Practical Data Science with R; Nina Zumel, John Mount; 2014

Practical Data Science with R Upplaga 1

av Nina Zumel, John Mount
 

DESCRIPTION

Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions.

 

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels.

 

 

RETAIL SELLING POINTS

Demonstrations of need-to-know statistical ideas

Covers all aspects of the project lifecycle

Data science for the motivated business professional

 

AUDIENCE

Written for the business analyst, technical consultant or technical director— no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus.

 

ABOUT THE TECHNOLOGY

R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions.

 
 

DESCRIPTION

Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions.

 

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels.

 

 

RETAIL SELLING POINTS

Demonstrations of need-to-know statistical ideas

Covers all aspects of the project lifecycle

Data science for the motivated business professional

 

AUDIENCE

Written for the business analyst, technical consultant or technical director— no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus.

 

ABOUT THE TECHNOLOGY

R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions.

 
Upplaga: 1a upplagan
Utgiven: 2014
ISBN: 9781617291562
Förlag: Pearson Education
Format: Häftad
Språk: Engelska
Sidor: 414 st
 

DESCRIPTION

Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions.

 

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels.

 

 

RETAIL SELLING POINTS

Demonstrations of need-to-know statistical ideas

Covers all aspects of the project lifecycle

Data science for the motivated business professional

 

AUDIENCE

Written for the business analyst, technical consultant or technical director— no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus.

 

ABOUT THE TECHNOLOGY

R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions.

 
 

DESCRIPTION

Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions.

 

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels.

 

 

RETAIL SELLING POINTS

Demonstrations of need-to-know statistical ideas

Covers all aspects of the project lifecycle

Data science for the motivated business professional

 

AUDIENCE

Written for the business analyst, technical consultant or technical director— no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus.

 

ABOUT THE TECHNOLOGY

R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions.

 
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