PPT-Finding Hidden Intelligence with Predictive Analysis of Data Mining

Rafal Lukawiecki Strategic Consultant Project Botticelli Ltd rafalprojectbotticellicom Objectives Show use of Microsoft SQL Server 2008 Analysis Services Data Mining Tantalise you with the power of DM. Business intelligence (BI). refers to all of the applications and technologies used to , provide access to, and information to efforts. June 2013. Wettbewerbsvorsprung durch . SAP . Predictive. . Analysis. Agenda. Introduction to Predictive Analysis. Use. Cases, Demo & Customers. Predictive Applications. High Performance Applications (on SAP HANA). Neil . Versel.  . ,. Information Week, November 15, 2011. Shipi. . Kankane. Prashanth. . Nakirekommula. Applying analytics and risk- management capabilities to health insurance through LexisNexis data platforms. . December 2013, Jakub Miarka, University of Leeds. RapidMiner. Formerly called . YALE. (Yet Another Language Environment). Environment for . machine learning, data and text mining, predictive and business analytics. Federal Big Data Working Group Meetup. November 3, 2014. Dave Vennergrund. Director Predictive Analytics and Data Science. David.Vennergrund@salienfed.com. 571 766 2757. Salient Data Analytics Center of Excellence. Agenda. Data Mining enabling Predictive Analysis. The Value of Predictive Analysis. SQL Server 2008 Predictive Analysis. Complete Predictive Analysis. Integrated Predictive Analysis. Extensible Predictive Analysis. CHAPTER OUTLINE. 12.1. Managers and Decision Making. 12.2. What Is Business Intelligence?. 12.3. Business Intelligence Applications for Data Analysis. 12.4. Business Intelligence Applications for Presenting Results. refers to all of the applications and technologies used to gather, provide access to, and analyze data and information to support decision-making efforts. Putting together all of the pieces of the puzzle. COSC 6335 Student Presentations on Nov. 17, 2011. [Group1. ] . Amalaman,Paul. . Koutoua. ; . Joshi,Sushil. ; . Kampalli. . Santhamurthy,Divya. . Durga. : . A Study on Data Pre-processing for Mining the Global Terrorism Database. Information Systems Development. Learning Objectives. Upon successful completion of this chapter, you will be able to:. Explain the difference between BI, Analytics, Data Marts and Big Data.. Define the characteristics of data for good decision making.. Unit-1. By: Prof. K.V. PAVAN KUMAR, . Methodist College of . Engg. & Tech.,. Abids. , Hyd. . 1.. . Business Intelligence - Definition. : . . The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. . M. Afdal, ST., . M.Kom. m.afdal@uin-suska.ac.id. Introduction :. Overall goals of this class. Students are able to analyze . digital business strategies using digital marketing . techniques such as SEO and SEM for the benefit of entrepreneurs.. Course webpage:. http://www.cs.bu.edu/~. evimaria/cs565-11.html. Schedule: Mon – Wed, . 2:30-4:00. Instructor: . Evimaria. . Terzi. , . evimaria@cs.bu.edu. Office hours: . Tues. . 11. :00am-12:30pm. Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.