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Titlebook: Business Analytics; Data Science for Bus Walter R. Paczkowski Book 2021 Springer Nature Switzerland AG 2021 data science.machine learning.b

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發(fā)表于 2025-3-21 18:45:30 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Business Analytics
期刊簡稱Data Science for Bus
影響因子2023Walter R. Paczkowski
視頻videohttp://file.papertrans.cn/193/192028/192028.mp4
發(fā)行地址Uses case studies to illustrate concepts.Presents examples using Python in the context of Jupyter notebooks with Programming Literacy examples.Features appendices with technical details
圖書封面Titlebook: Business Analytics; Data Science for Bus Walter R. Paczkowski Book 2021 Springer Nature Switzerland AG 2021 data science.machine learning.b
影響因子.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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書目名稱Business Analytics影響因子(影響力)




書目名稱Business Analytics影響因子(影響力)學科排名




書目名稱Business Analytics網絡公開度




書目名稱Business Analytics網絡公開度學科排名




書目名稱Business Analytics被引頻次




書目名稱Business Analytics被引頻次學科排名




書目名稱Business Analytics年度引用




書目名稱Business Analytics年度引用學科排名




書目名稱Business Analytics讀者反饋




書目名稱Business Analytics讀者反饋學科排名




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Data Visualization: The Basicsscussed in many books. I focus on data visualization from a practical analytical point-of-view in this chapter, not their presentation. This does not mean, however, that they cannot be used in a presentation; they certainly can be used. The graphs I describe are meant to aid and enhance the extracti
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Advanced , for Business Data Analyticsns of the Data Cube. The reason for the segue is that Business Data Analytics involves more than just modeling. It also involves classification. Both are methods for learning from your data, the former in a supervised fashion and the latter in an unsupervised fashion.
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