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Computer Science and Data Analytics (CSDA)

About CSDA:

This project aims to enable a candidate to identify a fundamental problem of valuable practical significance for the industry and work towards its viable solution. Students will work on a specified project, in online/offline mode, under a mentor from academia and/or industry for a period expending up to three months after completion of the fourth semester. There will be a project evaluation committee which will examine the students after completion of his/her project for awarding grades. The instruments of assessment will be the final project report and a presentation.

Course Structure: 3 Year Undergraduate

SEMESTER-I
Code Course Name L-T-P-Credits Credit Hours L+T+P/2
BO CDA 101 3-1-0-8 4
BO CDA 103 3-1-2-10 5
BO CDA 105 3-1-2-10 5
BO CDA 107 3-2-0-10 5
Total Credits / Credit Hours 38 19
  • BO CDA 101 Mathematics - I 3-1-0-8 4
    Course Description:

    Definitions, proofs, sets, functions, relations. Functions of several variables: partial derivatives, chain rule, gradient and directional derivative. Tangent planes and normals. Maxima and minima. Probability Theory: Axiomatic construction of the theory of probability, independence, conditional probability, and basic formulae, random variables, probability distributions, mathematical expectations, moments, moment generating function, Random vectors, multivariate distributions, marginal and conditional distributions, conditional expectations, laws of large numbers, central limit theorems.

    Faculty:

    Prof. Kuldeep Patel

    Learning Resources:
    1. G. B. Thomas and R. L. Finney, Calculus and Analytic Geometry, 6th Ed/ 9th Ed, Narosa/ Addison Wesley/ Pearson, 1985/ 1996.
    2. G. R. Grimmett and D. R. Stirzaker, Probability and Random Processes, Oxford University Press, 2001.
    3. P. G. Hoel, S. C. Port and C. J. Stone, Introduction to Probability Theory, Universal Book Stall, 2000.
  • BO CDA 103/ ACS 103 / MCS 103 Foundations of Computer Science 3-1-2-10 5
    Course Description:

    Computer organization: how computers utilize hardware to store data and execute instructions to carry out our algorithms. Binary number systems, Boolean logic, computer circuits and control circuits. Primary machine architecture in all modern machines. Introduction to Computer Programming using Python. Problem solving using algorithms. Historical events in computation. Different types of data analysis and their use cases. Basic introduction to algorithmic paradigms like divide and conquer, recursion, greedy, dynamic programming, etc.

    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using Python.

    Faculty:

    Prof. Sumit Kumar

    Learning Resources:
    1. Michael Dawson, Python Programming for the Absolute Beginner, Third Edition Course Technology PTR, Cengage Learning Inc, 2010.
    2. Ben Stephenson, The Python Workbook: A Brief Introduction with Exercises and Solutions, Springer, 2014.
    3. Jake Vanderplas, Python Data Science Handbook, O′Reilly, 2016.
    4. Dromey R.G., How To Solve It By Computer, Pearson India, 1982.
    5. V. A. Spraul, Think Like a Programmer: An Introduction to Creative Problem Solving, No Starch Press, 2012.
  • BO CDA 105/ ACS 105 / MCS 105 Foundations of Data Analytics 3-1-2-10 5
    Course Description:

    Data sources and collection: Review of existing structured and unstructured data sources, Data collection techniques using sensors, surveys, and different instruments with examples of data collection and storing for different domains such as IoT, Audio and Video, Web and Social Networks etc.
    Data Pre-processing: Importance of data correction, Basic features for data analysis, Descriptive data summarization, data cleaning, normalization, data integration and transformation, data reduction.
    Data representation: Importance of data representations, Extracting salient features from data, Examples include MFCC from audio signals, histogram representation for text, feature representations for images, encoded representations, Spatial data representation: cartography, GIS paper maps to ArcGIS ArcMap symbolizing, Time-series data representations and curve fitting.
    Data visualization: Basic charting, examples with real world weather data, extract and manipulate the data to display the maximum information, various types of graphs like pie chart, bar graphs, 3-D plots using R/Python.
    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python.

