Data Science for Decision Makers

Name: Data Science for Decision Makers | Using Analytics and Case Studies
Brand: De Gruyter
Price: 37.95 EUR
Availability: OnlineOnly

Using Analytics and Case Studies

Erik Herman(Autor*in)

De Gruyter (Verlag)

1. Auflage

Erschienen am 31. Dezember 2024

250 Seiten

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978-1-5015-2064-8 (ISBN)

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Inhalt

Title Page
Copyright Page
Dedication
Contents
Preface
Chapter 1: Understanding the Role of Data in Business Strategy
Introduction to Data-Driven Decision-Making
Identify market trends and consumer preferences
Enhance operational efficiency
Mitigate risks and uncertainties
Drive innovation and competitive advantage
The Data Science Lifecycle
Data Acquisition
Data Preparation
Exploratory Data Analysis (EDA)
Model Development
Model Evaluation
Deployment and Monitoring
Key Concepts and Terminology
Data
Big Data
Data Types
Descriptive vs. Inferential Statistics
Machine Learning
Predictive Analytics
Data Visualization
Algorithm
Using Analytics: Business Strategy
Strategic Decision Support
Lifecycle Optimization
Conceptual Understanding
Case Study: Optimizing Supply Chain Operations
Objective
Approach
Result
Sample Visualizations
Python code
Implementation Roadmap: Introduction to Data Science and Decision Making
Roadmap steps
Conclusion
Chapter 2: Foundations of Data Science
Data Collection: The Backbone of Data Science
Data Source
Data Acquisition Techniques
Data Quality and Integrity
Data Preprocessing: Cleaning and Transforming Raw Data
Data Cleaning
Data Transformation
Data Integration
Unveiling Insights Through Exploratory Data Analysis
Descriptive Statistics
Data Visualization
Correlation Analysis
Clustering and Dimensionality Reduction
Statistical Fundamentals for Data Analysis
Probability Distributions
Hypothesis Testing
Regression Analysis
Statistical Inference
Using Analytics: Data Science Foundations
Data Quality Assurance
Pattern Recognition
Inference and Prediction
Case Study: Predictive Maintenance in Manufacturing
Objective
Approach
Result
Example Visualizations
Python Code
Implementation Roadmap: Foundations of Data Science
Roadmap Steps
Conclusion
Chapter 3: Data Visualization and Communication
Principles of Data Visualization
Clarity and Simplicity
Accuracy and Integrity
Relevance and Context
Use of Color and Visual Elements
Interactivity and Engagement
Tools and Techniques for Data Visualization
Data Visualization Libraries
Business Intelligence (BI) Tools
Geographic Information Systems (GIS)
Infographics and Data Storytelling
Dashboard Design Principles
Effective Data Communication Strategies
Know Your Audience
Tell a Story
Use Multiple Channels
Solicit Feedback
Foster Data Literacy
Using Analytics: Data Visualization and Communication
Insights Communication
Interactive Exploration
Storytelling and Persuasion
Case Study: Market Trend Analysis for Retail Expansion
Objective
Approach
Result
Sample Visualizations
Python code
Implementation Roadmap: Data Visualization and Communication
Roadmap steps:
Conclusion
Chapter 4: Predictive Analytics and Machine Learning
Introduction to Predictive Modeling
Problem Formulation
Data Preparation
Model Selection
Model Training
Model Evaluation
Model Deployment
Supervised and Unsupervised Learning Techniques
Supervised Learning
Unsupervised Learning
Model Evaluation and Validation
Performance Metrics
Cross-Validation
Model Selection
Validation Strategies
Overfitting and Underfitting
Using Analytics: Predictive Analytics and Machine Learning
Predictive Modeling
Unsupervised Learning
Model Evaluation and Validation
Case Study: Personalized Recommendations in E-commerce
Objective
Approach
Result
Sample Visualizations
Python Code
Implementation Roadmap: Predictive Analytics and Machine Learning
Roadmap Steps
Conclusion
Chapter 5: Ethical Considerations in Data Science
