CSE DATA SCIENCE

Data Science 8th Semester Study Resources

Access complete unit-wise notes, important questions, PYQs and exam-focused study material for RGPV CSE Data Science 8th Semester.

Semester Subjects

8th Semester Subjects

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Compiler Design

Lexical analysis, syntax analysis, type checking, code generation and optimization.

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Reinforcement Learning

MDP, value iteration, policy iteration, Q-learning, SARSA, actor-critic and deep RL.

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Project Management

Feasibility, PERT, CPM, agile, scrum, DevOps, Docker, CI/CD and monitoring.

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Computational Statistics

Probability, sampling, Monte Carlo methods, data mining, decision trees and Apriori.

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Machine Learning for Data Science

Regression, classification, ensemble learning, neural networks and model evaluation.

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Blockchain Technologies

Blockchain, Bitcoin, smart contracts, consensus, Hyperledger, Ethereum and Corda.

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Time-Series Analysis

Forecasting, regression models, ARIMA, SARIMA, VAR, neural networks and spectral analysis.

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Internet of Things

IoT architecture, sensors, ZigBee, RFID, MQTT, CoAP, Arduino, Raspberry Pi and cloud IoT.

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Quantum Computing

Qubits, superposition, entanglement, quantum gates, algorithms and quantum toolkits.

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CD-801

Compiler Design Units

1

Unit 1

Introduction of compiler, major data structures, bootstrapping, porting, compiler phases, lexical analysis, input buffering, tokens and LEX.

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2

Unit 2

Syntax analysis, CFG, top-down parsing, recursive descent parsing, predictive parsing, bottom-up parsing, LR parsers and syntax directed definitions.

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3

Unit 3

Type checking, type system, type conversion, overloading, polymorphic functions, runtime environment, storage allocation and symbol table.

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4

Unit 4

Intermediate code generation, declarations, assignment statements, boolean expressions, backpatching, procedure calls and DAG.

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5

Unit 5

Code optimization, basic blocks, loops in flow graphs, dead code elimination, loop optimization and data flow analysis.

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802(A)

Reinforcement Learning Units

1

Unit 1

Applications of RL, Atari, AlphaGo, relation with AI, Markov Decision Process, value iteration, policy iteration and linear programming.

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2

Unit 2

Approximate dynamic programming, curse of dimensionality, representations, approximate value iteration and convergence guarantees.

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3

Unit 3

Stochastic approximation, TD learning, TD(0), TD(lambda), Q-learning, SARSA, function approximation and off-policy learning.

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4

Unit 4

Actor-Critic method, policy gradient, natural actor-critic and deep reinforcement learning.

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5

Unit 5

Exploration vs exploitation, Upper Confidence Bound and Upper Confidence Reinforcement Learning.

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802(B)

Project Management Units

1

Unit 1

Project overview, feasibility studies, project identification, market analysis, demand analysis, cost estimate and financial appraisal.

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2

Unit 2

Project scheduling, PERT, CPM, critical path, precedence relationship, float, crashing, cost control and resource leveling.

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3

Unit 3

Risk analysis, project control, project audit, termination, agile principles, scrum, lean, DevOps and ITIL.

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4

Unit 4

Scrum terminology, sprint, product backlog, sprint backlog, sprint review, retrospective and scrum roles.

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5

Unit 5

DevOps overview, Docker containerization, source code management, automated testing, CI, CD and monitoring.

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802(C)

Computational Statistics Units

1

Unit 1

Probability concepts, sampling concepts, generating random variables, exploratory data analysis and Monte Carlo methods.

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2

Unit 2

Data partitioning, probability density estimation, statistical pattern recognition and nonparametric regression.

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3

Unit 3

Data mining algorithms, instances, features, concept learning, concept description and decision trees.

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4

Unit 4

Classification trees, information theory, entropy, ID3, C4.5, CHAID, CART, regression trees and pruning.

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5

Unit 5

Preprocessing, discretization, feature extraction, missing data, association rule mining, Apriori, regression and KNN.

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802(D)

Machine Learning for Data Science Units

1

Unit 1

Linear algebra review, ML introduction, applications, VC dimension, PAC learning, hypothesis space and bias-variance tradeoff.

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2

Unit 2

Linear regression, gradient descent, perceptron, logistic regression, Naive Bayes, SVM, decision tree and random forest.

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3

Unit 3

Ensemble learning, voting, bagging, boosting, stacking, K-means, KNN, Gaussian mixture models and EM algorithm.

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4

Unit 4

Multilayer perceptron, activation functions, SGD, backpropagation, ReLU, hyperparameter tuning, batch normalization and dropout.

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5

Unit 5

ML experiments, cross-validation, bootstrapping, classifier performance, t-test, McNemar test and K-fold paired t-test.

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803(A)

Blockchain Technologies Units

1

Unit 1

Blockchain overview, public ledgers, Bitcoin, smart contracts, distributed consensus, crypto primitives, hash function and Merkle tree.

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2

Unit 2

Bitcoin blockchain, creation of coins, payments, double spending, Bitcoin scripts, P2P network, mining and proof of work.

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3

Unit 3

Permissioned blockchain, design issues, smart contract execution, state machine replication, Paxos, RAFT and BFT.

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4

Unit 4

Enterprise blockchain applications, cross-border payments, KYC, food security, mortgage, trade finance and supply chain.

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5

Unit 5

Hyperledger Fabric, identities, policies, channels, transaction validation, smart contracts, Ethereum, Ripple and Corda.

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803(B)

Time-Series Analysis Units

1

Unit 1

Introduction to time series, forecasting, types of data, autocorrelation, partial autocorrelation and forecasting process.

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2

Unit 2

Statistics background, time series plots, smoothed data, data transformation, model performance and monitoring.

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3

Unit 3

Time series regression, least squares estimation, inference, prediction, model checking, variable selection and exponential smoothing.

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4

Unit 4

ARMA, ARIMA, stationarity, invertibility, seasonal ARIMA, forecasting, model selection and impulse response function.

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5

Unit 5

Multivariate time series, vector ARIMA, VAR models, neural networks, spectral analysis and Bayesian forecasting.

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803(C)

Internet of Things Units

1

Unit 1

IoT definition, characteristics, framework, physical and logical design, IoT enablers, M2M, WoT and IPv4 vs IPv6.

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2

Unit 2

Sensors, sensor node components, sensor features, sensor classes, errors, actuators and actuator types.

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3

Unit 3

IoT networking, 6LoWPAN, IEEE 802.15.4, ZigBee, RFID, NFC, Bluetooth and wireless sensor networks.

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4

Unit 4

MQTT, SMQTT, CoAP, request-response model, XMPP, AMQP features, components and frame types.

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5

Unit 5

IoT platforms, Arduino, Raspberry Pi, data analytics for IoT, cloud for IoT, storage models, APIs and case studies.

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803(D)

Quantum Computing Units

1

Unit 1

Motivation, industry players, origin of quantum computing, qubits, Braket notation, Bloch sphere, superposition and entanglement.

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2

Unit 2

Matrix algebra, basis vectors, orthogonality, inner product, Hilbert space, tensors, unitary operators and eigenvalues.

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3

Unit 3

Quantum architecture, qubit representation, multi-qubit states, Bell state, quantum gates and quantum circuits.

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4

Unit 4

Quantum programming model, classical and quantum steps, amplitude amplification, QFT, phase estimation and quantum walks.

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5

Unit 5

Shorโ€™s algorithm, Groverโ€™s algorithm, Deutsch algorithm, Deutsch-Jozsa algorithm, IBM Quantum and Microsoft Q.

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