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Introduction: Foundations of Data Science
Tools and Software Resources
1. Chapter 1.1: Professional Context and Core Concepts
2. Chapter 1.2: What is Data Science and Why It Matters
3. Chapter 1.3: Data Science vs. Statistics vs. Analytics
4. Chapter 1.4 The Data Science Lifecycle (CRISP-DM)
5. Chapter 1.5: Career Paths and Roles in Data Science
6. Chapter 1.6: Data Science Tools and Professional Practice
7. Chapter 1.7: Key Takeaways - Professional Context and Core Concepts
8. Chapter 2.1: Data Sources and Ethical Considerations
9. Chapter 2.2: Understanding Data Types - Quantitative vs. Qualitative Data
10. Chapter 2.3: Discrete vs. Continuous Variables
11. Chapter 2.4: Structured vs. Unstructured Data
12. Chapter 2.5: Primary vs. Secondary Data Sources
13. Chapter 2.6: Data Collection Methods - Surveys, Experiments, and Observations
14. Chapter 2.7: Data Ethics, Privacy, and Bias Considerations
15. Chapter 2.8: Introduction to Synthetic Datasets
16. Chapter 2.9: Key Takeaways - Data Sources and Ethical Considerations
17. Chapter 3.1: Data Preparation and Quality Assurance
18. Chapter 3.2: Recognizing Data Quality Issues and Their Impact
19. Chapter 3.3: Excel Data Cleaning Techniques and Professional Workflows
20. Chapter 3.4: Data Validation and Quality Assurance
21. Chapter 3.5: Data Formatting and Structure for Analysis
22. Chapter 3.6: Data Organization and Reproducible Workflows
23. Chapter 3.7: Key Takeaways - Data Preparation and Quality Assurance
24. Chapter 4.1: Exploratory Data Analysis
25. Chapter 4.2: Foundations of Exploratory Data Analysis
26. Chapter 4.3: Excel Statistical Functions and Data Analysis Tools
27. Chapter 4.4: JASP Interface and Statistical Analysis Environment
28. Chapter 4.5: Measures of Central Tendency
29. Chapter 4.6: Measures of Variability
30. Chapter 4.7: Creating Summary Tables and Initial Statistical Insights
31. Chapter 4.8: Distribution Shapes and Data Characteristics
32. Chapter 4.9: Pattern Recognition and Relationship Identification
33. Chapter 4.10: Creating Comprehensive Descriptive Statistics Reports
34. Chapter 4.11: Key Takeaways - Exploratory Data Analysis
35. Chapter 5.1: Data Visualization and Communication
36. Chapter 5.2: Principles of Effective Data Visualization
37. Chapter 5.3: Chart Types and Data Scenario Matching
38. Chapter 5.4: Color Theory and Accessibility in Data Visualization
39. Chapter 5.5: Creating Charts in Excel and JASP
40. Chapter 5.6: Dashboard Design and Layout Principles
41. Chapter 5.7: Key Takeaways - Data Visualization and Communication
42. Chapter 6.1: Statistical Analysis and Inference
43. Chapter 6.2: Introduction to Inferential Statistics and Probability Foundations
44. Chapter 6.3: T-Tests for Mean Comparisons
45. Chapter 6.4: Correlation Analysis and Relationship Interpretation
46. Chapter 6.5: Simple Linear Regression and Prediction Modeling
47. Chapter 6.6: Statistical Significance versus Practical Significance and Professional Communication
48. Chapter 6.7: Key Takeaways - Statistical Analysis and Inference
49. Chapter 7.1: Automated Data Workflows
50. Chapter 7.2: Data Workflow Concepts and Automation Benefits
51. Chapter 7.3: KNIME Interface Mastery and Basic Operations
52. Chapter 7.4: Data Transformation Nodes and Processing Components
53. Chapter 7.5: Advanced KNIME Workflows and Data Integration
54. Chapter 7.6: Workflow Documentation and Sharing Practices
55. Chapter 7.7: Reproducible Research and Workflow Organization
56. Chapter 7.8: Key Takeaways - Automated Data Workflows
57. Chapter 8.1: Professional Data Communication
58. Chapter 8.2: Executive Summary and Report Construction
59. Chapter 8.3: Multi-Audience Presentation Design and Delivery
60. Chapter 8.4: Data Storytelling and Compelling Narratives
61. Chapter 8.5: Dashboard Design and Interactive Communication
62. Chapter 8.6: Communication Effectiveness Measurement and Iteration
63. Chapter 8.7: Key Takeaways - Professional Data Communication
Conclusion: Foundations for Professional Data Science Practice
Discovering Patterns in Data
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Introduction to Data Science Copyright © by GORAN TRAJKOVSKI is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.