Business Analytics: Data Science for Business Problems
Autor Walter R. Paczkowskien Limba Engleză Paperback – 5 ian 2023
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 590.30 lei 6-8 săpt. | |
| Springer International Publishing – 5 ian 2023 | 590.30 lei 6-8 săpt. | |
| Hardback (1) | 739.00 lei 6-8 săpt. | |
| Springer International Publishing – 4 ian 2022 | 739.00 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783030870256
ISBN-10: 3030870251
Pagini: 387
Ilustrații: XXXVIII, 387 p. 238 illus., 215 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.59 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030870251
Pagini: 387
Ilustrații: XXXVIII, 387 p. 238 illus., 215 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.59 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
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.
Notă biografică
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).
Textul de pe ultima copertă
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.
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.
Caracteristici
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