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Total Size:
15.4 MB
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55513EDC6E2F639FC829F9B4361F276F15AB6BEC
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April 22, 2026, 1:06 a.m.
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(Last updated: April 22, 2026, 1:07 a.m.)
| File | Size |
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| Herman E. Data Science for Decision Makers.Using Analytics and Case Studies 2025.pdf | 15.4 MB |
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248.5 MB
[35
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33]
2024-03-04
| Uploaded by SgtEyeer | Size 248.5 MB | Health [ 35 /33 ] | Added 2024-03-04 |
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SOURCE: Herman E. Data Science for Decision Makers.Using Analytics and Case Studies 2025
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MEDIAINFO
Textbook in PDF format 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. Choose appropriate tools and technologies for data collection and preprocessing, ensuring they meet the requirements of your data strategy. Python libraries like Pandas and NumPy are essential for data manipulation and analysis, offering powerful functions for handling structured data. Additionally, consider data preprocessing platforms and software that provide robust solutions for data integration, cleaning, and transformation. Evaluate tools based on their scalability, ease of use, and compatibility with existing systems. Selecting the right technologies is crucial for efficient data processing and ensures that the data is prepared effectively for subsequent analysis and modeling stages. Python libraries such as Matplotlib, Seaborn, and Plotly, and JavaScript libraries like D3.js and Highcharts are widely used for creating static and interactive visualizations. These tools offer a vast range of capabilities, from simple plots to complex, multi-plot layouts, enabling users to tailor visual presentations to their specific analysis needs. Each library comes with unique functionalities that can be used across various data types and visualization goals. For instance, Plotly provides extensive support for interactive features, making it ideal for Web-based projects, while Matplotlib excels at creating publication-quality figures in a variety of hardcopy formats and interactive environments. 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. Preface Understanding the Role of Data in Business Strategy Foundations of Data Science Data Visualization and Communication Predictive Analytics and Machine Learning Ethical Considerations in Data Science Building a Data-Driven Culture Case Studies in Data-Driven Success The Future of Data Analytics Getting Started with Data Analytics Development Glossary of Key Terms Ethical Considerations Framework Python Code Files Data Sets 100 Luminaries in Data Science Top 10 Data-Driven Decisions Index
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