Data Analytics (Elective-I)

Unit I

Introduction to Data Analytics: Sources and Nature of Data, Classification of Data (Structured, Semi-Structured, Unstructured), Characteristics of Data, Introduction to Big Data Platform, Need of Data Analytics, Evolution of Analytic Scalability, Analytic Process and Tools, Analysis vs Reporting, Modern Data Analytic Tools, Applications. Data Analytics Lifecycle: Need, Key Roles for Successful Analytic Projects, Phases — Discovery, Data Preparation, Model Planning, Model Building, Communicating Results, Operationalization.

Unit II

Data Analysis: Regression Modeling, Multivariate Analysis, Bayesian Modeling, Inference and Bayesian Networks, Support Vector and Kernel Methods, Analysis of Time Series — Linear Systems Analysis & Nonlinear Dynamics, Rule Induction, Neural Networks — Learning and Generalisation, Competitive Learning, Principal Component Analysis and Neural Networks, Fuzzy Logic — Extracting Fuzzy Models from Data, Fuzzy Decision Trees, Stochastic Search Methods.

Unit III

Mining Data Streams: Streams Concepts, Stream Data Model and Architecture, Stream Computing, Sampling Data in a Stream, Filtering Streams, Counting Distinct Elements, Estimating Moments, Counting Oneness in a Window, Decaying Window, Real-time Analytics Platform (RTAP) Applications, Case Studies — Real-Time Sentiment Analysis, Stock Market Predictions.

Unit IV

Frequent Itemsets and Clustering: Mining Frequent Itemsets, Market-Based Modelling, Apriori Algorithm, Handling Large Data Sets in Main Memory, Limited Pass Algorithm, Counting Frequent Itemsets in a Stream. Clustering Techniques: Hierarchical, K-Means, Clustering High-Dimensional Data, CLIQUE and ProCLUS, Frequent Pattern-Based Clustering, Clustering in Non-Euclidean Space, Clustering for Streams and Parallelism.

Unit V

Frameworks and Visualization: MapReduce, Hadoop, Pig, Hive, HBase, MapR, Sharding, NoSQL Databases, S3, Hadoop Distributed File System. Visualization: Visual Data Analysis Techniques, Interaction Techniques, Systems and Applications. Introduction to R: R GUIs, Data Import and Export, Attribute and Data Types, Descriptive Statistics, Exploratory Data Analysis, Visualization Before Analysis, Analytics for Unstructured Data.