| Edition |
First edition. |
| Description |
1 online resource (xviii, 384 pages) : illustrations (some color) |
| Series |
Big data, business analytics, and smart technology collection,
2333-6757
|
|
Big data, business analytics, and smart technology collection.
2333-6757
|
| Bibliography |
Includes bibliographical references (pages 373-375) and index. |
| Contents |
Chapter 1. Business analytics at a glance -- Chapter 2. Business analytics and business intelligence -- Chapter 3. Analytics, business analytics, data analytics, and how they fit into the broad umbrella of business intelligence -- Chapter 4. Descriptive analytics--overview, applications, and a case -- Chapter 5. Descriptive versus predictive analytics -- Chapter 6. Key predictive analytics models (predicting future business outcomes using analytic models) -- Chapter 7. Regression analysis and modeling -- Chapter 8. Time series analysis and forecasting -- Chapter 9. Data mining: tools and applications in predictive analytics -- Chapter 10. Wrap-up, overview, notes on implementation, and current state of business analytics. |
| Access |
Access restricted to authorized users and institutions. |
| Summary |
This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together. The first three chapters provide an introduction to BA, importance of analytics, types of BA--descriptive, predictive, and prescriptive--along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics--machine learning, neural networks, and artificial intelligence. The concluding chapter discusses the current state, job outlook, and certifications in analytics. |
| Form |
Also available in print. |
| System Details |
Mode of access: World Wide Web. |
|
System requirements: Adobe Acrobat reader. |
| Provenance |
Purchased with the Phippen Library Fund. |
| Subject |
Management -- Statistical methods.
|
|
Decision making -- Statistical methods.
|
|
Business planning.
|
|
Strategic planning.
|
|
Business intelligence.
|
| Indexed Term |
Analytics. |
|
Business analytics. |
|
Business intelligence. |
|
Data analysis. |
|
Decision making. |
|
Descriptive analytics. |
|
Predictive analytics. |
|
Prescriptive analytics. |
|
Statistical analysis. |
|
Quantitative techniques. |
|
Data mining. |
|
Predictive modeling. |
|
Regression analysis. |
|
Modeling. |
|
Time series forecasting. |
|
Optimization. |
|
Simulation. |
|
Maching learning. |
|
Neural networks. |
|
Artificial intelligence. |
| Genre/Form |
Electronic books.
|
| Added Title |
Predictive analytics |
| Other Form: |
Print version: 9781631574795 |
| ISBN |
9781631574801 e-book |
|
9781631574795 print |
|
