IBM SPSS Modeler 18.1.1 Python Scripting and Automation Guide
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Date Created Size MB . تماس بگیر ید 09353344788
Name Software ( Search List Ctrl+F ) Date created Size MB برای سفارش هر یک از نرم افزارها با شماره 09123125449 - 09353344788 تماس بگ ریید . \1\ Simulia Abaqus 6.6.3 2013-06-10 435.07 Files: 1 Size: 456,200,192 Bytes (435.07 MB) \2\ Simulia Abaqus 6.7 EF 2013-06-10 1451.76 Files: 1 Size: 1,522,278,400 Bytes (1451.76 MB) \3\ Simulia Abaqus 6.7.1 2013-06-10 584.92 Files: 1 Size: 613,330,944 Bytes (584.92 MB) \4\ Simulia Abaqus 6.8.1 2013-06-10 3732.38 Files: 1 Size: 3,913,689,088 Bytes (3732.38 MB) \5\ Simulia Abaqus 6.9 EF1 2017-09-28 3411.59 Files: 1 Size: 3,577,307,136 Bytes (3411.59 MB) \6\ Simulia Abaqus 6.9 2013-06-10 2462.25 Simulia Abaqus Doc 6.9 2013-06-10 1853.34 Files: 2 Size: 4,525,230,080 Bytes (4315.60 MB) \7\ Simulia Abaqus 6.9.3 DVD 1 2013-06-11 2463.45 Simulia Abaqus 6.9.3 DVD 2 2013-06-11 1852.51 Files: 2 Size: 4,525,611,008 Bytes (4315.96 MB) \8\ Simulia Abaqus 6.10.1 With Documation 2017-09-28 3310.64 Files: 1 Size: 3,471,454,208 Bytes (3310.64 MB) \9\ Simulia Abaqus 6.10.1.5 2013-06-13 2197.95 Files: 1 Size: 2,304,712,704 Bytes (2197.95 MB) \10\ Simulia Abaqus 6.11 32BIT 2013-06-18 1162.57 Files: 1 Size: 1,219,045,376 Bytes (1162.57 MB) \11\ Simulia Abaqus 6.11 For CATIA V5-6R2012 2013-06-09 759.02 Files: 1 Size: 795,893,760 Bytes (759.02 MB) \12\ Simulia Abaqus 6.11.1 PR3 32-64BIT 2013-06-10 3514.38 Files: 1 Size: 3,685,099,520 Bytes (3514.38 MB) \13\ Simulia Abaqus 6.11.3 2013-06-09 3529.41 Files: 1 Size: 3,700,856,832 Bytes (3529.41 MB) \14\ Simulia Abaqus 6.12.1 2013-06-10 3166.30 Files: 1 Size: 3,320,102,912 Bytes -
Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi. -
International Business Machines Corporation a New York Corporation 1016331 International Business Machines Corporation a New York Corporation
International Business Trademarks Matching '"International Bu...' by Owner. IPMonitorTrademarks www.ipmonitor.com.au Contents Alerts 3 "International Business" 3 Terms and Conditions 26 General 26 Disclaimer of warranty and limitation of liability 26 Copyright 26 Arbitration 26 www.ipmonitor.com.au Alerts "International Business" 479 results matching '"International B...' by Owner. Number Mark Owner 1033728 IBM POWER International Business Machines Corporation a New York corporation 1016331 International Business Machines Corporation a New York corporation 1016332 International Business Machines Corporation a New York corporation 1011021 OPENCHIP International Business Machines Corporation a New York corporation 1036316 SYMMETRY International Business Machines Corporation a New York corporation 617766 MQSeries International Business Machines Corporation 1015285 OMNIFIND International Business Machines Corporation a New York corporation 1023800 EXPRESS PORTFOLIO International Business Machines Corporation a New York corporation 1024020 THINK EXPRESS International Business Machines Corporation a New York corporation 619418 International Business Machines Corporation 1037390 CATENA International Business Machines Corporation a New York corporation 1037391 CERULEAN International Business Machines Corporation a New York corporation 1037536 GLOBAL INNOVATION OUTLOOK International Business Machines Corporation a New York corporation 1037256 UNICA INTERNATIONAL BUSINESS MACHINES CORPORATION 1020234 International Business Network Pty Ltd ACN/ARBN -
Overview-Of-Statistical-Analytics-And