    Faculty:

    Prof. Sriparna Saha

    Learning Resources:
    1. Tufte, Edward R. The visual display of quantitative information. Vol. 2. Cheshire, CT: Graphics press, 2001.
    2. Yau, Nathan. Visualize this: the Flowing Data guide to design, visualization, and statistics. John Wiley & Sons, 2011.
  • BO CDA 107/ ACS 107 / MCS 107 Foundations of English for Professionals 3-2-0-8 5
    Course Description:

    Unit 1: Language and Communication: What is Communication Nature, Style and Process of Communication, Communication Barriers, Objectives and Importance of Communication, Formal and Informal Communication, Verbal and Non Verbal Communication
    Unit 2: English Language Remedial Skills, Construction of Sentences, Subject-Verb Agreement, Tenses, Active and Passive Voice, Direct and Indirect Speech, Common Errors
    Unit 3: Listening Skill, Meaning of Listening, Different Types of Listening, Barriers to Listening and Methods to overcome them, Various strategies to develop effective Listening, Semantic Markers, Listening Comprehension
    Unit 4: Oral Skills, Public Speaking, Dealing with lack of confidence, Making an Effective Presentation, Telephone Etiquette, Understanding GD, Why conduct a GD?, How to gear up for a GD?, Different Phases of GD
    Unit 5: Reading Skills: Art of Effective Reading, Speed and Types of Reading, Technique of Effective Reading, Reading Comprehension. (Some pieces of Francis Bacon to be included, such as “Of Studies” and “Of Truth”, “ Of Travel” etc.)
    Unit 6: Principles of Business Writing, Application Letters, Professional Letters, Resume, Curriculum Vitae, Essay Writing, Report- Writing, Writing of Abstract and Summary, Writing Research Papers
    Unit 7: Interviews, types of Interview, Strategies of Successful Interviews.
    Tutorial: to be conducted on a 2-hour slot weekly.

    Faculty:

    Prof. Binod Mishra

    Learning Resources:
    1. John Seely, Oxford Guide to Effective Writing and Speaking, Oxford University Press, 2009.
    2. Sharma Sangeeta and Binod Mishra. Communication Skills for Engineers & Scientists. PHI India, Sixth Reprint 2015
    3. Foundation books, 2007.Walter K. Smart, Handbook of Effective Writing. Forgotten Books, 2017.
    4. Wood, Frederick T. A Remedial English Grammar for Foreign Students, McMillan, 2016
    5. V. Sasikumar, P. KiranmaiDutt, Geetha Rajeevan, "A Course in Listening and Speaking-II', Foundation books, 2007.
    6. Rizvi, Ashraf, Effective Technical Communication, Tata McGraw Hill, 2005.
SEMESTER-II
Code Course Name L-T-P-Credits Credit Hours L+T+P/2
BO CDA 102 3-1-0-8 4
BO CDA 104 3-0-3-9 4.5
BO CDA 106 3-0-3-9 4.5
BO CDA 108 0-0-0-16 16
  Total Credits / Credit Hours 42 29
  • BO CDA 102/ ACS 102 / MCS 102 Mathematics - II 3-1-0-8 4
    Course Description:

    Linear Algebra: Vector spaces (over the field of real and complex numbers). Systems of linear equations and their solutions. Matrices, rank and inverse. Linear transformations. Range space and rank, null space and nullity. Eigenvalues and eigenvectors. Diagonalization of Hermitian matrices.
    Linear programming problems: Problem formulation. Geometrical aspects of LPP, graphical solution. Linear programming in standard form, Simplex Method. 
    Discrete structures: graphs, state machines, modular arithmetic, counting.

    Faculty:

     

    Learning Resources:
    1. K. Hoffman and R. Kunze, Linear Algebra, Prentice Hall, 1996.
    2. G. Strang, Linear Algebra and its Applications, 4th Edition, Thomson, 2006.
    3. S. Chandra, Jayadeva, Aparna Mehra, Numerical Optimization with Applications, Narosa Publishing House, 2009.
    4. Lehman, E., Leighton, F. T., and Meyer A. R., Mathematics for Computer Science, Samurai Media Limited, 2013.
  • BO CDA 104/ ACS 104 / MCS 104 Programming and Data Structures with Python 3-0-3-9 4.5
    Course Description:

    Introduction to Computer Programming using Python. Review of Basic Python Object-Oriented Programming concepts, Algorithm Analysis Recursion, Array-Based Sequences, Stacks, Queues, and Deques, Linked Lists, Trees, Priority Queues Maps, Hash Tables, Search Trees, Sorting and Selection, Text Processing, Graph Algorithms, Memory Management and B-Trees
    Practical component:  Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using Python.

    Faculty:

     

    Learning Resources:
    1. Goodrich, M. T., Tamassia, R. and Goldwasser, Data structures and algorithms in Python, John Wiley & Sons Ltd., 2013.
    2. Miller, Bradley N., and David L. Ranum. Problem solving with algorithms and data structures using python, Second Edition, Franklin, Beedle & Associates Inc., 2011.
  • BO CDA 106/ ACS 106 / MCS 106 Numerical Methods for Data Science 3-0-3-9 4.5
    Course Description:

    Numerical Linear Algebra: LU, PLU, QR, and Cholesky factorizations; direct methods for solution of linear systems. Eigenvalues and eigenvectors; least square and minimum normed solutions with applications to data problems; singular value decomposition; principal component analysis; linear discriminant analysis
     
    Numerical Differentiation and Integration: Discrete Approximation of Derivatives: Forward, Backward and Central Finite Difference Forms, Numerical Integration, Simple Newton‐Cotes Rules: Trapezoidal and Simpson's (1/3) Rules; Gaussian Quadrature Rules: Gauss‐Legendre, Gauss‐Hermite.
    Numerical Optimization Techniques: Derivative-Free methods (Golden Section, Fibonacci Search Method, Bisecting Method), Methods using Derivatives (Newton’s Method, Steepest Descent Method), Penalty Function Methods for Constrained Optimization. 
    Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python/MATLAB.

