Introduction to Social Media: World Wide Web, Web 1.0, Web 2.0, Web 3.0, Social Media, Core Features of Social Media, Types of Social Media, Social Networking Sites, Use of Facebook for Business Purposes, Content Communities. Traditional business analytics, seven layers of social media analytics, types of social media analytics, social media analytics cycle, social media analytics challenges, social media analytics tools. Social Media Text Analysis: Types of Social Media Text, Purpose of Text Analysis, Steps in Text Analysis, Social Media Text Analysis Tools.
Social Media Action Analytics: Introduction to Action Analytics, Common Social Media Actions, Action Analytics Tools. Social media hyperlink analysis: Types of hyperlinks, hyperlink analytics, types of hyperlink analytics, hyperlink analysis tools. Seven Layers of Social Media Analytics Mining Business Insights from Social Media Text, Actions, Networks, Hyperlinks, Apps, Search Engine and Location Data By Gohar F.
Social Media Analytics: Techniques and Insights to Extract Business Value from Social Media By Matthew Ganis, Avinash Kohirkar, Pearson Education. The aim of this course is to provide the students with the knowledge of Big data Analytics principles and techniques. This course is also designed to give an exposure to the frontiers of Big data Analytics.
Ability to explain the foundations, definitions and challenges of Big Data and various analytical tools. History of Data Management - Evolution of Big Data, Structuring Big Data, Elements of Big Data, Big Data Analytics, Careers in Big Data, Future of Big Data. Technologies for handling big data: distributed and parallel computing for big data, introduction of Hadoop, cloud computing and big data, in-memory computing technology for big data.
Understanding Hadoop Ecosystem: Hadoop Ecosystem, Hadoop Distributed File System, MapReduce, Hadoop YARN, Hbase, Hive, Pig and Pig Latin, Sqoop, ZooKeeper, Flume, Oozie Understanding MapReduce Fundamentals and Hbase: The MapReduce Framework, MapReduces from MapReduce , Role of HBase in Big Data Processing. Understanding analytics and big data: Comparing reporting and analytics, types of analytics, points to consider during analytics, developing an analytics team, understanding text analytics analytics approaches and tools to analyze data: analytics approaches, history of analytics tools . Social Media Analytics and Text Mining: Introduction to Social Media, Introduction to Key Elements of Social Media, Introduction to Text Mining, Understanding Text Mining Process, Sentiment Analysis, Performing Social Media Analysis and Opinion.
Big Data Analytics: Disruptive Technologies for Changing the Game, Arvind Sathi, 1st Edition, IBM Corporation, 2012. To learn the basics of BlockChain and different types of block chain and consensus mechanism. Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and.
Taming the Tide of Big Data: Finding Opportunities in Big Data Streams with Advanced Analytics, Bill Franks, 1st Edition, Wiley and SAS Business Series, 2012.
Resources
Create and manage shared folders using operating system, the importance of the forensic mindset, define the law enforcement workload, Explain what the normal case will look like, Define who should be notified of a crime, parts of evidence collection, Define and apply probable cause. A brief history of dialogue systems, contemporary dialogue systems, modeling conversational dialogue systems, design and development of dialogue systems. Rule-based dialog systems: architecture, methods and tools: A typical dialog system architecture, design of a dialog system, tools for dialog system development, rule-based techniques in dialog systems participating in the Alexa award.
Statistical data-driven dialog systems: motivation of the statistical data-driven approach, dialog components in the statistical data-driven approach, reinforcement learning (RL), representation of dialog as a Markov decision process, from MDPs to POMDPs, dialog state tracking, dialog policy, Problems and issues with reinforcement learning in POMDPs. Evaluating dialog systems: How to perform the evaluation, evaluating task-oriented dialog systems, evaluating open-domain dialog systems, evaluation frameworks- PARADYS, quality of experience (QoE), interaction quality, best way to evaluate dialog systems. End-to-End Neural Dialogue Systems: Neural Network Approaches to Dialogue Modeling, A Neural Conversational Model, Introduction to the Technology of Neural Dialogue, Retrieval-Based Response Generation, Task-Oriented Neural Dialogue Systems, Open-Domain Neural Dialogue Systems, Some Issues and Current Solutions, Dialogue Systems: Datasets, Competitions , tasks and challenges.
Michael McTear, “Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots,” Second Edition, Moran and Claypool Publishers, 2020. Establishing essential coverage of service-oriented architecture models and the underlying design paradigm, along with documentation of the methodology. Understanding Service Orientation: Introduction to Service Orientation, Problems Solved by Service Orientation, Effects of Service Orientation on the Business, Objectives and Benefits of Service Oriented Computing, Four Pillars of Service Orientation.
Understanding SO Architectures: Introduction to SOA, Four Characteristics of SOA, Four Common Types of SOA, Service Orientation and SOA End Result, SOA Project and Life Cycle Phases. Greatest Common Divisors and Prime Factorization: Greatest Common Divisors, Euclidean Algorithm, Fundamental Theorem of Arithmetic, Factorization of Integers and Fermat Numbers Congruences: Introduction to Congruences, Linear Congruences, Chinese Remainder Theorem, Systems of Congruences linear. Simple Linear Regression and Correlation: Introduction to Linear Regression, Simple Linear Regression Model, Least Squares and Fitted Model, Properties of Least Squares Estimators, Inference About Regression Coefficients, Prediction, Simple Regression linear Case Study and Random Variability: Concept of a random variable, discrete probability distribution, continuous probability distribution, statistical independence.
Continuous probability distributions: normal distribution, areas under the normal curve, applications of normal distribution, normal binomial approximation, basic sampling distributions: random sampling, sampling distributions, sampling, distribution of means and central limit theorem, S2 sampling distribution, t-distribution, F distribution. Data Visualization-I: Introduction to data visualization, techniques used to visualize data, types of data visualization, applications of data visualization, big data visualization, tools used in data visualization, Tableau products. Data Visualization with Tableau (Data Visualization-II): Introduction to Tableau Software, Tableau Desktop Workspace, Data Analysis in Tableau Public, Using Visual Controls in Tableau Public.
Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Business, Michael Minelli, Michehe Chambers, Edition 1, Ambiga Dhiraj, Wiley CIO Series, 2013. Genetic Algorithms: Copying Nature Approaches: A Look at the Biological Genetic World, (GA), Importance of Genetic Operators, Termination Parameters, Niching and Speciation, Evolutionary Neural Networks, Theoretical Grounding, Ant Algorithms.
