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Business analyticsdata science for b...

Paczkowski, Walter R.

Business analyticsdata science for business problems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Business analyticsby Walter R. Paczkowski.
其他題名:	data science for business problems /
作者:	Paczkowski, Walter R.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xxxviii, 387 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Decision makingMathematical models.
電子資源:	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 analyticsdata 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

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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.
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