How To Understand And Understand A Problem

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1 Lluis Belanche + Alfredo Vellido Intelligent Data Analysis and Data Mining or Data Analysis and Knowledge Discovery a.k.a. Data Mining II

2 Last sessions wrap up

3 CRISP: The virtuous loop of methodology phases

4 CRISP: Phases: Problem understanding PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION DETERMINE PROBLEM GOAL BACKGROUND PROBLEM GOALS SUCCESS CRITERIA ASSESS SITUATION INVENTORY RESOURCES REQUERIMS. ASSUMPTIONS LIMITATIONS RISKS CONTINGEN. TERMINOLOG. COSTS & BENEFITS DETERMINE DM GOALS GOALS DM SUCCESS CRITERIA DM PRODUCE PROJECT PLAN PROJECT PLAN INITIAL SELECTION OF TOOLS

5 DM application areas ( 10 > 11)

6 CRISP: Phases: Data understanding PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION OBTAIN INITIAL INITIAL REPORT DESCRIPTION EXPLORATION QUALITY VERIFICATION DESCRIPTIVE REPORT EXPLORATION REPORT QUALITY REPORT

7 METROFANG: a real story about data understanding (2) caudal entrada 350,00 300,00 250,00 200,00 150,00 100,00 50,00 0, Par motor Secador A 140,00 120,00 100,00 80,00 60,00 Missing data Stationality Outliers Time Series Weekend? FORUM??? 40,00 20,00 0, (Barcelona España)

8 CRISP: Phases: Data preparation PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION SELECTION ARGUMENTS FOR SELECTION CLEANING CLEANING REPORT RECONSTRUCT DERIVATED VARIABLES OSERVATIONS GENERATED INTEGRATE INTEGRATED FORMATTING WITH NEW FORMAT

9 Is data preparation that important?

10 How large is it? ( 13) Some fun facts: Google processes over 20 PB worth of data every day. Back in December 2007, YouTube generated 27 PB of traffic. The CERN Large Hadron Collider (HLC) generetes about 20 PB of usable data per year. The volume of global annual data traffic is expected exceed 60,000 PB in 2016, from 8,000 petabytes in 2011 In the next decade, astronomers expect to be processing 10 PB of data every hour from the Square Kilometre Array (SKA) telescope one exabyte every four days.

11 Data manipulation tools ( > 13)

12 CRISP: Phases: Modelling PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION SELECT MODELING TECHNIQUE SELECTED TECHNIQUE CREATE TEST DESIGN TEST DESIGN BUILD MODEL PARAMETER SELECTION MODEL MODEL DESCRIPTION VALIDATE MODEL MODEL VALIDATION

13 CRISP: Selection of techniques U N I V E R S E OF T E C H N I Q U E S (Definided by tools) TECHNIQUES SUITED TO A PROBLEM POLITICAL REQUIREMENTS (Business, executive) Money, time, hh.rr. LIMITATIONS Data types, knowledge SELECTED TOOL(S)

14 end of last session wrap up

15 Commonly used models/techniques ( 07)

16 Commonly used models/techniques ( 11)

17 CRISP: Phases: Evaluation PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION EVALUATE RESULTS EVALUATION OF DM RESULTS APPROVED MODELS REVISE PROCESSES REVISION OF THE PROCESS DETERMINE NEXT STEPS LIST OF POSSIBLE ACTIONS DECISSIONS Model results should be evaluated in the context of the problem objectives established in the first phase (problem understanding) This will lead to the identification of other needs, frequently reverting to prior phases of CRISP DM. Gaining problem understanding is an iterative procedure in DM, where the results show the user new relationships that provide a deeper understanding of the problem.

18 CRISP: Phases: Deployment PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION PLAN IMPLEMEN TATION IMPLEMENTATION PLAN PLAN MONITORIZATION & MAINTENANCE MONITORIZATION & MAINTENANCE PLAN FINAL REPORT PRODUCTION FINAL REPORT FINAL PRESENTATION PROJECT REVISION DOCUMENTATION OF EXPERIENCE Through the knowledge discovered in the earlier phases of the CRISP DM process, sound models can be obtained that may then be applied to address practical issues in the area of the problem. These models need to be monitored for changes in operating conditions, because what might be true today may not be true even in the near future. If significant changes do occur, the model should be reapprised.

19 How do you deploy it? ( 06 > 09)

20 Software popularity ( 07) Free vs. commercial: debate!

21 Software popularity ( 09)

22 Ouch! ( 10)

23 Consolidated new scene ( 12)

24 A note on CRISP-DM 2.0 CRISP-2.0: Updating the Methodology Why? Many changes have occurred in the business application of data mining since CRISP DM 1.0 was published. Emerging issues and requirements include: The availability of new types of data text, Web, and attitudinal data, for example along with new techniques for pre processing, analyzing, and combining them with related case data Integration and deployment of results with operational systems such as call centers and Web sites Far more demanding requirements for scalability and for deployment into real time environments The need to package analytical tasks for non analytical end users and integrate these tasks in business workflows The need to seamlessly integrate the deployment of results and closed loop feedback with existing business processes The need to mine large scale databases in situ, rather than exporting an analytical dataset Organizations increasing reliance on teams, making it important to educate greater numbers of people on the processes and best practices associated with data mining and predictive analytics In July 2006 the consortium announced that it was going to start the process of working towards a second version of CRISP DM. On 26 September 2006, the CRISP DM SIG met to discuss potential enhancements for CRISP DM 2.0 and the subsequent roadmap. However, these efforts appear to be stalled. The SIG has not met, updated the CRISP website, or communicated anything to members since early 2007.