Brief Overview of Statistical Analytics and Machine Learning tools for Data Scientists Tom Breur 17 January 2017 It is often said that Excel is the most commonly used analytics tool, and that is hard to argue with: it has a Billion users worldwide. Although not everybody thinks of Excel as a Data Science tool, it certainly is often used for “data discovery”, and can be used for many other tasks, too. There are two “old school” tools, SPSS and SAS, that were founded in 1968 and 1976 respectively. These products have been the hallmark of statistics. Both had early offerings of data mining suites (Clementine, now called IBM SPSS Modeler, and SAS Enterprise Miner) and both are still used widely in Data Science, today. They have evolved from a command line only interface, to more user-friendly graphic user interfaces. What they also share in common is that in the core SPSS and SAS are really COBOL dialects and are therefore characterized by row- based processing. That doesn’t make them inherently good or bad, but it is principally different from set-based operations that many other tools offer nowadays. Similar in functionality to the traditional leaders SAS and SPSS have been JMP and Statistica. Both remarkably user-friendly tools with broad data mining and machine learning capabilities. JMP is, and always has been, a fully owned daughter company of SAS, and only came to the fore when hardware became more powerful. Its initial “handicap” of storing all data in RAM was once a severe limitation, but since computers now have large enough internal memory for most data sets, its computational power and intuitive GUI hold their own. -
Start Something BIG!
Start Something BIG! Cool projects IBM offers a world of opportunity with Work on challenging unlimited challenges and endless projects in leading possibilities to start something BIG. If technology areas and hot business trends. you are creative, passionate, and Feature articles: willing to collaborate to transform the Innovative teams way customers do business, the IBM Extreme Blue alumni Intensity, passion, Extreme Blue internship experience comment on their challenge, fun, optimism is for you. Extreme Blue and no boundaries experience. characterize the Extreme The IBM Extreme Blue program is Blue diverse high designed to provide you - one of the Jason Jho performance team spirit. best students from around the world - 2001 Cambridge a memorable internship experience. Alumni Our program draws from the wealth Dynamic places of resources that only IBM can Desired laboratories Daniel Rabinovitz provide, combining challenging carefully selected in 2000 Extreme Blue projects with competitive hotbeds of technology and Alumni compensation, unparalleled technical innovation. expertise and a leading-edge work Royi Ronen and living environment. Recruiting status 2001 Haifa Alumni You will experience the fusion of European labs business and technology in this IBM’s mission challenging internship program. Now underway. You can still apply today if you are interested in applying for one of the European labs. At IBM, we strive to lead However, if you are attending school in the creation, The Netherlands in the United States, note that the development and European schedule starts and ends manufacture of the industry's most advanced New Extreme Blue Lab, Amsterdam several weeks later that may impact information technologies, / Uithoorn, Netherlands. -
GPL Reference Guide for IBM SPSS Statistics
GPL Reference Guide for IBM SPSS Statistics Note Before using this information and the product it supports, read the information in “Notices” on page 415. Product Information This edition applies to version 162261647161, release 0, modification 0 of IBM SPSS Modeler BatchIBM SPSS ModelerIBM SPSS Modeler ServerIBM SPSS StatisticsIBM SPSS Statistics ServerIBM SPSS AmosIBM SPSS SmartreaderIBM SPSS Statistics Base Integrated Student EditionIBM SPSS Collaboration and Deployment ServicesIBM SPSS Visualization DesignerIBM SPSS Modeler Text AnalyticsIBM SPSS Text Analytics for Surveys IBM Analytical Decision ManagementIBM SPSS Modeler Social Network AnalysisIBM SPSS Analytic Server and to all subsequent releases and modifications until otherwise indicated in new editions. Contents Chapter 1. Introduction to GPL .....1 color.hue Function (For GPL Guides) .....90 The Basics ...............1 color.saturation Function (For GPL Graphic GPL Syntax Rules.............3 Elements) ..............90 GPL Concepts ..............3 color.saturation Function (For GPL Guides) . 