國立高雄大學圖資館 |

登入

回首頁

Practical business analytics using R and Pythonsolve business problems using a data-driven approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical business analytics using R and Pythonby Umesh R. Hodeghatta, Umesha Nayak.
其他題名:	solve business problems using a data-driven approach /
作者:	Hodeghatta, Umesh R.
其他作者:	Nayak, Umesha.
出版者:	Berkeley, CA :Apress :2023.
面頁冊數:	xxv, 706 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Decision makingData processing.
電子資源:	https://doi.org/10.1007/978-1-4842-8754-5
ISBN:	9781484287545$q(electronic bk.)

Practical business analytics using R and Pythonsolve business problems using a data-driven approach /
Hodeghatta, Umesh R.

Practical business analytics using R and Pythonsolve business problems using a data-driven approach /[electronic resource] :by Umesh R. Hodeghatta, Umesha Nayak. - Second edition. - Berkeley, CA :Apress :2023. - xxv, 706 p. :ill., digital ;24 cm.

Section 1: Introduction to Analytics -- Chapter 1: Business Analytics Revolution -- Chapter 2: Foundations of Business Analytics -- Chapter 3: Structured Query Language (SQL) Analytics -- Chapter 4: Business Analytics Process -- Chapter 5: Exploratory Data Analysis (EDA) -- Chapter 6: Evaluating Analytics Model Performance -- Section II: Supervised Learning and Predictive Analytics -- Chapter 7: Simple Linear Regressions -- Chapter 8: Multiple Linear Regressions -- Chapter 9: Classification -- Chapter 10: Neural Networks -- Chapter 11: Logistic Regression -- Section III: Time Series Models -- Chapter 12: Time Series - Forecasting -- Section IV: Unsupervised Model and Text Mining -- Chapter 13: Cluster Analysis -- Chapter 14: Relationship Data Mining -- Chapter 15: Mining Text and Text Analytics -- Chapter 16: Big Data and Big Data Analytics -- Section V: Business Analytics Tools -- Chapter 17: R programming for Analytics -- Chapter 18: Python Programming for Analytics.

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. You will: Master the mathematical foundations required for business analytics Understand 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 task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes.

ISBN: 9781484287545$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8754-5doiSubjects--Topical Terms:

235779
Decision making
--Data processing.

LC Class. No.: HD30.23

Dewey Class. No.: 658.403

Practical business analytics using R and Pythonsolve business problems using a data-driven approach /
LDR:04000nmm a2200337 a 4500 001 643683
003 DE-He213
005 20230403063010.0
006 m d
007 cr nn 008maaau
008 231123s2023 cau s 0 eng d
020 $a 9781484287545$q(electronic bk.)
020 $a 9781484287538$q(paper)
024 7 $a 10.1007/978-1-4842-8754-5 $2 doi
035 $a 978-1-4842-8754-5
040 $a GP $c GP
041 0 $a eng
050 4 $a HD30.23
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 658.403 $2 23
090 $a HD30.23 $b .H687 2023
100 1 $a Hodeghatta, Umesh R. $3 951554
245 1 0 $a Practical business analytics using R and Python $h [electronic resource] : $b solve business problems using a data-driven approach / $c by Umesh R. Hodeghatta, Umesha Nayak.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xxv, 706 p. : $b ill., digital ; $c 24 cm.
505 0 $a Section 1: Introduction to Analytics -- Chapter 1: Business Analytics Revolution -- Chapter 2: Foundations of Business Analytics -- Chapter 3: Structured Query Language (SQL) Analytics -- Chapter 4: Business Analytics Process -- Chapter 5: Exploratory Data Analysis (EDA) -- Chapter 6: Evaluating Analytics Model Performance -- Section II: Supervised Learning and Predictive Analytics -- Chapter 7: Simple Linear Regressions -- Chapter 8: Multiple Linear Regressions -- Chapter 9: Classification -- Chapter 10: Neural Networks -- Chapter 11: Logistic Regression -- Section III: Time Series Models -- Chapter 12: Time Series - Forecasting -- Section IV: Unsupervised Model and Text Mining -- Chapter 13: Cluster Analysis -- Chapter 14: Relationship Data Mining -- Chapter 15: Mining Text and Text Analytics -- Chapter 16: Big Data and Big Data Analytics -- Section V: Business Analytics Tools -- Chapter 17: R programming for Analytics -- Chapter 18: Python Programming for Analytics.
520 $a 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. You will: Master the mathematical foundations required for business analytics Understand 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 task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes.
650 0 $a Decision making $x Data processing. $3 235779
650 0 $a Business planning $x Data processing. $3 524262
650 0 $a R (Computer program language) $3 210846
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Data Science. $3 913495
650 2 4 $a Python. $3 763308
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Nayak, Umesha. $3 700318
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-8754-5
950 $a Professional and Applied Computing (SpringerNature-12059)