Hodeghatta Umesh R R (32 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.

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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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.

Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

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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Paperback. Condition: Brand New. 2nd edition. 731 pages. 10.00x7.01x1.47 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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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 our Sydney, NSW warehouse or from our UK or US warehouse, 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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