CIS 8695 Big Data Analytics

Transcription

1 CIS 8695 Big Data Analytics Pre-Requisites: CSP 1-8, CIS 8040 Database Management Course Description The Big Data revolution is underway mainly because technology helps firms gather extremely detailed information from their consumers, suppliers, alliance partners, and competitors. Technology advancements now allow companies to capture and store large amount of data (or facts) in databases and data warehouses. However, often organizations are unable to provide access to right data to decision makers at the appropriate time because the data is gathered and maintained in organizational silos. The management and exploitation of 'big data's is among the top priorities of senior IT executives. Executives often are unable to integrate all the data that flows through the organization and find the information needed to make critical decisions. The purpose of executive information systems is to managers easily access relevant internal and external data for managing their organizations. The course provides IT professionals the knowledge and skills needed to redesign their processes and systems to enable managers deliver the right information to the right people at the right time. The course will take a case-study approach. The course will also use a variety of state-of-the-art software for the organization, analysis, and visualization of organizational data through executive information systems. Course objectives: Understand different roles of organizational data in various business domains Understand the key issues and solutions for the capture and storage of large amount of data (or facts) in databases and data warehouses Acquire working knowledge of several popular techniques for organizing, delivering and analyzing information to managers and executives Acquire a working knowledge of some popular data management, analytical and retrieval tools Learn about emerging tools and techniques for developing and implementing executive information systems Create commonly expected "deliverables" of an executive information systems project

2 Course Material Books: 1. Galit Shmueli, Nitin R Patel And Peter Bruce, Data Mining For Business Intelligence: Concepts, Techniques, and Applications in Microsoft Excel with XLMiner (India Edition), [Referred as SRB in the plan below] 2. Arvind Sathi, Big Data Analytics: Disruptive Technologies for Changing the Game [Referred to as Sathi in the plan below] 3. Foster Provost, Tom Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking [Referred to as PF in the plan below] : Session 0 Mathematics for Management Online Course: Statistics Section Instructions Mathematics for Management Online Course: Statistics Section Course Link Session 1 Data Scientist: The Sexiest Job of the 21st Century Evidence- Based Management (HBR On Point Enhanced Edition) Defying the Odds: Using Decision Analytics to Win Big in the Gaming Business Session 2 Decision Analytics: From Back Office to Center Stage Session 3 Appendix D: Pattern Directed Data Mining of Point-of-Sale Data Session 4 CASE STUDY Financial Market Analysis and Prediction LBS Capital Management Clearwater Florida Matchmaking With Math: How Analytics Beats Intuition to Win Customers Netflix Leading with Data: The Emergence of Data-Driven Video Session 5 CASE STUDY Improving Personnel Dispatching NYNEX Inc New York

3 The Secrets to Managing Business Analytics Projects Session 6 Advertising Analytics 2.0 CASE STUDY Help Desk Task Scheduling Moody's Investors Service New York Using Social Network Analysis to Improve Communities of Practice Session 7 How Big Data is Different You may not need Bug Data After All Big Data, Analytics and the Path From Insights to Value Big Data: The Management Revolution Session 8 Creating Business Value with Analytics Making Advanced Analytics Work for You Why IT Fumbles Analytics Course Outline Session Topic and Cases Assignments and Exams 1 2 Fundamentals of Data Analytic Thinking Session 1 Analytical Processes for PF: 1, 2 harnessing organizational Knowledge Competing on Analytics Session 2 with internal and external

4 processes PF: 3 3 d Introduction to Knowledge Discovery and Data Mining Session 3 SRB: 1,2 Group Assignment 1 Due Learning from Data using Neural networks SRB: 11 PF: 4,5 4 Learning from Data using Decision Trees Session 4 SRB: 9 Individual Assignment 1 Due Big Data Hadoop Overview 5 Association Rules Session 5 SRB: 13 Individual Assignment 2 Due Mid Term Exam Data Warehousing, OLAP Tools SRB: 3 6 Visualizing Performance Roadmap to Enhanced Analytic Capabilities PF: 7, 8 Session 6 Individual Assignment 3 Due Group Assignment 2 Due Managing

5 Analytical People Big Data Analytics Representing and Mining Text Sathi: The future of analytic competition PF: 11 Group Assignment 3 - Big Data Overview Presentation The architecture of business intelligence PF: 11, 12, 13 8 Business Intelligence in Practice Session 8 Comparing Techniques Final Exam

6 Assessment Criteria: Component Weightages Description Class participation 10 Mid Term Exam 15 Individual Assignment 1 10 Neural Networks & Linear Regression Individual Assignment 2 10 Hadoop Hands On Tutorial Individual Assignment 3 10 Market Basket Analysis Group Assignment 1 10 Application of Analytics Group Assignment 2 10 EIS Case Analysis Group Assignment 3 10 Big Data Overview (Presentation) End Term Exam 20 Policy on due dates, grammar & spelling requirements for assignments, late submissions, make-ups, participation requirements, and extra-credit o Late submission of final paper will result in a 10% reduction of your grade for your final paper. o Participation involves the student sharing insights and examples as necessary. It also entails being involved in the threaded discussions and exercises.