Business Analytics: Data Science for Business Problems

Author:   Walter R. Paczkowski
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
ISBN:  

9783030870225


Pages:   387
Publication Date:   04 January 2022
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Business Analytics: Data Science for Business Problems


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Overview

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.

Full Product Details

Author:   Walter R. Paczkowski
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Weight:   0.805kg
ISBN:  

9783030870225


ISBN 10:   3030870227
Pages:   387
Publication Date:   04 January 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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Author Information

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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