Data Management and Analysis

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 of data analytics. Data Analytics Lifecycle: Need, key roles for successful analytic projects, Various phases of data analytics lifecycle – discovery, Data preparation, model planning, model building, Communicating results, operationalization

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, Principal component analysis and neural networks, Fuzzy logic: extracting fuzzy models from data, Fuzzy decision trees, Stochastic search methods.

Introduction to stream concepts, Stream data model and architecture, Stream computing, Sampling data in a stream, filtering streams, Counting distinct elements in a stream, Estimating moments, Counting oneness in a window, decaying window, Real-time Analytics Platform (RTAP) applications, Case studies – real time sentiment analysis, Stock market predictions

Mining frequent itemset, Market-based modelling, Apriori algorithm, Handling large data sets in main memory, Limited pas algorithm, Counting frequent itemset in a stream, Clustering techniques: hierarchical, K-means, Clustering high dimensional data, CLIQUE and ProCLUS, Frequent pattern-based clustering methods

Frame Works and Visualization: MapReduce, Hadoop, Pig, Hive, HBase, MapR, Sharding, NoSQL Databases, S3, Hadoop Distributed File Systems, Visualization: visual data analysis techniques, interaction techniques, systems and applications. Introduction to R – R graphical user interfaces, data import and export, attribute and data types, descriptive statistics, exploratory data analysis, visualization before analysis, and analytics for unstructured data. Textbooks 1. Data Analytics Made Accessible by Dr. Anil Maheshwari 2. Python for Everybody: Exploring Data by Dr. Charles Severance 3. Data Streams in Data Mining Simplified 101By: Nidhi B