    Faculty:

     

    Learning Resources:
    1. G.Strang, Linear Algebra and its Applications, 4th Edition, Thomson, 2006.
    2. S. Axler, Linear Algebra Done Right. 3rd edition, Springer International Publishing, 2015.
    3. J. Nocedal and S. J. Wright, Numerical Optimization. New York: Springer Science+Business Media, 2006.
    4. E. Kreyszig, Advanced Engineering Mathematics, 10th Edition, John Wiley and Sons, Inc., U.K., 2018.
    5. S. Chandra, Jayadeva, Aparna Mehra, Numerical Optimization with Applications, Narosa Publishing House, 2009.
  • BO CDA 108/ ACS 108 / MCS 108 Capstone project I 0-0-0-16 16
    Course Description:

    Capstone project I aims to impart hands-on skills to develop problem-solving aptitude via project. The objective of this project would be to apply tools and techniques to some innovative work based on learning from the first-year courses. Capstone projects should take investigative aspects culminating in a final product (report, short film, or multimedia presentation). Students may be asked to select a topic or social problem that interests them, conduct a study on it, and create a final product demonstrating their learning acquisition. Students may be asked to give an oral presentation on the project to a panel of experts to evaluate the project's quality. Students may be given time for two months, after finishing the other courses of second semester, to submit the final project product. Student cannot start the second year courses without completion of Capstone Project I.

    Faculty:

     

    Learning Resources:
    1. John Seely, Oxford Guide to Effective Writing and Speaking, Oxford University Press, 2009.
SEMESTER-III
Code Course Name L-T-P-Credits Credit Hours L+T+P/2
BO CDA 201 3-1-2-10 5
BO CDA 203 3-1-2-10 5
BO CDA 205 3-1-2-10 5
BO CDA 207 3-1-0-8 4
  Total Credits / Credit Hours 38 19
  • BO CDA 201 Statistics for Data Science 3-1-2-10 5
    Course Description:

    Ordered Statistics, probability distributions of Sample Range, Minimum and Maximum order Statistics. Random Sampling, Sampling distributions: Chi-square, T, F distributions.
    Point Estimation: Sufficiency, Factorization theorem, Consistency, Moment method of estimation, Unbiased Estimation, Minimum Variance Unbiased Estimator and their properties, Rao-Cramer lower bound, Rao-Blackwellization, Fisher Information, Maximum Likelihood Estimator and properties, Criteria for evaluating estimators: Mean squared error.
    Interval Estimation:  Coverage Probabilities, Confidence level, Sample size determination, Shortest Length interval, Pivotal quantities, interval estimators for various distributions.
    Testing of Hypotheses: Null and Alternative Hypotheses, Simple hypothesis, Composite hypothesis, Test Statistic, Critical region, Error Probabilities, Power Function,  Level of Significance, Neyman-Pearson Lemma, One and Two Sided Tests for Mean, Variance and Proportions, One and Two Sample T-Test, Pooled T-Test, Paired T-Test, Chi-Square Test, Contingency Table Test, Maximum Likelihood Test, Duality between Confidence Intervals.
    Bayesian Estimation: Prior and Posterior Distributions, Quadratic Loss Function, Posterior Mean, Bayes Estimates for well Known Distributions (Normal, Gamma, Exponential, Binomial, Poisson, Beta etc.) 
    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python.