91 Brief Overview of GPL Algebra .......3 csvSource Function ...........92 How Coordinates and the GPL Algebra Interact . 6 dataMaximum Function .........92 Common Tasks .............14 dataMinimum Function .........93 How to Add Stacking to a Graph ......14 delta Function ............93 How to Add Faceting (Paneling) to a Graph . 15 density.beta Function ..........93 How to Add Clustering to a Graph .....17 density.chiSquare Function ........96 How to Use Aesthetics .........18 density.exponential -
CONNECTEDO Received a Second Place Award in ACM Competition
Kudos Matthew Vail and Qingfent (Frank) He received CISCO schol- ED arships. Also, Jack Frink received ECT an award for developing a new NN software tool, and Lucas Layman CCONNECTEDO received a second place award in ACM Competition. [See page 4.] Nader Moussa, a triple-major A NEWSLETTER FROM THE senior, was an IBM Extreme Blue DEPARTMENT OF COMPUTER SCIENCE intern last fall. MARCH 2005 Bensong Chen, doctoral stu- dent, was named an Outstanding Teaching Assistant by NC State’s Students' Wolfgrid tests strengths Graduate Student Association. Neha Jain, Tyler Johnson and of grid computing software Matthew Vail, all seniors, received CRA’s 2005 Outstanding Under- What if you had a bunch of graduate Award honorable men- computers all over the world, tions. hooked together? That’s a Senior Rich Killian is serving as the Microsoft Ambassador at question that Sammie Carter, NC State for the 2004-05 aca- computer science senior, and demic year. Jon Harris, graduate student Carol Allen, administrative as- in the College of Design, had sistant in the undergraduate advis- in mind when they began to ing offi ce, was recognized for 25 years of service at the university’s build the Wolfgrid across the staff recognition program last NC State community. August. She has been with the Using Apple’s new Xgrid computer science department for software, the two have been her entire NC State career. Dr. Peng Ning, assistant pro- hooking up personal com- Sammie Carter, computer science student, discusses the Wolfgrid with fessor, received an NSF CAREER puters across campus, creating Everette Allen, computing consultant. -
Used.Html#Sbm Mv
http://www.kdnuggets.com/2015/05/poll-analytics-data-mining-data-science-software- used.html#sbm_mv Vote in KDnuggets 16th Annual Poll: What Analytics, Data Mining, Data Science software/tools you used in the past 12 months for a real project?. We will clean and analyze the results and publish our trend analysis afterward What Analytics, Data Mining, Data Science software/tools you used in the past 12 months for a real project (not just evaluation)? 4 categories: Platforms, Languages, Deep Learning, Hadoop. Analytics / Data Mining Platforms / Suites: Actian Alpine Data Labs Alteryx Angoss Amazon Machine Learning Ayasdi BayesiaLab BigML Birst C4.5/C5.0/See5 Datameer Dataiku Dato (former Graphlab) Dell (including Statistica) FICO Model Builder Gnu Octave GoodData H2O (0xdata) IBM Cognos IBM SPSS Modeler IBM SPSS Statistics IBM Watson Analytics JMP KNIME Lavastorm Lexalytics MATLAB Mathematica Megaputer Polyanalyst/TextAnalyst MetaMind Microsoft Azure ML Microsoft SQL Server Microsoft Power BI MicroStrategy Miner3D Ontotext Oracle Data Miner Orange Pentaho Predixion Software QlikView RapidInsight/Veera RapidMiner Rattle Revolution Analytics (now part of Microsoft) SAP (including former KXEN) SAS Enterprise Miner Salford SPM/CART/Random