Business Analytics
Autor Walter R. Paczkowskien Limba Engleză Hardback – 4 ian 2022
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.
Preț: 822.39 lei
Preț vechi: 1002.92 lei
-18%
Puncte Express: 1234
Carte tipărită la comandă
Livrare economică 08-22 decembrie
Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
Specificații
ISBN-13: 9783030870225
ISBN-10: 3030870227
Pagini: 428
Ilustrații: XXXVIII, 387 p. 238 illus., 215 illus. in color.
Dimensiuni: 160 x 241 x 29 mm
Greutate: 0.75 kg
Ediția:1st ed. 2021
Editura: Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030870227
Pagini: 428
Ilustrații: XXXVIII, 387 p. 238 illus., 215 illus. in color.
Dimensiuni: 160 x 241 x 29 mm
Greutate: 0.75 kg
Ediția:1st ed. 2021
Editura: 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 earned his Ph.D. in Economics at Texas A&M University and has worked at AT&T's Analytical Support Center, Market Analysis and Forecasting Division, and Business Research Division. He was also a Member of the Technical Staff at AT&T Bell Labs before founding Data Analytics Corp., a statistical consulting and data modeling company, in 2001. Dr. Paczkowski is a part-time lecturer in the Department of Economics and the Department of Statistics at Rutgers University. He published six books in, what he refers to as, his Analytics Series. His latest are Business Analytics: Data Science for Business Problems (Springer, 2021) and Modern Survey Analysis: Using Python for Deeper Insights (Springer, 2022).
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