    Faculty:

     

    Learning Resources:
    1. Kandethody M. Ramachandran, Chris P. Tsokos, Mathematical Statistics with applications, Academic Press, 2009.
    2. William W. Hines, Douglas C. Montgomery, David M. Goldsman, Connie M. Borror, Probability and Statistics in Engineering, 4th Edition, John Wiley & Sons, 2003.
    3. Robert V. Hogg, Joseph W. McKean, Allen T. Craig, Introduction to Mathematical Statistics, 7th Edition, Pearson, 2012.
  • BO CDA 203 Design of Algorithms 3-1-2-10 5
    Course Description:

    Model of Computations: RAM Model of computation, uniform cost model, logarithmic cost model. Complexity Analysis: Big O, omega, theta notations, solving recurrence relation. Data Structure: binary search trees, AVL trees and red-black trees, B-trees, hashing, Priority queues, Heaps. Sorting algorithms: Merge sort, Quick sort, Heap sort, Randomized quick sort, Lower bound of comparison based sorting, Counting sort, Radix sort, Bucket sort. Algorithm design techniques: Greedy, Divide and Conquer, Dynamic Programming . Graph Algorithms: BFS and DFS, Minimum spanning trees- Kruskal and Prim algorithm, Shortest Path- Dijkstra, Bellman-Ford, Johnson algorithm, Network flow- Ford-Fulkerson algorithm. NP-Completeness: Class P, NP, NP-hard and NP-complete, Examples of NP-complete problems.
    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using C/Python. The problems to be solved should involve all the algorithm designing techniques that are covered in the theory class.

     

    Faculty:

     

    Learning Resources:
    1. M. A. Weiss, Data Structures and Algorithms in C++, 4th Edition, Pearson, 2014
    2. J. Kleinberg and Eva Tardos, Algorithm Design, Pearson Education, 2013
    3. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2009
    4. A. Aho, J. E. Hopcroft and J. D. Ullman, The Design and Analysis of Computer Algorithms, Pearson, 1974
  • BO CDA 205 Machine Learning Techniques 3-1-2-10 5
    Course Description:

    Supervised learning: decision trees, nearest neighbor classifiers, generative classifiers like naive Bayes, linear discriminate analysis, Support vector Machines, feature selection techniques: wrapper and filter approaches, back-ward selection algorithms, forward selection algorithms, PCA, LDA
    Unsupervised learning: K-means, hierarchical, EM, K-medoid, DB-Scan, cluster validity indices, similarity measures, some modern techniques of clustering;
    Graphical models: HMM, CRF, MEMM
    Semi-supervised learning, Active Learning, Topic Modelling: LDA

    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python. The problems to be solved should involve all the algorithm designing techniques that are covered in the theory class.
     
    Faculty:

     

    Learning Resources:
    1. Christopher Bishop, Pattern recognition and machine learning, Springer Verlag, 2006.
    2. Hastie, Tibshirani, Friedman, The elements of Statistical Learning, Springer, 2009
    3. T. Mitchell., Machine Learning, McGraw-Hill, 1997.
  • BO CDA 207 Financial Economics 3-1-0-8 4
    Course Description:

    Introduction: An introduction to financial economics, financial assets and their roles in the economy, financial system and its management.
    Choice under uncertainty and financial decisions: Definitions of uncertainty and risk, utility theory under uncertainty, axioms of choice under uncertainty, Definition of risk aversion and risk neutrality.
    Financial instruments: Types and characteristics of financial instruments, term structure and theories of interest rates, i) The Expectations Hypothesis, ii) Liquidity Preference Theory, iii) Market Segmentation Theory, iv) Preferred Habitat Theory.
    Options and financial derivatives: Introduction to future and option. Understanding rights and obligations of the parties involved in various types of options, Fundamental of Weiner process and Random Walk etc. Basic of Black-Scholes Pricing, Notions of Delta, Gamma, Vega, Theta , Rho. Commodity derivatives: price discovery, valuation of futures and options.
    Investment theory and portfolio analysis: The trade-off between expected return and risk. Efficient diversification with multiple risky assets. Weak and strong form of market efficiency. The capital asset pricing model and arbitrage price model.
    Financial market and economy: Nexus between Financial market and economy. A brief discussion on fragile financial system and its recurring crises. Indian economy and its relation with global financial world.

     
     
    Faculty:

     

    Learning Resources:
    1. Z. Bodie, C. Merton and D.L. Cleeton, Financial Economics, 1st Edition, Pearson Education, 2009.
    2. Stephen Roy, J. Werner and Stephen Ross, Principle of Financial Economics, 2nd Edition, Cambridge University Press, 2012.
    3. J.C.Hull, Futures and Option Markets, 7th Edition, Prentice-Hall, New Jersey, 2010.
    4. David G. Luenberger, Investment Science, Oxford University Press, 1997.
    5. Case Studies and Research Articles.
SEMESTER-IV
Code Course Name L-T-P-Credits Credit Hours L+T+P/2
BO CDA 202 3-0-3-9 4.5
BO CDA 204 3-0-3-9 4.5
BO CDA 206 3-0-3-9 4.5
BO CDA 208 3-0-3-9 4.5
  Total Credits / Credit Hours 36 18

Mandatory BO CDA 210: Summer Industry Project (24 credits): To work on a project relevant to Industry/contemporary problems of Industrial significance for award of Diploma and/or entering to the third year for the degree module.
This project aims to enable a candidate to identify a fundamental problem of valuable practical significance for the industry and work towards its viable solution. Students will work on a specified project, in online/offline mode, under a mentor from academia and/or industry for a period expending up to three months after completion of the fourth semester. There will be a project evaluation committee which will examine the students after completion of his/her project for awarding grades. The instruments of assessment will be the final project report and a presentation.

