18 Useful Mobile Apps for Data Scientist / Data Analysts. This article was posted by Manish Saraswat.
Free Alternatives to Excel for Data Cleaning. INFOGRAPHIC: How To Choose A Statistical Model. Most Viewed Posts Tag Cloud Agile BIAmazonArtificial IntelligenceBankingBest PracticesBig DataBusiness AnalyticsBusiness IntelligenceCIOCloudClouderaCMOCRMData GovernanceData LakeData MiningData ScienceData ScientistData VisualizationData WarehouseFacebookFast DataFinanceForresterGartnerGoogleHadoopHealthcareHRIBMIoTJeffrey StricklandMachine LearningManufacturingMarketingMicrosoftMobile BINetflixNoSQLOraclePredictive AnalyticsQlikRRetailSAPSelf-Service BISMBSparkTableauTIBCO Archives Infographics INFOGRAPHIC: How To Choose A Statistical Model.
【使ってみよう! Watson Analytics-その①】Watson Analyticsのはじめ方. Www.cs.uvm.edu/~icdm/algorithms/CandidateList.shtml. Classification ============== #1.
C4.5 Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc. Google Scholar Count in October 2006: 6907 #2. Top 10 Machine Learning Algorithms. This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms?
Must read books for Analysts (or people interested in Analytics) One of the ways I continue my learning is reading.
I read for 30 minutes before hitting the bed every day. This not only makes sure that I learn some thing daily, but also ends my day in a fulfilling manner. Over the years, I have read a variety of books on various subjects. In this article, I will share a list of 7 must read books, which I think should be present in every Analyst’s bookshelf. I believe that each and every book listed below has helped me learn about Analytics and expect that they will immensely help people who want to learn about this field. 100 Savvy Sites on Statistics and Quantitative Analysis. Nate Silver’s unprecedented accurate prediction of state-by-state election results in the most recent presidential race was a watershed moment for the public awareness of statistics.
While data gathering and analysis has become a massive industry in the past decade, it hasn’t always been as well covered in the press or publicly accessible as it is now. With more and more of our daily interactions being mediated through computers and the internet, it is easier than ever to gather detailed quantitative data and do statistical analysis on that data derive valuable information and predictions from it. Knowledge of statistics and quantitative analysis techniques is more valuable than ever. From biostatisticians to politicians and economists, people in every field are using statistics to further their careers and knowledge. These sites are some of the most useful, informative, and comprehensive on the web covering stats and quantitative analysis. 電子図書システム. 確率論及統計論 確率論及統計論 著者: 伏見 康治 著.
Tableau Software Expands Big Data Capabilities; Launches Japanese Version. Tableau Software, the global leader in rapid-fire business intelligence (BI) software, today announced direct access to the MapR Distribution for Apache Hadoop for big data analytics.
Tableau’s MapR support is included in the general availability of Tableau 7.0.7, providing customers with the ability to instantly create reports, data visualizations and dashboards without any programming or coding. The product update also includes the availability of Tableau’s Desktop and Server products in Japanese. With just a couple of clicks, Tableau natively connects to MapR’s open, enterprise-grade distribution for Apache Hadoop. Forrester Wave: Big Data Predictive Analytics Solutions, Q1 2013.
Forrester Wave evaluates Big Data Predictive Analytics solutions from Angoss Software, IBM, KXEN, Oracle, Revolution Analytics, Salford Systems, SAP, SAS, StatSoft, and Tibco Software and names SAS and IBM as leaders.
Forrester Research has published a big report evaluating Big Data Predictive Analytics Solutions for Q1 2013. The report, written by Mike Gualtieri with Stephen Powers and Vivian Brown, used 51 criteria to evaluate solutions from 10 companies: Angoss Software, IBM, KXEN, Oracle, Revolution Analytics, Salford Systems, SAP, SAS, StatSoft, and Tibco Software. Forrester ranks SAS and IBM as strong leaders, and puts SAP, a newcomer to Predictive Analytics, in the third place.
SAS Enterprise Miner tool is praised as easy to learn and able to run analysis in-database or on distributed clusters to handle big data. Ayasdi: Stanford Math Begets a Data Company. Like most of his peers, Gunnar Carlsson spends his time thinking about hairy, theoretical math problems.
It’s ivory tower stuff—he’s been a math professor for 30 years—which is just how the people in his field like it. “Mathematicians want to work on the deepest, hardest problems and get interesting intellectual results,” he says. Soft10ware. History about 24 analytic software over the last 30 years. Minitab ホーム（統計解析ソフトウェア） 研究に有用なソフトウェア-統計ソフト. 「医薬研究者のための統計ソフトの選び方 」【bk1】【amazon.co.jp】を参考にして、現在よく使われている統計解析ソフトをまとめてみました。 ※使用論文数はBMJ, JCI, JPET, PNAS, JAPに掲載された論文のうち該当ソフトが使われている論文数。。 ※(A)はアカデミック価格。 StatView ( 英語版、日本語版） 統計解析の定番ソフトですが、残念ながら、Statview 5を最後に、2002年12月末で生産中止になってしまいました。
解説本もいろいろ発売されています。 Statview関連書籍 SPSS ( 英語版、日本語版) Windows版の統計解析ソフトではもっとも使われているソフトです。 SPSS関連書籍 Prism (Graphpad社 、（有）エムデーエフ) Graphpad PrismはUCSDの薬学研究者であったMotulsky博士が作った統計ソフト、医学生物学における統計解析にフォーカスしているため、生存率解析などの統計解析がそろっています。 統計ソフトはどれもそうなのですが各ソフトによって手順や操作性が異なります。 一つだけ、とても気になったのは、多重比較をおこなった際にp値が<0.05と表示されるだけで、実際のp値が出力されないという点です。 昨年、アップル-MedicalでPrismが取り上げられましたので、是非読んでみて下さい。 Bi_tool_evaluation%20V4_jp3. 統計ソフトEZR (自治医大さいたま医療センター血液科) ******** Click here for English version ********
医学統計解析ソフトウェア_GraphPad InStat. 10 most popular analytic tools in business. Business analytics is a fast growing field and there are many tools available in the market to serve the needs of organizations. The range of analytical software goes from relatively simple statistical tools in spreadsheets (ex-MS Excel) to statistical software packages (ex-KXEN, Statistica) to sophisticated business intelligence suites (ex-SAS, Oracle, SAP, IBM among the big players).
Open source tools like R and Weka are also gaining popularity. Besides these, companies develop in-house tools designed for specific purposes. Here is a list of 10 most popular analytic tools used in the business world. Commercial software MS Excel: Almost every business user has access to MS Office suite and Excel. While most people are aware of its excellent reporting and graphing abilities, excel can be a powerful analytic tool in the hands of an experienced user. SPSS Modeler (Clementine): SPSS Modeler is a data mining software tool by SPSS Inc., an IBM company. Software in Analytics – the Changing Game. We have discussed in an earlier post how the explosion of data (especially in terms of new variables) is leading to a huge requirement of Analytics professionals who can work on data mining and Visualisation / dash boarding.
They say that a picture says more than a 1000 words. And the world of Analytics surely seems to be following this. We know that there are a large number of SaaS (software as a service) today which make all aspects of analytics much easier to execute than ever before. RapidMinerを日本語で表示する - Rapid-I データマイニングブログ【KSKアナリティクス】 Bayesia SAS. 10 Business Intelligence Trends for 2012. Rapid-I.