Ethics in Data Collection and Usage
Privacy and Consent
Data Ownership and Control
Data Security
Responsible Data Usage
Fairness, Bias, and Transparency
Fairness in Algorithm Design
Bias Detection and Mitigation
Transparency and Explainability
Algorithmic Impact Assessments
Legal and Regulatory Frameworks
Data Protection Laws
Anti-Discrimination Laws
Intellectual Property Rights
Regulatory Compliance
Using Analytics: Ethical Considerations in Data Science
Ethical Data Collection and Usage
Fairness, Bias, and Transparency
Compliance with Legal and Regulatory Frameworks
Case Study: Fair Lending Practices in Financial Services
Objective
Approach
Result
Sample Visualizations
Python Code
Implementation Roadmap: Ethical Considerations in Data Science
Roadmap Steps
Conclusion
Chapter 6: Building a Data-Driven Culture
Fostering a Culture of Data Literacy
Education and Training
Promote Continuous Learning
Firsthand Experience
Leadership Support
Overcoming Organizational Challenges
Change Management
Data Integration and Governance
Resource Allocation
Metrics and KPIs
Implementing Data-Driven Decision Making
Define Clear Objectives
Data Collection and Analysis
Decision Support Systems
Iterative Improvement
Cultivate a Learning Culture
Using Analytics: Building a Data-Driven Culture
Fostering a Culture of Data Literacy
Overcoming Organizational Challenges
Implementing Data-Driven Decision Making
Case Study: Cultivating Data Literacy in a Healthcare Organization
Objective
Approach
Result
Sample Visualizations
Python Code
Implementation Roadmap: Building a Data-Driven Culture
Roadmap Steps
Conclusion
Chapter 7: Case Studies in Data-Driven Success
Industry-Specific Case Studies
Retail
Healthcare
Finance
Manufacturing
Marketing
Lessons Learned and Best Practices
Data Quality and Governance
Cross-Functional Collaboration
Agile and Iterative Approach
Ethical Considerations
Practical Insights for Decision Makers
Leadership Support
Talent Acquisition and Development
Measure Impact and ROI
Continuous Improvement
Using Analytics: Case Studies in Data-Driven Success
Industry-Specific Case Studies
Lessons Learned
Best Practices
Case Study Compilation: Lessons from Industry-Specific Data Successes
Industry-Specific Case Studies
Lessons Learned and Best Practices
Implementation Roadmap: Case Studies in Data-Driven Success
Roadmap Steps
Conclusion
Chapter 8: The Future of Data Analytics
Emerging Trends Shaping the Future of Data Analytics
Advanced AI and Machine Learning (ML)
Real-Time Analytics and Edge Computing
Augmented Analytics
Automated Machine Learning (AutoML)
Data Ethics, Privacy, and Governance
Technological Advancements Impacting Data Analytics
Quantum Computing
Blockchain Technology for Data Integrity
Advanced Data Visualization Tools
Internet of Things (IoT) and Connected Devices
The Evolution of Data-Driven Decision Making
Conclusion
Embracing Technological Advancements for a Competitive Edge
The Role of Ethics and Data Governance in the Future of Analytics
Data as a Strategic Asset
Building a Data-Driven Culture
Anticipating the Challenges and Preparing for the Future
Toward a Future Where Data is Central to Every Industry
Empowering Decision-Makers with Data-Driven Insights
Data Analytics as a Path to Innovation and Growth
Chapter 9: Getting Started with Data Analytics Development
Setting Up a Data Analytics Development Environment
Choosing and Installing Python as the Primary Language
Selecting the Right IDE
Hardware Considerations for Data Processing
Introduction to Python for Data Analytics
Essential Libraries for Data Analytics
Practical Application: Setting Up a Data Analytics Environment and Running an Initial Analysis
Step-by-Step Guide to Setting Up and Running an Analysis
Example Script: Loading and Visualizing Data
Conclusion
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

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