Forests/MARS/TreeNet scikit-learn Skytree SiSense Splunk/ Hunk Stata TIBCO Spotfire Tableau Vowpal Wabbit Weka WordStat XLSTAT for Excel Zementis Other free analytics/data mining tools Other paid analytics/data mining/data science software Languages / low-level tools: C/C++ Clojure Excel F# Java Julia Lisp Perl Python R Ruby SAS base SQL Scala Unix shell/awk/gawk WPS: World Programming System Other programming languages Deep Learning tools Caffe Cuda-convnet Deeplearning4j Torch Theano Pylearn2 Other Deep Learning tools Hadoop-based Analytics-related tools Hadoop HBase Hive Pig Spark Mahout MLlib SQL on Hadoop tools Other Hadoop/HDFS-based tools. -
IBM SPSS Modeler Data Sheet
IBM Analytics IBM SPSS Modeler Data sheet IBM SPSS Modeler Accelerate time to value with visual data science and machine learning Overview Highlights In a business environment, the main objective of analytics is to improve a business outcome. These outcomes can include: • Exploit the power of visual and programmatic data science with IBM® SPSS® Modeler for IBM Data • Acquiring and retaining customers Science Experience, or get started with • Driving operational efficiency a subscription standalone deployment • Minimizing and mitigating fraud of IBM SPSS Modeler • Optimizing hiring processes and reducing attrition • Harness the value and find patterns in more data sources including text, flat • Creating new business models files, databases, data warehouses and Hadoop distributions in a multi-cloud When you apply data science and machine learning to improve a environment decision, the result is likely to be a better outcome. • Put 40+ out-of-box machine learning algorithms to work for ease of model development and management Data science is the process of using analytical techniques to uncover patterns in data and applying the results for business value. Descriptive • Integrate with Apache Spark for fast in-memory computing analysis, predictive modeling, text analytics, geospatial analytics, entity • Optimize productivity of data science analytics, decision management and optimization are used to identify and business teams with programmatic, patterns and deploy predictive models into operational systems. visual and other skills Systems and people can use these patterns and models to derive insights • Speed data analysis with in-database that enable them to consistently make the right decision at the point of performance and minimized data impact. -
Statistický Software
Statistický software 1 Software AcaStat GAUSS MRDCL RATS StatsDirect ADaMSoft GAUSS NCSS RKWard[4] Statistix Analyse-it GenStat OpenEpi SalStat SYSTAT The ASReml GoldenHelix Origin SAS Unscrambler Oxprogramming Auguri gretl language SOCR UNISTAT BioStat JMP OxMetrics Stata VisualStat BrightStat MacAnova Origin Statgraphics Winpepi Dataplot Mathematica Partek STATISTICA WinSPC EasyReg Matlab Primer StatIt XLStat EpiInfo MedCalc PSPP StatPlus XploRe EViews modelQED R SPlus Excel Minitab R Commander[4] SPSS 2 Data miningovýsoftware n Cca 20 až30 dodavatelů n Hlavníhráči na trhu: n Clementine, IBM SPSS Modeler n IBM’s Intelligent Miner, (PASW Modeler) n SGI’sMineSet, n SAS’s Enterprise Miner. n Řada vestavěných produktů: n fraud detection: n electronic commerce applications, n health care, n customer relationship management 3 Software-SAS n : www.sas.com 4 SAS n Společnost SAS Institute n Vznik 1976 v univerzitním prostředí n Dnes:největšísoukromásoftwarováspolečnost na světě (více než11.000 zaměstnanců) n přes 45.000 instalací n cca 9 milionů uživatelů ve 118 zemích n v USA okolo 1.000 akademických zákazníků (SAS používávětšina vyšších a vysokých škol a výzkumných pracovišť) 5 SAS 6 SAS 7 SAS q Statistickáanalýza: Ø Popisnástatistika Ø Analýza kontingenčních (frekvenčních) tabulek Ø Regresní, korelační, kovariančníanalýza Ø Logistickáregrese Ø Analýza rozptylu Ø Testováníhypotéz Ø Diskriminačníanalýza Ø Shlukováanalýza Ø Analýza přežití Ø … 8 SAS q Analýza časových řad: Ø Regresnímodely Ø Modely se sezónními faktory Ø Autoregresnímodely Ø -
