Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/1438
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dc.contributor.authorPaper, David. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:28:46Z-
dc.date.available2020-05-17T08:28:46Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235973 ;en_US
dc.identifier.isbn9781484235966 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1438-
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484235966. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionQA76en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractBuild 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 isneet 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 eerockyee 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. ;en_US
dc.description.statementofresponsibilityby David Paper.en_US
dc.description.tableofcontents1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data. ;en_US
dc.format.extentXIII, 214 p. 117 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235973.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Scienceen_US
dc.subjectBig Data. ;en_US
dc.subjectPython. ;en_US
dc.titleData Science Fundamentals for Python and MongoDBen_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

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9781484235973.pdf7.38 MBAdobe PDFThumbnail
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Full metadata record
DC FieldValueLanguage
dc.contributor.authorPaper, David. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:28:46Z-
dc.date.available2020-05-17T08:28:46Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235973 ;en_US
dc.identifier.isbn9781484235966 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1438-
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484235966. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionQA76en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractBuild 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 isneet 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 eerockyee 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. ;en_US
dc.description.statementofresponsibilityby David Paper.en_US
dc.description.tableofcontents1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data. ;en_US
dc.format.extentXIII, 214 p. 117 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235973.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Scienceen_US
dc.subjectBig Data. ;en_US
dc.subjectPython. ;en_US
dc.titleData Science Fundamentals for Python and MongoDBen_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484235973.pdf7.38 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPaper, David. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:28:46Z-
dc.date.available2020-05-17T08:28:46Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235973 ;en_US
dc.identifier.isbn9781484235966 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1438-
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484235966. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionQA76en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractBuild 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 isneet 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 eerockyee 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. ;en_US
dc.description.statementofresponsibilityby David Paper.en_US
dc.description.tableofcontents1. Introduction -- 2. Monte Carlo Simulation and Density Functions -- 3. Linear Algebra -- 4. Gradient Descent -- 5. Working with Data -- 6. Exploring Data. ;en_US
dc.format.extentXIII, 214 p. 117 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235973.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Scienceen_US
dc.subjectBig Data. ;en_US
dc.subjectPython. ;en_US
dc.titleData Science Fundamentals for Python and MongoDBen_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484235973.pdf7.38 MBAdobe PDFThumbnail
Preview File