Students are encouraged to explore and identify suitable industries/institutions for the project, and the IIT Patna placement cell would like to guide and facilitate their efforts. The project abstract defining aim, methodology, and deliverable has to be submitted to IIT Patna with due approval of the project evaluation committee for further monitoring on the progress of the work.  

  • BO CDA 202 Database management 3-0-3-9 4.5
    Course Description:

    Database system architecture: Data Abstraction, Data Independence, Data Definition and Data Manipulation Languages; Data models: Entity-relationship, network, relational and object oriented data models, integrity constraints and data manipulation operations; Relational query languages: Relational algebra, tuple and domain relational calculus, SQL and QBE; Relational database design: Domain and data dependency, Armstrong’s axioms, normal forms, dependency preservation, lossless design; Query processing and optimization: Evaluation of relational algebra expressions, query equivalence, join strategies, query optimization algorithms; Storage strategies: Indices, B-trees, hashing; Transaction processing: Recovery and concurrency control, locking and timestamp based schedulers, multi-version and optimistic Concurrency Control schemes; Recent Trends: XML Data, XML Schema, JSON and NoSQL Systems, etc,

    Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The problems to be solved should involve all the algorithm designing techniques that are covered in the theory class, for example, Database schema design, database creation, SQL programming and report generation using a commercial RDBMS like ORACLE/SYBASE/DB2/SQL-Server/INFORMIX. Students are to be exposed to front end development tools, ODBC and CORBA calls from application Programs, internet based access to databases and database administration.

     
    Faculty:

     

    Learning Resources:
    1. Abraham Silberschatz, Henry Korth, and S. Sudarshan, Database System Concepts, McGraw-Hill, 2013.
    2. Raghu Ramakrishnan, Database Management Systems, WCB/McGraw-Hill, 2014.
    3. Bipin Desai, An Introduction to Database Systems, Galgotia Publications Pvt Ltd., 2010.
    4. J. D. Ullman, Principles of Database Systems, Galgotia Publications Pvt Ltd., 1994.
    5. R. Elmasri and S. Navathe, Fundamentals of Database Systems, Pearson Education India, 2015.
    6. Serge Abiteboul, Richard Hull and Victor Vianu, Foundations of Databases, Pearson, 1994.
  • BO CDA 204 Computer Organization 3-0-3-9 4.5
    Course Description:

    Basic computer organization and design, Operational concepts, Instruction codes, Computer Registers, Computer Instructions , 
    CPU - registers, instruction execution cycle, RTL interpretation of instructions, addressing modes, instruction set. Case study - instruction sets of some common CPUs; Assembly language programming for some processor; Data representation: signed number representation, fixed and floating point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder.
    Case study - design of a simple hypothetical CPU; Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards; Memory organization: Memory interleaving, concept of hierarchical memory organization, cache memory, cache size vs block size, mapping functions, replacement algorithms, write policy; Peripheral devices and their characteristics: Input-output subsystems, I/O transfers - program controlled, interrupt driven and DMA, privileged and non-privileged instructions, software interrupts and exceptions. Programs and processes - role of interrupts in process state transitions. Familiarization with assembly language programming; Development kits as well as Microprocessors/PCs may be used for the laboratory, along with design/simulation tools as and when necessary.
    Practical component: Lab to be conducted on a 3-hour slot weekly.  The lab classes will mainly consist of (a) Assembly language programming using MIPS/X86/ARM instruction set (b) Design and Simulation of Data Path and Control. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.

     

    Faculty:

     

    Learning Resources:
    1. M. Morris Mano, Computer Systems Architecture, Third Edition, Pearson Education, 2017.
    2. Kai Hwang and F A Briggs, Computer Architecture and parallel processing, McGraw Hills,1990.
    3. Patterson, David A., and John L. Hennessy. Computer organization and design ARM edition: the hardware software interface, Morgan Kaufmann, 2016.
  • BO CDA 206 Advanced Machine Learning Techniques 3-0-3-9 4.5
    Course Description:

    Mathematics of machine learning. Brief introduction of big data problem, Overview of linear algebra, probability, numerical computation Scalars, vectors, matrix, tensors,  norms, eigen value, eigenvector, singular value decomposition, determinant, Probability distribution, bayes rule, conditional probability, variance, covariance, Overflow, underflow, gradient based optimization, least square,- Neural network - Perceptron, Multi-level perceptron, Universal approximation theorem. Deep Networks for Sequence Prediction: Encoder-decoder models (case study translation), Attention models, LSTM, Memory Networks . Deep Network for Generation  – Sequence to Sequence Models  – Variational Autoencoders – Generative Adversarial Networks (GANs) – Pointer Generator Networks  – Transformer Networks
    Time series forecasting: models and case-studies
    Modern clustering techniques: Multi-objective optimization for clustering, Deep learning for clustering Online Learning, Mistake Bounds, Sub-space clustering
    Meta-learning and federated learning concepts: tools and techniques
    Case-studies: Recent topics for solving various problems of natural language processing, bioinformatics, information retrieval
    Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python.