Stručný Návod K Ovládání IBM SPSS Statistics a IBM SPSS Modeler Autor Doc
STRUČNÝ NÁVOD K OVLÁDÁNÍ IBM SPSS Statistics 19 a IBM SPSS Modeler 14 Pavel PETR Ústav systémového inženýrství a informatiky Fakulta ekonomicko – správní UNIVERZITA PARDUBICE 2012 Obsah 1. Základní informace o programu IBM SPSS Statistics .............................................. 4 1.1. Základní moduly IBM SPSS Statistics 19 ........................................................ 4 2. Program IBM SPSS Statistics ................................................................................... 6 2.1. Prostředí programu IBM SPSS Statistics.......................................................... 6 2.2. Okna v programu IBM SPSS Statistics ............................................................ 8 2.2.1. Datové okno .............................................................................................. 8 2.2.2. Výstupové okno ...................................................................................... 13 2.3. Základní ovládání programu IBM SPSS Statistics ......................................... 19 2.3.1. Soubor (File) ........................................................................................... 20 2.3.2. Úpravy (Edit) .......................................................................................... 22 2.3.3. Pohled (View) ......................................................................................... 24 3. Základní informace o programu IBM SPSS Modeler ............................................ 26 3.1. Fáze CRISP-DM ............................................................................................ -
Comparison of Commonly Used Statistics Package Programs
Black Sea Journal of Engineering and Science 2(1): 26-32 (2019) Black Sea Journal of Engineering and Science Open Access Journal e-ISSN: 2619-8991 BSPublishers Review Volume 2 - Issue 1: 26-32 / January 2019 COMPARISON OF COMMONLY USED STATISTICS PACKAGE PROGRAMS Ömer Faruk KARAOKUR1*, Fahrettin KAYA2, Esra YAVUZ1, Aysel YENİPINAR1 1Kahramanmaraş Sütçü Imam University, Faculty of Agriculture, Department of Animal Science, 46040, Onikişubat, Kahramanmaraş, Turkey 2Kahramanmaraş Sütçü Imam University, Andırın Vocational School, Computer Technology Department, 46410, Andırın, Kahramanmaras, Turkey. Received: October 17, 2018; Accepted: December 04, 2018; Published: January 01, 2019 Abstract The specialized computer programs used in the collection, organization, analysis, interpretation and presentation of the data are known as statistical software. Descriptive statistics and inferential statistics are two main statistical methodologies in some of the software used in data analysis. Descriptive statistics summarizes data in a sample using indices such as mean or standard deviation. Inferential statistics draws conclusions that are subject to random variables such as observational errors and sampling variation. In this study, statistical software used in data analysis is examined under two main headings as open source (free) and licensed (paid). For this purpose, 5 most commonly used software were selected from each groups. Statistical analyzes and analysis outputs of this selected software have been examined comparatively. As a result of this study, the features of licensed and unlicensed programs are presented to the researchers in a comparative way. Keywords: Statistical software, Statistical analysis, Data analysis *Corresponding author: Kahramanmaraş Sütçü Imam University, Faculty of Agriculture, Department of Animal Science, 46040, Onikişubat, Kahramanmaraş, Turkey E mail: [email protected] (Ö.