduce - Identity and Access Management - Service Level Agreements - Billing

1.      Storage as a Service: Ingesting & Querying data into cloud

2.      Database as a Service: Building DB Server

Compute Services

Amazon Elastic Compute Cloud - Google Compute Engine -     Windows     Azure     Virtual Machines

Storage Services

Amazon Simple Storage Service - Google Cloud Storage - Windows Azure Storage

Database Services

Amazon Relational Data Store - Amazon DynamoDB - Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database - Windows Azure Table Service

1.      PaaS: Working with GoogleAppEngine

Cloud Dataflow - The Dataflow programming model - Cloud Pub/Sub - Cloud storage - Cloud SQL - Cloud BigTable - Cloud Spanner - Cloud Datastore - Persistent disks 

1. Database as a Service: Building DB Server

2. Transforming data

 Google BigQuery - Cloud Dataproc - Google Cloud Datalab - Google Data Studio

 1.      Visualize structured data and unstructureddata

Services on Artificial intelligence - Machine learning - Cloud Natural Language API – TensorFlow - Cloud Speech API - Cloud Translation API - Cloud Vision API - Cloud Video Intelligence – Dialogflow – AutoML

1. Load and query data in a data warehouse

2. Setting up and executing a data pipeline job to load data into cloud

2.  Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, Create Space Independent Publishing Platform, 2014.

2.      Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Cloud Computing: Concepts, Technology & Architecture, Prentice Hall, 2014.

ESE: 50%

 The goal is to make familiar with the concepts of the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models,Knowledge Bottlenecks in NLP-Introduction to NLTK,Case study.

 Lab Exercises:

1.     Write a program to tokenize text

2.     Write a program to count word frequency and toremove stopwords

WordLevelAnalysis: RegularExpressions,Text Normalization, Edit Distance, Parsing and Syntax-Spelling, Error Detection and correction-Words and Word classes-Part-of speech Tagging, Naive Bayes and Sentiment Classification: Case study

 Lab Exercises:

 1.     Write a program to tokenize Non-English Languages

 2.     Write a program to get synonyms from WordNet

N-gram Language Models:N-Grams,Evaluating Language Models-The language modelling problem

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

 Lab Exercises:

 1.     Write a program to get Antonyms from WordNet

 2.       Write a program for stemming Non-English words

 Natural Language Generation: Architecture of NLG Systems, Applications

 Machine Translation: Problems in Machine Translation- Machine Translation Approaches-

Evaluation of Machine Translation systems.Case study: Characteristics of Indian Languages

 LabExercises:  

1.  Write a program for lemmatizing words UsingWordNet

2.  Write a program to differentiate stemming and lemmatizing words

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non- classical, Alternative

Models of Information Retrieval – valuation Lexical Resources: Word Embeddings - Word2vec- Glove.

Graphical Models for Sequence Labelling in NLP

 Lab Exercises

 1.     Write a program for POS Tagging or Word Embeddings.

 2.     Case study-based program (IBM) or Sentiment analysis.

2.   Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.

2.  Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1 edition,2009.

Web resources:

ESE:50%

The objective of this course is to provide an overview and the importance of Web analytics and helps to understand role of Web analytic. This course also explores the effective of Web analytic strategies and implementation

Introduction to Web Analytics: Web Analytics Approach – A Model of Analysis – Context matters – Data Contradiction – Working of Web Analytics: Log file analysis – Page tagging – Metrics and Dimensions – Interacting with data in Google Analytics

Lab Exercise

1. Working concept of web analytics

2. Evaluation with Intermediate metrics, custom metrics, calculated metrics.

Goals: Introduction – Goals and Conversions – Conversion Rate – Goal reports in Google Analytics – Performance Indicators – Analyzing Web Users: Learning about users – Traffic Analysis – Analyzing user content – Click-Path analysis – Segmentation