     
    Faculty:

     

    Learning Resources:
    1. Kevin P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
    2. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
    3. Yoav Goldberg, A primer on neural network models for natural language processing. J. Artif. Int. Res. 57, 1 (September 2016), 345-420.
    4. R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter, Probabilistic Networks and Expert Systems, Springer-Verlag, 1999.
    5. M. I. Jordan (ed), Learning in Graphical Models, MIT Press, 1998.
  • BO CDA 208 Web Development and App Design 3-0-3-9 4.5
    Course Description:

    Web data protocols (HTTP, WebSockets), Web client programming (HTML, CSS, Bootstrap, JavaScript, jQuery, Ajax), Cookies and sessions, Databases, transaction management, ORM tools, Web frameworks, principles of UI design. Introduction to Mobile Computing , Introduction to Android Development Environment,  Factors in Developing Mobile Applications: Mobile Software Engineering, Frameworks and Tools , Generic UI Development

    Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.

     
     
    Faculty:

     

    Learning Resources:
    1. Danny Goodman, Dynamic HTML: The Definitive Reference, O'Reilly Media Inc., 2006.
    2. Hardy, Brian, and Bill Phillips. Android programming: the big nerd ranch guide. Addison-Wesley Professional, 2013.
SEMESTER-V
Code Course Name L-T-P-Credits Credit Hours L+T+P/2
BO CDA 301 3-0-3-9 4.5
BO CDA 303 3-0-3-9 4.5
BO CDA 305 3-1-2-10 5
BO CDA 307 3-0-0-6 3
BO CDA 3xx 3-0-x-x 3
  Total Minimum Credits / Credit Hours 40 20
  • BO CDA 301 Computer and Network Security 3-0-3-9 4.5
    Course Description:

    Evolution of computer networks; Physical Layer: Theoretical basis for data communication, transmission media and impairments, switching systems Medium Access Control Sublayer: Channel allocation Problem, multiple access protocols, Ethernet Data link layer: Framing, HDLC, PPP, sliding window protocols, error detection and correction Network Layer: Internet addressing, IP, ARP, ICMP, CIDR, routing algorithms (RIP, OSPF, BGP); Transport Layer: UDP, TCP, flow control, congestion control; Introduction to quality of service; Application Layer: DNS, Web, email, authentication, encryption.
    Network security: IP Routing, Firewalls, ACLs, network address translation, virtual networking, network services (DHCP, DNS) , IP routing basics, ability to configure network services
    Network services vulnerabilities: ARP spoofing, network scanning and fingerprinting, vulnerability exploitation, basics of penetration, esting; knowledge of vulnerability mitigation techniques, Wireless network security Connecting to WEP/WPA PSK secured networks, monitoring and diverting wireless traffic.
    Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The problems to be solved should involve all the techniques that are covered in the theory class. Specifically explore security tools and attacks in practice. It will focus on attacks (e.g., buffer overflow, heap spray, kernel rootkits, and denial of service), hacking fundamentals (e.g., scanning and reconnaissance), defenses (e.g., intrusion detection systems and firewalls). Students are expected to finish  lab assignments that use real-world malware, exploits, and defenses.  VAPT ( Vulnerability Analysis and Penetration Testing) for Web, Network, Mobile App and Cloud and IoT infrastructure. Tutorial/Exercises on writing a Cyber Security report

     
    Faculty:

     

    Learning Resources:
    1. Peterson & Davie, Computer Networks, A Systems Approach, 5th Edition, Morgan Kaufmann, 2011.
    2. William Stallings, Data and Computer Communication, Pearson Education, 2017.
    3. Behrouz A. Forouzan, Data Communication and Networking, McGraw-Hill, 2006.
    4. Andrew S. Tanenbaum, Computer Networks, Prentice Hall, 2002.
    5. Douglas Comer, Internetworking with TCP/IP, Volume 1, Pearson Education India, 2015.
    6. W. Richard Stevens, TCP/IP Illustrated, Volume 1, Addison-Wesley, 2011.
  • BO CDA 303 Operating Systems 3-0-3-9 4.5
    Course Description:

    Process Management: process; thread; scheduling. Concurrency: mutual exclusion; synchronization; semaphores; monitors; Deadlocks: characterization; prevention; avoidance; detection. Memory Management: allocation; hardware sup- port; paging; segmentation. Virtual Memory: demand paging; replacement; allocation; thrashing. File Systems and Implementation. Secondary Storage: disk structure; disk scheduling; disk management. (Linux will be used as a running example, while examples will draw also from Windows NT/7/8.); Advanced Topics: Distributed Systems. Security. Real-Time Systems.

    Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes. Programming assignments should be given to build different parts of an OS kernel.
     
    Faculty:

     

    Learning Resources:
    1. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 8th Ed, John Wiley & Sons, 2010.
    2. A. S. Tenenbaum, Modern Operating Systems, 2nd Ed, Prentice Hall of India, 2001.
    3. H. M. Deitel, P. J. Deitel and D. R. Choffness, Operating Systems, 3rd Ed, Prentice Hall, 2004.
    4. W. Stallings, Operating Systems: Internal and Design Principles, 5th Ed, Prentice Hall, 2005.
    5. M. J. Bach, The Design of the UNIX Operating System, Prentice Hall of India, 1994.
    6. M. K. McKusick et al, The Design and Implementation of the 4.4 BSD Operating System, Addison Wesley, 1996.
  • BO CDA 305 Artificial Intelligence Techniques 3-1-2-10 5
    Course Description:

    Problem Solving: Uninformed search, Informed search, Local Search. Game Playing: Minmax, Alpha-Beta Pruning, Constraint Satisfaction Problems: Factor Graphs, Backtracking Search, Dynamic Ordering, Arc consistency. Knowledge, Reasoning and Planning: Propositional and Predicate Calculus, Semantic Nets, Automated Planning, Machine Learning: Learning from examples and analogy, Association rule mining, Application Topics: Introduction to NLP, Introduction to Fuzzy Sets and Logic.  Basic functions on fuzzy sets,  relations, rule based models and linguistic variables, fuzzy controls, Fuzzy decision making, applications of fuzzy logic.
    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python. The problems to be solved should involve all the algorithm designing techniques that are covered in the theory class.

     

     

    Faculty:

     

    Learning Resources:
    1. S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2009.
    2. E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990.
    3. T. Mitchel, Machine Learning, McGraw-Hill, 1997.
  • BO CDA 307 Industrial and Organizational Psychology 3-0-0-6 3
    Course Description:

    Introduction: Psychology as a science of Behaviour and Mental Processes: Nature, Scope and Subject Matter of Industrial and Organizational Psychology; Time and Motion Study, Classical Hawthorne Studies.
    Employer Selection: Recruitment Process; Selection Process - Job and Worker Analyses, Matching Job with the Person; Selection Methods - Application Blank, Biographical Inventories, References and Recommendation Letters, Interviews.
    Psychological Testing: Characteristics of Psychological Tests; Types of Psychological Tests; Tests of Knowledge, Skills and Abilities - Interest, Aptitude and Personality Tests; Limitations of Psychological Testing Programmes.
    Training and Learning: Need Identification; Psychological Factors in Learning; Training Methods in the Workplace; Effective Training Programme; Career Planning and Development.
    Motivation: Needs, Incentives and Motives; Financial and Non-financial Motives; Theories of Motivation; Management of Motivation; Organizational Commitment and Job Satisfaction.
    Leadership: Changing Views of Leadership; Theories of Leadership; Leadership Styles; Pole of Power in Leadership; Charismatic and Effective Leaders.
    Group Behaviour:Formal and Informal Organizations in Industry; Conflicts in Organization; Resolution of the Conflicts; Decision Making Process.
    Characteristics of the Workplace: Working Conditions - Physical and Psychological; Accident, Safety and Health; Management of Stress; Spirituality at Work.
    Organizational Communication: Process of Communications; Upward, Downward and Horizontal Communications; Barriers to Communication; Effective Communication.

     
     
    Faculty:

     

    Learning Resources:
    1. Schultz, D. & Schultz, S. E., Psychology & Work Today: An Introduction to Industrial and Organizational Psychology, 10th Ed., New Jersy: Prentice Hall, 2009.
    2. Landy, F. J. & Conte, J. M., Work in the 21st Century: An Introduction to Industrial and Organizational Psychology, 3rd Ed., New York: Wiley- Blackwell, 2009.
    3. Robins, S. P. & Judge, T. A., Organizational Behaviour, 14th Ed., New Jersey, Prentice Hall, 2010.
    4. Pierce G.F, Spirituality at Work: 10 Ways to Balance Your Life on the Job, 1ST Ed., Illinois, Loyola Press, 2005.
  • BO CDA 3xx Elective I 3-0-x-x 3
    Course Description:

    An indicative list of electives:

    1. Data Mining
    2. Cloud Computing
    3. High Performance Computing

     

    After getting necessary approvals from the IIT Patna Senate, the list of electives may be extended to match the industry demand from time-to-time.

