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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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发表于 2025-3-21 18:45:30 | 显示全部楼层 |阅读模式
期刊全称Business Analytics
期刊简称Data Science for Bus
影响因子2023Walter R. Paczkowski
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发行地址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
图书封面Titlebook: Business Analytics; Data Science for Bus Walter R. Paczkowski Book 2021 Springer Nature Switzerland AG 2021 data science.machine learning.b
影响因子.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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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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