Practical data science python par varga ervin (30 résultats)

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Paperback. Etat : New. 1st ed. Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code willbe available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data sciencepracticesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3. …

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Etat : New. 1st ed. Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code willbe available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data sciencepracticesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3. …

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Paperback. Etat : Brand New. 462 pages. 9.00x6.00x1.25 inches. In Stock.

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Paperback. Etat : New.

Paperback. Etat : New. 1st ed. Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code willbe available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data sciencepracticesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3. …

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Etat : New. pp. 270.

Paperback. Etat : New. 1st ed. Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code willbe available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data sciencepracticesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3. …

Taschenbuch. Etat : Neu. Practical Data Science with Python 3 | Synthesizing Actionable Insights from Data | Ervin Varga | Taschenbuch | xvii | Englisch | 2019 | Apress | EAN 9781484248584 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Paperback. Etat : Brand New. 462 pages. 9.00x6.00x1.25 inches. In Stock. This item is printed on demand.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Gain insight into essential data science skills in a holistic manner using data engineering andassociated scalable computational methods. This book covers the most popular Python 3frameworks for both local and distributed (in premise and cloud based) processing. Alongthe way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn,Numba, Apache Spark, etc. The book is structured around examples, so you will graspcore concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineeringknowledge and experience is crucial to produce evolvable solutions. You'll see how to createmaintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to performdata science. All the code willbe available in the form of IPython not Elektronisches Buch and Python 3programs, which allow you to reproduce all analyses from the book and customize them foryour own purpose. You'll also benefit from advanced topics like Machine Learning,Recommender Systems, and Security in Data Science.Practical Data Science with Pythonwill empower you analyze data, formulate properquestions, and produce actionable insights, three core stages in most data scienceendeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologiesReview scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data sciencepracticesWho This Book Is ForAnyone who would like to embark into the realm of data science usingPython 3. 480 pp. Englisch.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Gain insight into essential data science skills in a holistic manner using data engineering andassociated scalable computational methods. This book covers the most popular Python 3frameworks for both local and distributed (in premise and cloud based) processing. Alongthe way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn,Numba, Apache Spark, etc. The book is structured around examples, so you will graspcore concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineeringknowledge and experience is crucial to produce evolvable solutions. You'll see how to createmaintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to performdata science. All the code willbe available in the form of IPython not Elektronisches Buch and Python 3programs, which allow you to reproduce all analyses from the book and customize them foryour own purpose. You'll also benefit from advanced topics like Machine Learning,Recommender Systems, and Security in Data Science.Practical Data Science with Pythonwill empower you analyze data, formulate properquestions, and produce actionable insights, three core stages in most data scienceendeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologiesReview scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data sciencepracticesWho This Book Is ForAnyone who would like to embark into the realm of data science usingPython 3.…

Etat : New. Print on Demand pp. 270.

Etat : New. PRINT ON DEMAND pp. 270.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 480 pp. Englisch.…