東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Marketing- Data processing." ]

切換: 標籤 | MARC模式 | ISBD

Python for marketing research and an...

Schwarz, Jason S.

FindBook

Google Book

Amazon

博客來

Python for marketing research and analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Python for marketing research and analytics/ by Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit.
作者:	Schwarz, Jason S.
其他作者:	Chapman, Chris.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xi, 272 p. :ill. (some col.), digital ;24 cm.
內容註:	Part I: Basics of Python -- Chapter 1: Welcome to Python -- Chapter 2: The Python Language -- Part II Fundamentals of Data Analysis -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Additional Linear Modeling Topics -- Part III Advanced data analysis -- Chapter 9: Reducing Data Complexity -- Chapter 10: Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations -- Chapter 11: Classification: Assigning observations to known categories -- Chapter 12: Conclusion -- Index.
Contained By:	Springer Nature eBook
標題:	Marketing - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-49720-0
ISBN:	9783030497200

Python for marketing research and analytics
Schwarz, Jason S.

Python for marketing research and analytics[electronic resource] /by Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit. - Cham :Springer International Publishing :2020. - xi, 272 p. :ill. (some col.), digital ;24 cm.

Part I: Basics of Python -- Chapter 1: Welcome to Python -- Chapter 2: The Python Language -- Part II Fundamentals of Data Analysis -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Additional Linear Modeling Topics -- Part III Advanced data analysis -- Chapter 9: Reducing Data Complexity -- Chapter 10: Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations -- Chapter 11: Classification: Assigning observations to known categories -- Chapter 12: Conclusion -- Index.

This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.

ISBN: 9783030497200

Standard No.: 10.1007/978-3-030-49720-0doiSubjects--Topical Terms:

582001
Marketing
--Data processing.

LC Class. No.: HF5415.125

Dewey Class. No.: 658.830285

Python for marketing research and analytics
LDR:02846nmm a2200325 a 4500 001 2257137
003 DE-He213
005 20210310095306.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030497200 $q (electronic bk.)
020 $a 9783030497194 $q (paper)
024 7 $a 10.1007/978-3-030-49720-0 $2 doi
035 $a 978-3-030-49720-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HF5415.125
072 7 $a UFM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a UFM $2 thema
082 0 4 $a 658.830285 $2 23
090 $a HF5415.125 $b .S411 2020
100 1 $a Schwarz, Jason S. $3 3528081
245 1 0 $a Python for marketing research and analytics $h [electronic resource] / $c by Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xi, 272 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Part I: Basics of Python -- Chapter 1: Welcome to Python -- Chapter 2: The Python Language -- Part II Fundamentals of Data Analysis -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Additional Linear Modeling Topics -- Part III Advanced data analysis -- Chapter 9: Reducing Data Complexity -- Chapter 10: Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations -- Chapter 11: Classification: Assigning observations to known categories -- Chapter 12: Conclusion -- Index.
520 $a This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
650 0 $a Marketing $x Data processing. $3 582001
650 0 $a Marketing research. $3 532352
650 0 $a Python (Computer program language) $3 729789
650 0 $a Information visualization. $3 615673
650 1 4 $a Statistics and Computing/Statistics Programs. $3 894293
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Statistics for Social Sciences, Humanities, Law. $3 3382004
700 1 $a Chapman, Chris. $3 1897926
700 1 $a Feit, Elea McDonnell. $3 2139592
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-49720-0
950 $a Mathematics and Statistics (SpringerNature-11649)