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DATA SCIENCE

DATA SCIENCE  UNIT – I: Introduction to Data Science: Data Analysis Life Cycle Overview. Data analysis Discovery, Framing Problem, Developing Initial Hypothesis, Sources of Data, Process for Making Sense of Data, Data Preparation, Performing ETLT, Data Conditioning, Survey and Visualize, Common tools for Data Preparation Phase, Data Exploration and Variable Selection, Common tools for the Model Planning and Building Phase, Communicate Results, Operationalize  UNIT – II: Describing Data: Observations and Variables, Types of Variables, Central Tendency, Distribution of the Data, Confidence Intervals, Hypothesis Tests, Student t-test  UNIT – III: Preparing Data Tables: Cleaning the Data, Removing Observations and Variables, Generating Consistent Scales across Variables, New Frequency Distribution, Converting Text to Numbers, Converting Continuous Data to Categories, Combining Variables, Generating Groups, Preparing Unstructured Data UNIT - IV: Understanding Relationships: Visualizing
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DEEP LEARNING

DEEP LEARNING UNIT-I: Mathematical foundations of Deep Learning Scalars, Vectors, Matrices and Tensors, Multiplying Matrices and Vectors, Identity and Inverse Matrices, Linear dependence and span, Norms, Special kinds of matrices and vectors, Trace operations, Eigen value decomposition.  UNIT-II: Fundamentals of Deep Learning Anatomy of Neural Networks: Layers, Models, Loss functions and optimizers Training Deep Networks: Cost Functions, Optimizers Types of Deep Neural Networks. UNIT-III: Convolutional Neural Networks Motivation, Convolution Operation, Types of layers, Pooling, LENET5 Architecture.  UNIT-IV: Recurrent Neural Networks Architecture of traditional RNN, Types and applications of RNN, Variants of RNNs, Word Embedding using Word2vec  UNIT-V: Regularization and Autoencoders Regularization for Deep Learning: L1 and L2, Dropout, Data Augmentation, Early Stopping, Case study on MNIST data Autoencoders: Architecture, Implementation, Denoising Autoencoders, Sparse Autoencoders
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INTERNET OF EVERYTHING

 INTERNET OF EVERYTHING UNIT –I: Introduction to Internet of Things Introduction, Definition and Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT Enabled Technologies, IoT Levels and Deployment Templates. UNIT-II: Domain Specific IoTs Introduction, Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry, Health & Lifestyle. UNIT-III: IoT AND M2M Introduction, M2M, Difference between IoT and M2M, SDN and NFV for IoT IoT System Management: Need for IoT Systems Management, SNMP, NETCONF, YANG, YANG-NETCONF, NETOPEER.  UNIT-IV: IoT Physical Devices & Endpoints What is an IoT Device, Exemplary Device? Raspberry Pi, About the Board, Linux on Raspberry Pi, Raspberry Pi Interfaces (Serial, SPI, and I2C), Programming Raspberry Pi with Python, Other IoT Devices.  UNIT-V: IoT Physical Servers and Cloud Offerings Introduction to Cloud Storage Models & Communication APIs, WAMP - AutoBahn for IoT, Xively Cloud for IoT, Python W
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DESIGN AND ANALYSIS OF ALGORITHM

 DESIGN AND ANALYSIS OF ALGORITHM UNIT-1 Introduction: Algorithm definition, Specifications, Performance Analysis- Time Complexity, Space Complexity. Asymptotic Notations-Big-Oh, Omega, Theta.  Divide and Conquer: General Method, Binary Search, Finding Maximum and Minimum, Merge Sort, Quick sort, closest pair of points.  UNIT – II  The Greedy Method – General Method, Knapsack Problem, Job sequencing with deadlines, Minimum-cost spanning trees, Optimal storage on tapes, Single source shortest paths, Huffman coding.  UNIT – III  Dynamic Programming - General method, Multistage graph, All pairs shortest path, Single Source Shortest path, Optimal Binary search trees, 0/1 Knapsack, Reliability design, the travelling salesman problem.  UNIT - IV  Back tracking - The General Method, The 8-Queens Problem, Sum of subsets, Graph Coloring, Hamiltonian cycles. UNIT-V   Branch and Bound – General method, Job sequencing with deadlines –LC Branch and Bound, FIFO Branch and Bound and LIFO Branch an
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