Language:
English
繁體中文
Help
圖資館首頁
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Business analyticsdata science for b...
~
Paczkowski, Walter R.
Business analyticsdata science for business problems /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Business analyticsby Walter R. Paczkowski.
Reminder of title:
data science for business problems /
Author:
Paczkowski, Walter R.
Published:
Cham :Springer International Publishing :2021.
Description:
xxxviii, 387 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Decision makingMathematical models.
Online resource:
https://doi.org/10.1007/978-3-030-87023-2
ISBN:
9783030870232$q(electronic bk.)
Business analyticsdata science for business problems /
Paczkowski, Walter R.
Business analytics
data science for business problems /[electronic resource] :by Walter R. Paczkowski. - Cham :Springer International Publishing :2021. - xxxviii, 387 p. :ill., digital ;24 cm.
1. Types of Business Problems -- 2. Data for Business Problems -- 3. Beginning Data Handling -- 4. Data Preprocessing -- 5. Data Visualization: The Basics -- 6. OLS Regression Basics -- 7. Time Series Basics -- 8. Statistical Tables -- 9. Advanced Data Handling -- 10. Advanced OLS -- 11. Logistic Regression -- 12. Classification.
This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.
ISBN: 9783030870232$q(electronic bk.)
Standard No.: 10.1007/978-3-030-87023-2doiSubjects--Topical Terms:
190791
Decision making
--Mathematical models.
LC Class. No.: HD30.23 / .P33 2021
Dewey Class. No.: 658.4033
Business analyticsdata science for business problems /
LDR
:02321nmm a2200337 a 4500
001
612445
003
DE-He213
005
20220103160456.0
006
m d
007
cr nn 008maaau
008
220526s2021 sz s 0 eng d
020
$a
9783030870232$q(electronic bk.)
020
$a
9783030870225$q(paper)
024
7
$a
10.1007/978-3-030-87023-2
$2
doi
035
$a
978-3-030-87023-2
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
HD30.23
$b
.P33 2021
072
7
$a
PBT
$2
bicssc
072
7
$a
BUS061000
$2
bisacsh
072
7
$a
PBT
$2
thema
072
7
$a
K
$2
thema
082
0 4
$a
658.4033
$2
23
090
$a
HD30.23
$b
.P122 2021
100
1
$a
Paczkowski, Walter R.
$3
900455
245
1 0
$a
Business analytics
$h
[electronic resource] :
$b
data science for business problems /
$c
by Walter R. Paczkowski.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
xxxviii, 387 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
1. Types of Business Problems -- 2. Data for Business Problems -- 3. Beginning Data Handling -- 4. Data Preprocessing -- 5. Data Visualization: The Basics -- 6. OLS Regression Basics -- 7. Time Series Basics -- 8. Statistical Tables -- 9. Advanced Data Handling -- 10. Advanced OLS -- 11. Logistic Regression -- 12. Classification.
520
$a
This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.
650
0
$a
Decision making
$x
Mathematical models.
$3
190791
650
1 4
$a
Statistics for Business, Management, Economics, Finance, Insurance.
$3
825914
650
2 4
$a
Big Data/Analytics.
$3
742047
650
2 4
$a
Probability and Statistics in Computer Science.
$3
274053
650
2 4
$a
Online Marketing/Social Media.
$3
739541
650
2 4
$a
Consumer Behavior.
$3
772804
650
2 4
$a
Market Research/Competitive Intelligence.
$3
731061
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/978-3-030-87023-2
950
$a
Mathematics and Statistics (SpringerNature-11649)
based on 0 review(s)
ALL
電子館藏
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
000000207919
電子館藏
1圖書
電子書
EB HD30.23 .P122 2021 2021
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Multimedia file
https://doi.org/10.1007/978-3-030-87023-2
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login