Practical Business Analytics Using R and Python Solve Business Problems Using a Data-driven Approach
This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze dat...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress
2023.
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| Edición: | 2nd ed. 2023. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009730937906719 |
Tabla de Contenidos:
- Section 1: Introduction to Analytics
- Chapter 1: Business Analytics Revolution
- Chapter 2: Foundations of Business Analytics
- Chapter 3: Structured Query Language (SQL) Analytics
- Chapter 4: Business Analytics Process
- Chapter 5: Exploratory Data Analysis (EDA)
- Chapter 6: Evaluating Analytics Model Performance
- Section II: Supervised Learning and Predictive Analytics
- Chapter 7: Simple Linear Regressions
- Chapter 8: Multiple Linear Regressions
- Chapter 9: Classification
- Chapter 10: Neural Networks
- Chapter 11: Logistic Regression
- Section III: Time Series Models
- Chapter 12: Time Series – Forecasting
- Section IV: Unsupervised Model and Text Mining
- Chapter 13: Cluster Analysis
- Chapter 14: Relationship Data Mining
- Chapter 15: Mining Text and Text Analytics
- Chapter 16: Big Data and Big Data Analytics
- Section V: Business Analytics Tools
- Chapter 17: R programming for Analytics
- Chapter 18: Python Programming for Analytics.
