02702nam 22005774a 450 991077700620332120230617041424.01-322-35355-70-231-50700-3(CKB)1000000000445342(EBL)909168(OCoLC)818856851(SSID)ssj0000183701(PQKBManifestationID)11154319(PQKBTitleCode)TC0000183701(PQKBWorkID)10194730(PQKB)11171468(MiAaPQ)EBC909168(Au-PeEL)EBL909168(CaPaEBR)ebr10183507(CaONFJC)MIL666637(EXLCZ)99100000000044534220031208d2004 uy 0engur|n|---|||||txtccrIraq between the two world wars[electronic resource] the militarist origins of tyranny /Reeva Spector SimonUpdated ed.New York Columbia University Pressc20041 online resource (257 p.)Description based upon print version of record.0-231-13215-8 0-231-13214-X Includes bibliographical references (p. [213]-230) and index.[ CONTENTS ]; ACKNOWLEDGMENTS; INTRODUCTION; I. The Creation of a State; II. The Officers, Germany, and Nationalism; III. The Officers in Iraq; IV. Education; V. The Army; VI. The Rashid 'Ali Coup; VII. Conclusion: Ideological Prelude to Tyranny; Appendixes; I. The Hashimites; II. Iraqi Cabinets 1921-1941; III. Biographical Sketches; Notes; Bibliography; IndexWhy did a group from the Iraqi army seize control of the government and wage a disastrous war against Great Britain, rejecting British and liberal values for those of a militaristic Germany? What impact did these actions have on the thirty-year regime of Saddam Hussein?Departing from previous studies explaining modern Iraqi history in terms of class theory, Reeva Simon shows that cultural and ideological factors played an equal, if not more important, role in shaping events. In 1921 the British created Iraq, and an entourage of ex-Ottoman army officers, the Sharifians, became the new ruling elNationalismIraqHistoryIraqHistoryHashemite Kingdom, 1921-1958IraqArmed ForcesPolitical activityNationalismHistory.956.704Simon Reeva S826126MiAaPQMiAaPQMiAaPQBOOK9910777006203321Iraq between the two world wars3696478UNINA00819nam0-2200265 --450 991077689470332120240201122709.020240201d1910----kmuy0itay5050 bafreengUNy 001yyQuatrieme commissioncommission pour la nomenclature et la classification des solsCommission pour l'europeB. FrosteursD. C. Glinka[S.l.]s.n.![1910?]XX, 320 p.25 cmSuoloClassificazione631.423itaFrosterus,Benj1585562Glinka,D. C1585563ITUNINAREICATUNIMARCBK9910776894703321A PAT 11111124/2024FAGBCFAGBCQuatrieme commission3870748UNINA03698nam 22007335 450 991052375160332120230820191722.03-030-85855-310.1007/978-3-030-85855-1(MiAaPQ)EBC6839019(Au-PeEL)EBL6839019(CKB)20275220200041(OCoLC)1291316403(DE-He213)978-3-030-85855-1(PPN)259387665(EXLCZ)992027522020004120211221d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDemand Prediction in Retail A Practical Guide to Leverage Data and Predictive Analytics /by Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (166 pages)Springer Series in Supply Chain Management,2365-6409 ;14Print version: Cohen, Maxime C. Demand Prediction in Retail Cham : Springer International Publishing AG,c2021 9783030858544 1. Introduction -- 2. Data Pre-Processing and Modeling Factors -- 3. Common Demand Prediction Methods -- 4. Tree-Based Methods -- 5. Clustering Techniques -- 6. Evaluation and Visualization -- 7. More Advanced Methods -- 8. Conclusion and Advanced Topics.From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.Springer Series in Supply Chain Management,2365-6409 ;14Sales managementBusiness logisticsProduction managementQuantitative researchRetail tradeData miningSales and DistributionSupply Chain ManagementOperations ManagementData Analysis and Big DataTrade and RetailData Mining and Knowledge DiscoverySales management.Business logistics.Production management.Quantitative research.Retail trade.Data mining.Sales and Distribution.Supply Chain Management.Operations Management.Data Analysis and Big Data.Trade and Retail.Data Mining and Knowledge Discovery.658.7Cohen Maxime C.1080231MiAaPQMiAaPQMiAaPQBOOK9910523751603321Demand Prediction in Retail2593131UNINA