Lab Exercise

1. Collection of web data and other internet data with the help of web analytics

2. Delivering reports based on collected data

3. Implement the concept of web analytics ecosystem

Different analytical tools - Key features and capabilities of Google analytics- How Google analytics works - Implementing Google analytics - Getting up and running with Google analytics -Navigating Google analytics – Using Google analytics reports -Google metrics - Using visitor data to drive website improvement- Focusing on key performance indicators- Integrating Google analytics with third-Party applications 

Lab Exercise

1. Creation of segmentation in web analytics

2. Visualization, acquisition and conversions of web analytics data

Lab Usability Testing- Heuristic Evaluations- Site Visits- Surveys (Questionnaires) - Testing and Experimentation: A/B Testing and Multivariate Testing-Competitive Intelligence - Analysis Search Analytics: Performing Internal Site Search Analytics, Search Engine Optimization (SEO) and Pay per Click (PPC)-Website Optimization against KPIs- Content optimization- Funnel/Goal optimization - Text Analytics: Natural Language Processing (NLP)- Supervised Machine Learning (ML) Algorithms-API and Web data scarping using R and Python 

Lab Exercise

1. Performing site search analytics

2. Analyse the web analytic reports and visualizations

3. Performing visual web analytics

VISUAL ANALYTICS:  Drill down and hierarchies-Sorting-Grouping- Additional Ways to Group- Creating Sets- Analysis with Cubes and MDX- Filtering for Top and Top N- Using the Filter Shelf- The Formatting Pane- Trend Lines- Forecasting- Formatting- Parameters -  SOCIAL NETWORK ANALYSIS:  Types of social network-Graph Visualization-Network Relationships-Network structures: equivalence-Network Evolution-Diffusion in networks- Descriptive Modeling-Predictive Modeling-Customer Profiling-Network targeting 

 Lab Exercise

1. Assignments and final discussions

2. Web Analytics case studies 

2. Sponder M, (2013), Social media analytics: Effective tools for building, interpreting, and using metrics, 1st edition, McGraw Hill Professional.

3. Clifton B, (2012), Advanced Web Metrics with Google Analytics, 3rd edition, John Wiley & Sons.. 

2. Sostre P, LeClaire J, (2007), Web Analytics for dummies, John Wiley & Sons.

3. Burby J, Atchison S, (2007), Actionable web analytics: using data to make smart business decisions, John Wiley & Sons.

4. Dykes B, (2011), Web analytics action hero: Using analysis to gain insight and optimize your business, Adobe Press.  

ESE 50%

To enable the students to learn the information search and retrieval, Genome analysis and Gene mapping, alignment of multiple sequences, and PERL for Bioinformatics. 

Introduction, Historical Overview and Definition, Applications, Major databases in Bioinformatics, Data management and Analysis, Central Dogma of Molecular Biology.

INFORMATION SEARCH AND RETRIEVAL       

Introduction, Tools for web search, Data retrieval tools, Data mining of Biological databases. 

Lab Exercise

1. Test and verify the basic Linux commands and Filters.

2. Create the file(s) and verify the file handling commands.

GENOME ANALYSIS AND GENE MAPPING Introduction, Genome analysis, Genome mapping, Sequence assembly problem, Genetic mapping and linkage analysis, Physical maps, Cloning the entire Genome, Genome sequencing, Applications of Genetic maps, Identification of Genes in Contigs, Human Genome Project.  ALIGNMENT OF PAIRS OF SEQUENCES Introduction, Biological motivation of alignment, Methods of sequence alignments, Using score matrices, Measuring sequence detection

Lab Exercise

1. Create directories and verify the directory commands.

2. Perform basic mathematical operations using PERL.

3. Write a PERL script to demonstrate the Array operations and Regular expressions.

ALIGNMENT OF MULTIPLE SEQUENCES Methods of multiple sequence alignment, Evaluating multiple alignments, Applications of multiple alignments, Phylogenetic analysis, Methods of phylogenetic analysis, Tree evaluation, Problems in Phylogenetic analysis. 

