Hodeghatta Umesh (38 results)

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Paperback or Softback. Condition: New. Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-Driven Approach. Book.

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Paperback. Condition: new. Paperback. This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. Youll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: New. 2nd ed. This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You'll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

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Condition: As New. Unread book in perfect condition.

Paperback. Condition: New. 2nd ed. This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You'll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

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Condition: New. 2023. 2nd Edition. paperback. . . . . .

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Condition: New. 2023. 2nd Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condition: Brand New. 2nd edition. 731 pages. 10.00x7.01x1.47 inches. In Stock.

Paperback. Condition: Brand New. 297 pages. 9.25x6.25x0.50 inches. In Stock.

Paperback. Condition: New. 2nd ed. This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You'll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

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Taschenbuch. Condition: Neu. Neuware -The InfoSec Handbook offers the reader an organized layout of information that is easily read and understood. Allowing beginners to enter the field and understand the key concepts and ideas, while still keeping the experienced readers updated on topics and concepts.It is intended mainly for beginners to the field of information security, written in a way that makes it easy for them to understand the detailed content of the book. The book offers a practical and simple view of the security practices while still offering somewhat technical and detailed information relating to security. It helps the reader build a strong foundation of information, allowing them to move forward from the book with a larger knowledge base.Security is a constantly growing concern that everyone must deal with. Whether it's an average computer user or a highly skilled computer user, they are always confronted with different security risks. These risks range in danger and should always be dealt with accordingly. Unfortunately, not everyone is aware of the dangers or how to prevent them and this is where most of the issues arise in information technology (IT). When computer users do not take security into account many issues can arise from that like system compromises or loss of data and information. This is an obvious issue that is present with all computer users.This book is intended to educate the average and experienced user of what kinds of different security practices and standards exist. It will also cover how to manage security software and updates in order to be as protected as possible from all of the threats that they face.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 392 pp. Englisch.

Paperback. Condition: Brand New. 297 pages. 9.25x6.25x0.50 inches. In Stock.

Paperback. Condition: New. 2nd ed. This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You'll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 392 | Sprache: Englisch | Produktart: Bücher | The InfoSec Handbook offers the reader an organized layout of information that is easily read and understood. Allowing beginners to enter the field and understand the key concepts and ideas, while still keeping the experienced readers updated on topics and concepts. It is intended mainly for beginners to the field of information security, written in a way that makes it easy for them to understand the detailed content of the book. The book offers a practical and simple view of the security practices while still offering somewhat technical and detailed information relating to security. It helps the reader build a strong foundation of information, allowing them to move forward from the book with a larger knowledge base. Security is a constantly growing concern that everyone must deal with. Whether it's an average computer user or a highly skilled computer user, they are always confronted with different security risks. These risks range in danger and should always be dealt with accordingly. Unfortunately, not everyone is aware of the dangers or how to prevent them and this is where most of the issues arise in information technology (IT). When computer users do not take security into account many issues can arise from that like system compromises or loss of data and information. This is an obvious issue that is present with all computer users. This book is intended to educate the average and experienced user of what kinds of different security practices and standards exist. It will also cover how to manage security software and updates in order to be as protected as possible from all of the threats that they face.