Data Science for Decision Makers
Using Analytics and Case Studies
- Publisher
Mercury Learning and Information - ISBN 9781501523311
- Language English
- Pages 176 pp.
- Size 6" x 9"
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- Publisher
Mercury Learning and Information - ISBN 9781501520648
- Language English
- Pages 176 pp.
- Size 6" x 9"
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- Publisher
Mercury Learning and Information - ISBN 9781501520686
- Language English
- Pages 176 pp.
- Size 6" x 9"
This book is an essential guide for executives, managers, entrepreneurs, and anyone seeking to harness the power of data to drive business success. In today's fast-paced and increasingly digital world, the ability to make informed decisions based on data-driven insights is vital. It bridges the complex world of data science and the strategic decision-making process, providing readers with the knowledge and tools they need to leverage data effectively. With a clear focus on practical application, this book demystifies key concepts in data science, from data collection and analysis to predictive modeling and visualization. Via real-world examples, case studies, and actionable insights, readers will learn how to extract insights from data and translate them into strategies that drive organizational growth. Whether you're
a seasoned executive looking to sharpen your strategic acumen or a manager seeking to enhance your team's data literacy, this essential reference provides the foundation to navigate the complex landscape of data science.
FEATURES
- Features real-world case studies from various industries, showing how data science techniques solve complex business challenges.
- Offers implementation roadmaps that detail step-by-step processes for applying data science techniques in practice. These roadmaps act as guides for readers, providing clear instructions on collecting, analyzing, and interpreting data to facilitate informed decision-making.
- Provides sample data sets, sample visualizations, and the supporting Python code, providing a firsthand approach to learning and application.
1: Understanding the Role of Data in Business Strategy
2: Foundations of Data Science
3: Data Visualization and Communication
4: Predictive Analytics and Machine Learning
5: Ethical Considerations in Data Science
6: Building a Data-Driven Culture
7: Case Studies in Data-Driven Success
8: The Future of Data Analytics
9: Getting Started with Data Analytics Development
Appendix A: Glossary of Key Terms
Appendix B: Ethical Considerations Framework
Appendix C: Python Code Files
Appendix D: Data Sets
Appendix E: 100 Luminaries in Data Science
Appendix F: Top 10 Data-Driven Decisions
Index
Erik L. Herman
Erik L. Herman is a Technical Trainer, Instructional Designer, and Content Developer with expertise spanning technical training, e-learning, and content development across diverse domains such as cloud computing, data analytics, and fraud detection.