TOOLS FOR SIMILARITY SEARCH AND SEQUENCE ALIGNMENT Introduction, Working with FASTA, Working with BLAST, Filtering and Gapped BLAST, FASTA and BLAST algorithm comparison. 

 Lab Exercise

1. Write a PERL script to concatenate DNA sequences. 

2. Write a PERL script to transcribe DNA sequence into RNA sequence

3. Write a PERL script to calculate the reverse complement of a strand of DNA.

Sequences and Strings: Representing sequence data, Program to store a DNA sequence, Concatenating DNA fragments, Transcription DNA to RNA, Proteins, Files and Arrays, Reading Proteins in Files, Arrays, Scalar and List Context. 

Motifs and Loops: Flow control, Code layout, Finding motifs, Counting Nucleotides, Exploding strings and arrays, Operating on strings. Subroutine and Bugs: Subroutines, Scoping and Subroutines, Command line arguments and Arrays, Passing data to Subroutines, Modules and Libraries of Subroutines. 

 Lab Exercise

1. Write a PERL script to read protein sequence data from a file.

2. Write a PERL script to search for a motif in a DNA sequence.

Hashes, Data structure and algorithms for Biology, Translating DNA into Proteins, Reading DNA from the files in FASTA format, Reading Frames. GenBank: GenBank files, GenBank Libraries, Separating Sequence and Annotation, Parsing Annotations, Indexing GenBank with DBM. Protein Data Bank: Files and Folders, PDB Files, Parsing PDB Files. 

 1. Write a PERL script to append ACGT to DNA using a subroutine.

2 . Case Study: a. To retrieve the sequence of the Human keratin protein from UniProt database and to interpret the results. b. To retrieve the sequence of the Human keratin protein from GenBank database and to interpret the results. 

[2] Beginning Perl for Bioinformatics, Tisdall James, 1st edition, Shroff Publishers (O’Reilly), 2009.    

[2] Bioinformatics Technologies, Yi-Ping Phoebe Chen (Ed), 1st edition, Springer, 2005.

[3] Bioinformatics Computing, Bryan Bergeron, 2nd Edition, Prentice Hall, 1st edition, 2003.

Web resources:

[1] http://cac.annauniv.edu/PhpProject1/aidetails/afug_2013_fu/24.%20BIO%20MED.pdf

[2] https://www.amrita.edu/school/biotechnology/academics/pg/introduction-bioinformaticsbif410

[3] https://canvas.harvard.edu/courses/8084/assignments/syllabus

[4] https://www.coursera.org/specializations/bioinformatics

[5] http://www.dtc.ox.ac.uk/modules/introduction-bioinformatics-bioscientists.html 

ESE 50%

Able to understand the core concepts of evolutionary computing techniques and popular evolutionary algorithms that are used in solving optimization problems.Students will be able to implement custom solutions for real-time problems applicable with evolutionary computing.

1.     Implementation of single and multi-objectivefunctions

2.     Implementation of binaryGA

Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation

– Search problems – Optimization constraints

One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy

1.     Implementation of continuousGA

2.     Implementation of evolutionaryprogramming

Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma

Fundamentals of genetic programming – Genetic programming for minimal time control

Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation

1.     Implementation of geneticprogramming

2.     Implementation of Ant ColonyOptimization

1.     Implementation of Particle SwarmOptimization

2.     Implementation of Multi-ObjectOptimization

Pheromone models – Ant system – Continuous Optimization – Other Ant System

Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm

1.     Simulation of EA in Planning problems (routing, scheduling, packing) and Design problems (Circuit, structure,art)

2.     Simulation of EA in classification/predictionmodelling

Pareto Optimality – Hyper volume – Relative coverage – Non-pareto based EAs – Pareto based EAs – Multi-objective Biogeography based optimization

2.     D.Goldberg,Geneticalgorithmsinsearch,optimization,andmachinelearning.Boston: Addison-Wesley,2012.

3.     K. Deb, Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons,2009.

4.     R. Poli, W. Langdon, N. McPhee and J. Koza, A field guide to genetic programming. [S.l.]: Lulu Press,2008.

5.     T.Bäck,Evolutionaryalgorithmsintheoryandpractice.NewYork:OxfordUniv.Press, 1996.

Web Resources:

1      E.A.EandS.J.E,"IntroductiontoEvolutionaryComputing|Theon-line accompaniment to the book Introduction toEvolutionary Computing",Evolutionarycomputation.org,2015.[Online].Available: http://www.evolutionarycomputation.org/.