25 Show me the money!

26 Mining jobs Company: Travelers Position: Predictive Modeling Director Location: Minnesota, US We are one of the leading insurance companies in the United States As a Director, you will employ the most advanced predictive modeling and data mining tools and methodologies in cutting edge research. You will manage strategic projects like next generation customer pricing plans, optimization of marketing practices, assessment of commission structures, and improvement in reserving practices. Work Experience * 7+ years of research, modeling or actuarial experience * Proven ability in statistical modeling and data mining techniques (GLM, clustering, decision trees, etc.) * Experience in preparing large, complex, and dirty datasets for analysis * Knowledge of insurance products and operations * Familiarity with SAS programming a plus Certificates/Degrees * College degree in statistics, mathematics, economics, finance, actuarial science, engineering or related field. Keywords * Statistical Analysis GLM Generalized Linear Models Math * Statistics Database Marketing SAS Actuary * Probability Predictive Modeling Time Series Regression * Economics S Plus Data Mining Data Analysis

27 Mining jobs Company: Microsoft adcenter Lab Position: Data Mining Analyst Location: Redmond, WA * Analyze a huge amount of data by using data mining and statistics techniques; generate actionable insights for improving advertising technology and systems. * Collaborate with other researchers in the lab on discovering problems in different areas where data mining/machine learning/statistics can help; act as an expert in the area of data mining/machine learning/statistics in the advertising technology field * Design and carry out experiments to evaluate different algorithms and their real impact on advertising business * Research and exploration in the areas of data mining, machine learning, and statistics. Qualifications: * Extensive knowledge and experience in data mining/machine learning/statistics/databases * Solid experience in very large real world data analysis * Experience/knowledge with various data analysis tools, data mining tools, and statistical packages such as R, SAS * Ability to explain statistical concepts to non statisticians. * Proficiency with databases and programming languages such as C#, Perl or F# * Master degree or PhD (preferred) in the area of data mining/machine learning/statistics is required.

28 Mining jobs Company: Dow AgroSciences Position: Computational Biologist Location: Indianapolis, US Dow AgroSciences, LLC, is a top tier agricultural company providing innovative crop protection, pest and vegetation management, seed, and agricultural biotechnology solutions to serve the world s growing population. Description Dow AgroSciences seeks a Computational Biologist to assist in the analyses of biological datasets. Key job responsibilities will include development and application of machine learning and statistical algorithms and software to mine biological datasets. Extensive development of software to integrate and automate data for mining is also expected. Topics of interest include bioinformatics, Bayesian networks, support vector machines, instance based algorithms, decision trees, neural networks, text mining, mixed models, spatial models, and time series modeling. Analysis of biological experiments, interpretation and presentation of results are also expected as well as to act as a guide for the ongoing research process. Qualifications: * Ph.D. in computer sciences, bioinformatics, computational biology, statistics or closely related field, is required. * An emphasis in machine learning is highly desired. * A solid foundation on statistics is preferred. * Demonstrated ability to write scientific papers is desirable.

29 Empleos! de minero Company: G2 Marketing Intelligence (WPP Group) Position: Senior Data Miner Location: Madrid * Position requires a demonstrated understanding and wide experience in CRM international projects, related with segmentation, modeling, relationship programs,... * Experience in experimental design and artificial intelligence * Position requires >5 years experience designing own methodologies and utilizing multiple learning techniques, such as, for instance, cluster analysis, discriminant analysis, optimization, decision trees and neural networks. * Experience with relational databases such as Oracle, SQLServer, and Data Marts/Data Warehouses. * Experience with tools as: SPSS, Clementine, SAS, MATLAB, Reporting and Analysis Services,... Qualifications: * Position requires a Master degree in Engineering, Mathematics, Statistics, or related IT, experimental studies. * High level of English language.

30 Mining jobs

31 Mining jobs socially

32 lists and groups The CS department at UC Santa Cruz invites applications for a tenured position at all levels in areas relevant to Big Data and Data Science broadly conceived, such as database management, machine learning, and data storage systems Please visit: 14.pdf for a full description and application instructions. To ensure full consideration, applications for this position, including letters of reference, must arrive by December 17, 2013

33 lists and groups Xerox Research Centre Europe (XRCE) and the Computer Vision Center (CVC) at the Universitat Autònoma de Barcelona (UAB) are seeking a candidate for a 3 year PhD on the domain of computer vision. The funded research will aim at exploring the use of synthetic data to tackle object detection and image classification challenges building on the strong competencies of CVC and XRCE in those aspects. The PhD student will be physically located at XRCE and will occasionally visit the CVC/UAB. We seek a motivated candidate with a university degree in computer science, telecommunications, mathematics, physics or similar discipline Moreover, a master degree in either Computer Vision, Machine Learning, Applied Mathematics or similar is also required. Criteria: High motivation for research. Capability of working in an autonomous way. Good mathematical understanding. Good programming skills (C, C++, MatLab, Python) start is expected by January 2014 (if possible, earlier). Candidates should send an e mail to adas.phdgrants@cvc.uab.es and joseantonio.rodriguez@xrce.xerox.com (subject: XRCE ADAS_CVC 2013). Applications are considered until October 1st, 2013.

34 Some (hopefully) useful resources

35

36

37

38 Some bibliography available at books.google.com: Data mining: practical machine learning tools and techniques I.H. Witten, E. Frank (2005) Data mining: concepts and techniques J. Han, M. Kamber (2006) Principles of data mining D. J. Hand, H. Mannila, P. Smyth (2001)

39 Some FREE SOFTWARE to know about

40 KEEL (.es)

41 WEKA (.nz)

42 RapidMiner (.us)

43 KNIME (.de)

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