     
     
    Faculty:

     

    Learning Resources:
    1. D.
SEMESTER-VI
Code Course Name L-T-P-Credits Credit Hours L+T+P/2
BO CDA 302 3-1-2-10 5
BO CDA 304 0-0-0-20 20
BO CDA 3xx 3-0-x-x 3
BO CDA 3xx 3-0-x-x 3
  Total Minimum Credits / Credit Hours 42 31
Total Minimum Credits 236 + 24* 260
Total Minimum Credit Hours 136 + 24* 160
*mandatory Summer Industry Project after fourth semester    
  • BO CDA 302 Big Data Analytics 3-1-2-10 5
    Course Description:

    Introduction to Big Data: Why Big Data and Where did it come from?, Characteristics of Big Data- Volume, Variety, Velocity, Veracity, Valence, Value, Challenges and applications of Big Data. Enabling Technologies for Big Data: Introduction to Big Data Stack, Introduction to some Big Data distribution packages. Big Data Computing Technology: Overview of Apache Spark, HDFS, YARN, Introduction to MapReduce, MapReduce Programming Model with Spark, MapReduce Example: Word Count, Page Rank etc. Big Data Storage Technology: CAP Theorem, Eventual Consistency, Consistency Trade-Offs, ACID and BASE, Introduction to Zookeeper and Paxos, Introduction to Cassandra, Cassandra Internals, Introduction to HBase, HBase Internals. Big Data Analytics framework: Introduction to Big Data Streaming Systems, Big Data Pipelines for Real-Time computing, Introduction to Spark Streaming, Kafka, Streaming Ecosystem. Scalable Machine Learning for Big Data: Overview of Big Data Machine Learning, Mahout Introduction, Big Data Machine learning Algorithms in Mahout- kmeans, Naïve Bayes etc. Scalable Machine learning with Spark for Big Data Analytics: Big Data Machine Learning Algorithms in Spark- Introduction to Spark MLlib, Introduction to Deep Learning for Big Data. Large Scale Graph Processing for Big Data: Introduction to Pregel, Introduction to Giraph, Introduction to Spark GraphX

    Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. The topics taught in the theory course should be synchronized with the laboratory classes by using R/Python. The problems to be solved should involve all the algorithm designing techniques that are covered in the theory class.
     
     
    Faculty:

     

    Learning Resources:
    1. Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley, 2014.
    2. Dirk Deroos et al., Hadoop for Dummies, Dreamtech Press, 2014.
    3. Chuck Lam, Hadoop in Action, Manning, 2010.
    4. Leskovec, Rajaraman, Ullman, Mining of Massive Datasets, Cambridge University Press, 2014.
    5. I.H. Witten and E. Frank, Data Mining: Practical Machine learning tools and techniques, 3rd Edition, Morgan Kaufmann, 2011.
    6. Erik Brynjolfsson et al., The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, 2014
  • BO CDA 304 Capstone Project II 0-0-0-20 20
    Course Description:

    This capstone project aims to impart hands-on skills to develop problem-solving aptitude via project. The objective of this project would be to learn tools and techniques for data analytics through a group project with essential contributions as an individual. Capstone projects also may take investigative aspects that culminate in a final product (report, short film, or multimedia presentation). Students may be asked to select a topic or social problem that interests them, conduct a study on it, and create a final product demonstrating their learning acquisition. Students may be asked to give an oral presentation on the project to a panel of experts collectively to evaluate the project's quality. Students may be given time for three months, after finishing the other courses of second semester, to submit the final project product.

     

     

    Faculty:

     

    Learning Resources:
    1. S.
  • BO CDA 3xx Elective II 3-0-x-x 3
    Course Description:

    An indicative list of electives:

    1. Image and Video Analytics
    2. Big Data Security
    3. Introduction to Deep Learning

     

    After getting necessary approvals from the IIT Patna Senate, the list of electives may be extended to match the industry demand from time-to-time.

     
     
    Faculty:

     

    Learning Resources:
    1. S
  • BO CDA 3xx Elective III 3-0-x-x 3
    Course Description:

    An indicative list of electives:

    1. Data Mining
    2. Cloud Computing
    3. High Performance Computing

     

    After getting necessary approvals from the IIT Patna Senate, the list of electives may be extended to match the industry demand from time-to-time.

     
     
    Faculty:

     

    Learning Resources:
    1. D.