2      F.Lobo,"EvolutionaryComputation2018/2019",Fernandolobo.info,2018.[Online]. Available:http://www.fernandolobo.info/ec1819.

3    "EClabTools",Cs.gmu.edu,2008.[Online].Available: https://cs.gmu.edu/~eclab/tools.html.

4    "Kanpur Genetic Algorithms Laboratory", Iitk.ac.in, 2008. [Online]. Available: https://www.iitk.ac.in/kangal/codes.shtml.

5    "Course webpage Evolutionary Algorithms", Liacs.leidenuniv.nl, 2017. [Online]. Available:http://liacs.leidenuniv.nl/~csnaco/EA/misc/ga_demo.htm.

ESE : 50%

This course will help the students to acquire and demonstrate the implementation of the necessary algorithms for solving advanced level Optimization techniques.

Operations Research Methods - Solving the OR model - Queuing and Simulation models – Art of modelling – phases of OR study.

Two variable LP model – Graphical LP solution – Applications. Simplex method and sensitivity analysis – Duality and post-optimal Analysis- Formulation of the dual problem.

Lab Exercise

 1.    Simplex Method 

 2.   Dual Simplex Method

Determination of the Starting Solution – Iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method – Simplex explanation of the Hungarian Method – The trans-shipment Model. 

Lab Exercise

1.   Balanced Transportation Problem

2.   Unbalanced Transportation Problem

3.   Assignment Problems

Network Representation – Critical Path Computations – Construction of the time Schedule – Linear Programming formulation of CPM – PERT networks. 

Lab Exercise:

1. Shortest path computations in a network

2.Maximum flow problem

Minimal Spanning tree Algorithm – Linear Programming formulation of the shortest-route problem. Maximal Flow Model: Enumeration of cuts – Maximal Flow Diagram – Linear Programming Formulation of Maximal Flow Model.

Formulation – Tax Planning Problem – Goal Programming algorithms – Weights method – Preemptive method.

Lab Exercise:

1.  Critical path Computations

2.   Game Programming

Strategic Games and examples - Nash equilibrium and examples - Optimal Solution of two person zero sum games - Solution of Mixed strategy games - Mixed strategy Nash equilibrium - Dominated action with example.

Recursive nature of computation in Dynamic Programming – Forward and Backward Recursion – Knapsack / Fly Away / Cargo-Loading Model – Equipment Replacement Model.

Lab Exercise:

1. Goal Programming

2. Dynamic Programming

Definition – Absolute and n-step Transition Probability – Classification of states.

2.      Garrido José M. Introduction to Computational Models with Python. CRC Press, 2016.

2.      R. Ravindran, D. T. Philips and J. J. Solberg, Operations Research: Principles and Practice, 2nd ed., John Wiley & Sons, 2007. 

3.      F. S. Hillier and G. J. Lieberman, Introduction to operations research, 8th ed., McGraw-Hill Higher Education, 2004. 

4.      K.C. Rao and S. L. Mishra, Operations research, Alpha Science International, 2005. 

5.      Hart, William E. Pyomo: Optimization Modeling in Python. Springer, 2012. 

6.      Martin J. Osborne, An introduction to Game theory, Oxford University Press, 2008

ESE: 50%

The course is designed to provide a real-world project development and deployment environment for the students. 

Project will be based on the specialization domains which students are opted for during this semester.

ESE: 50%

The course is designed to provide to enhance the soft skills and technical undetstanding of the students.  

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