Téléchargez le livre : Business Analytics

Business Analytics

Data Science for Business Problems

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Éditeur :

Springer


Paru le : 2022-01-03



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Description

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.
Pages
387 pages
Collection
n.c
Parution
2022-01-03
Marque
Springer
EAN papier
9783030870225
EAN PDF
9783030870232
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
38
Taille du fichier
14531 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
94,94 €
EAN EPUB
9783030870232
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
38
Taille du fichier
93051 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
94,94 €

Walter R. Paczkowski, PhD, has worked at AT&T, AT&T Bell Labs, and AT&T Labs. He founded Data Analytics Corp., a statistical consulting company, in 2001. Dr. Paczkowski is also a part-time lecturer of economics at Rutgers University. He is the author of Deep Data Analytics for New Product Development (2020), Pricing Analytics: Models and Advanced Quantitative Techniques for Product Pricing (2018), and Market Data Analysis Using JMP (2016).


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