Foundations of Programming, Statistics, and Machine Learning for Business Analytics
Autor Ram Gopal, Dan Philps, Tillman Weydeen Limba Engleză Paperback – 29 apr 2023
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Specificații
ISBN-13: 9781529620917
ISBN-10: 1529620910
Pagini: 512
Dimensiuni: 189 x 246 x 27 mm
Greutate: 0.9 kg
Editura: SAGE Publications Ltd
ISBN-10: 1529620910
Pagini: 512
Dimensiuni: 189 x 246 x 27 mm
Greutate: 0.9 kg
Editura: SAGE Publications Ltd
Notă biografică
Ram D. Gopal is the Information Systems Society¿s Distinguished Fellow and Alan Turing Institute¿s Turing Fellow, a Professor of Information Systems and Management, and Pro-Dean for Research, Engagement, and Impact at the Warwick Business School.
Cuprins
Chapter 1: Introduction To Programming And Statistics
Chapter 2: Summarizing And Visualizing Data
Chapter 3: Summarizing And Visualizing Data
Chapter 4: Programming Fundamentals
Chapter 5: Programming Fundamentals
Chapter 6: Distributions
Chapter 7: Statistical Testing - Concepts and Strategy
Chapter 8: Statistical Testing - Concepts and Strategy
Chapter 9: Nonparametric Tests
Chapter 10: Reality Check
Chapter 11: Fundamentals of Estimation
Chapter 12: Linear Models
Chapter 13: General Linear Models
Chapter 14: Regression Diagnostics And Structure
Chapter 15: Timeseries And Forecasting
Chapter 16: Introduction To Machine Learning
Chapter 17: Model Selection And Cross Validation
Chapter 18: Regression Models In Machine Learning
Chapter 19: Classification Models And Evaluation
Chapter 20: Automated Machine Learning
Chapter 2: Summarizing And Visualizing Data
Chapter 3: Summarizing And Visualizing Data
Chapter 4: Programming Fundamentals
Chapter 5: Programming Fundamentals
Chapter 6: Distributions
Chapter 7: Statistical Testing - Concepts and Strategy
Chapter 8: Statistical Testing - Concepts and Strategy
Chapter 9: Nonparametric Tests
Chapter 10: Reality Check
Chapter 11: Fundamentals of Estimation
Chapter 12: Linear Models
Chapter 13: General Linear Models
Chapter 14: Regression Diagnostics And Structure
Chapter 15: Timeseries And Forecasting
Chapter 16: Introduction To Machine Learning
Chapter 17: Model Selection And Cross Validation
Chapter 18: Regression Models In Machine Learning
Chapter 19: Classification Models And Evaluation
Chapter 20: Automated Machine Learning