Business Analytics - Paczkowski, Walter R.
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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…mehr

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Produktbeschreibung
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 marketresearch.

Autorenporträt
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).