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The beginner's guide to data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The beginner's guide to data scienceby Robert Ball, Brian Rague.
作者:	Ball, Robert.
其他作者:	Rague, Brian.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xi, 248 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-3-031-07865-1
ISBN:	9783031078651$q(electronic bk.)

The beginner's guide to data science
Ball, Robert.

The beginner's guide to data science[electronic resource] /by Robert Ball, Brian Rague. - Cham :Springer International Publishing :2022. - xi, 248 p. :ill., digital ;24 cm.

Chapter. 1. Introduction to Data Science -- Chapter. 2. Data Collection -- Chapter. 3. Data Wrangling -- Chapter. 4. Crash Course on Descriptive Statistics -- Chapter. 5. Inferential Statistics -- Chapter. 6. Metrics -- Chapter. 7. Recommendation Engines -- Chapter. 8. Machine Learning -- Chapter. 9 -- Natural Language Processing (NLP) -- Chapter. 10. Time Series -- Chapter. 11. Final Product.

This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered. Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in "big data," leveraging database and data collection tools such as web scraping and text identification. This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics: Data gathering and acquisition techniques including data creation Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner Machine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time Data science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.

ISBN: 9783031078651$q(electronic bk.)

Standard No.: 10.1007/978-3-031-07865-1doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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505 0 $a Chapter. 1. Introduction to Data Science -- Chapter. 2. Data Collection -- Chapter. 3. Data Wrangling -- Chapter. 4. Crash Course on Descriptive Statistics -- Chapter. 5. Inferential Statistics -- Chapter. 6. Metrics -- Chapter. 7. Recommendation Engines -- Chapter. 8. Machine Learning -- Chapter. 9 -- Natural Language Processing (NLP) -- Chapter. 10. Time Series -- Chapter. 11. Final Product.
520 $a This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered. Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in "big data," leveraging database and data collection tools such as web scraping and text identification. This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics: Data gathering and acquisition techniques including data creation Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded Metrics that measure some quantity such as distance, similarity, or error and which are especially useful when comparing one or more data observations Recommendation engines representing a set of algorithms designed to predict (or recommend) a particular product, service, or other item of interest a user or customer wishes to buy or utilize in some manner Machine learning implementations and associated algorithms, comprising core data science technologies with many practical applications, especially predictive analytics Natural Language Processing, which expedites the parsing and comprehension of written and spoken language in an effective and accurate manner Time series analysis, techniques to examine and generate forecasts about the progress and evolution of data over time Data science provides the methodology and tools to accurately interpret an increasing volume of incoming information in order to discern patterns, evaluate trends, and make the right decisions. The results of data science analysis provide real world answers to real world questions. Professionals working on data science and business intelligence projects as well as advanced-level students and researchers focused on data science, computer science, business and mathematics programs will benefit from this book.
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