BUSINESS ANALYTICS
			 Communicating with numbers
			
 
       	Alison Kelly , Kevin Lertwachara , Leida Chen y Sanjiv Jaggia
Editorial: McGraw-Hill Higher Education
       
       
        Edición:  1 
       
	      
		  Fecha Publicación: 2020 
    
       
      
       	ISBN:  9781260576016 
      
      
       	ISBN ebook:  9781260590531 
		
      
      
       	Páginas:  689 
		        
      
      
       	Grado:  Universitario 
		        
      
      
       	Área:  Economía y Empresa
		        
      
      
       	Sección:  Economía 
	
      
      	  
		
			Idioma:  Inglés 
		
      
	  
	  
	  
       
        
				
				
				 
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Edición: 1
Fecha Publicación: 2020
ISBN: 9781260576016
ISBN ebook: 9781260590531
Páginas: 689
Grado: Universitario
Área: Economía y Empresa
Sección: Economía
Idioma: Inglés
        
				
				
				 
    Tweet
  
  
 
	
	
		
		
		   
		¡Disponible nueva edición!
		
		
		
	
Chapter 1. Introduction to Business Analytics
Chapter 2. Data Management and Wrangling
Chapter 3. Data Visualization and Summary Measures
Chapter 4. Probability and Probability Distributions
Chapter 5. Statistical Inference
Chapter 6. Regression Analysis
Chapter 7. Advanced Regression Analysis
Chapter 8. Introduction to Data Mining
Chapter 9. Supervised Data Mining: k-Nearest Neighbors and Naïve Bayes
Chapter 10. Supervised Data Mining: Decision Trees
Chapter 11. Unsupervised Data Mining
Chapter 12. Forecasting with Time Series Data
Chapter 13. Introduction to Prescriptive Analytics
Appendix A. Big Data Sets: Variable Description and Data Dictionary
Appendix B. Getting Started with Excel and Excel Add-Ins
Appendix C. Getting Started with R
Appendix D. Statistical Tables
Appendix E. Answers to Selected Exercises
*La edición digital no incluye códigos de acceso a material adicional o programas mencionados en el libro.
Business Analytics: Communicating with Numbers was written from the ground up to prepare students to understand, manage, and visualize the data, apply the appropriate tools, and communicate the findings and their relevance. Unlike other texts that simply repackage statistics and traditional operations research topics, this text seamlessly threads the topics of data wrangling, descriptive analytics, predictive analytics, and prescriptive analytics into a cohesive whole. It provides a holistic analytics process, including dealing with real life data that are not necessarily 'clean' and/or 'small' and stresses the importance of effectively communicating findings by including features such as a synopsis (a short writing sample) and a sample report (a longer writing sample) in every chapter. These features help students develop skills in articulating the business value of analytics by communicating insights gained from a non-technical standpoint.
Suffolk University
Kevin Lertwachara
California Polytechnic State University
Leida Chen
California Polytechnic State University
Sanjiv Jaggia
California Polytechnic State University
MÉTODOS DE COMPRA
* Precios con IVA
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