01970nam 2200541 450 991070211420332120130115145314.0(CKB)5470000002425000(OCoLC)824454761(EXLCZ)99547000000242500020130115d2012 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierComputational work to support FAP/SRW variable-speed power-turbine development /Ali A. AmeriCleveland, Ohio :National Aeronautics and Space Administration, Glenn Research Center,[2012]1 online resource (14 pages) color illustrationsNASA contractor report ;NASA-CR 2012-217434Title from title screen (viewed Jan. 15, 2013)."April 2012.""This work was sponsored by the Fundamental Aeronautics Program at the NASA Glenn Research Center."Includes bibliographical references (pages 13-14).Heat transfernasatK-omega turbulence modelnasatLow Reynolds numbernasatTurbine bladesnasatMathematical modelsnasatComputational fluid dynamicsnasatKinetic energynasatHeat transfer.K-omega turbulence model.Low Reynolds number.Turbine blades.Mathematical models.Computational fluid dynamics.Kinetic energy.Ameri Ali A.1384587NASA Glenn Research Center,Fundamental Aeronautics Program (U.S.),United States.National Aeronautics and Space Administration,GPOGPOBOOK9910702114203321Computational work to support FAP3511825UNINA03408nam 22005415 450 991030074510332120200702025737.09781484235973148423597510.1007/978-1-4842-3597-3(CKB)4100000004243395(DE-He213)978-1-4842-3597-3(MiAaPQ)EBC5390249(CaSebORM)9781484235973(PPN)227406982(OCoLC)1039099557(OCoLC)on1039099557(EXLCZ)99410000000424339520180510d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierData Science Fundamentals for Python and MongoDB /by David Paper1st ed. 2018.Berkeley, CA :Apress :Imprint: Apress,2018.1 online resource (XIII, 214 p. 117 illus.) 9781484235966 1484235967 1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data.Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn: Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data.Big dataPython (Computer program language)Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Pythonhttps://scigraph.springernature.com/ontologies/product-market-codes/I29080Big data.Python (Computer program language)Big Data.Python.005.757Paper Davidauthttp://id.loc.gov/vocabulary/relators/aut995402UMIUMIBOOK9910300745103321Data Science Fundamentals for Python and MongoDB2533264UNINA