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Introduction: Foundations of Data Science
Tools and Software Resources
Chapter 1.1: Professional Context and Core Concepts
Chapter 1.2: What is Data Science and Why It Matters
Chapter 1.3: Data Science vs. Statistics vs. Analytics
Chapter 1.4 The Data Science Lifecycle (CRISP-DM)
Chapter 1.5: Career Paths and Roles in Data Science
Chapter 1.6: Data Science Tools and Professional Practice
Chapter 1.7: Key Takeaways - Professional Context and Core Concepts
Chapter 2.1: Data Sources and Ethical Considerations
Chapter 2.2: Understanding Data Types - Quantitative vs. Qualitative Data
Chapter 2.3: Discrete vs. Continuous Variables
Chapter 2.4: Structured vs. Unstructured Data
Chapter 2.5: Primary vs. Secondary Data Sources
Chapter 2.6: Data Collection Methods - Surveys, Experiments, and Observations
Chapter 2.7: Data Ethics, Privacy, and Bias Considerations
Chapter 2.8: Introduction to Synthetic Datasets
Chapter 2.9: Key Takeaways - Data Sources and Ethical Considerations
Chapter 3.1: Data Preparation and Quality Assurance
Chapter 3.2: Recognizing Data Quality Issues and Their Impact
Chapter 3.3: Excel Data Cleaning Techniques and Professional Workflows
Chapter 3.4: Data Validation and Quality Assurance
Chapter 3.5: Data Formatting and Structure for Analysis
Chapter 3.6: Data Organization and Reproducible Workflows
Chapter 3.7: Key Takeaways - Data Preparation and Quality Assurance
Chapter 4.1: Exploratory Data Analysis
Chapter 4.2: Foundations of Exploratory Data Analysis
Chapter 4.3: Excel Statistical Functions and Data Analysis Tools
Chapter 4.4: JASP Interface and Statistical Analysis Environment
Chapter 4.5: Measures of Central Tendency
Chapter 4.6: Measures of Variability
Chapter 4.7: Creating Summary Tables and Initial Statistical Insights
Chapter 4.8: Distribution Shapes and Data Characteristics
Chapter 4.9: Pattern Recognition and Relationship Identification
Chapter 4.10: Creating Comprehensive Descriptive Statistics Reports
Chapter 4.11: Key Takeaways - Exploratory Data Analysis
Chapter 5.1: Data Visualization and Communication
Chapter 5.2: Principles of Effective Data Visualization
Chapter 5.3: Chart Types and Data Scenario Matching
Chapter 5.4: Color Theory and Accessibility in Data Visualization
Chapter 5.5: Creating Charts in Excel and JASP
Chapter 5.6: Dashboard Design and Layout Principles
Chapter 5.7: Key Takeaways - Data Visualization and Communication
Chapter 6.1: Statistical Analysis and Inference
Chapter 6.2: Introduction to Inferential Statistics and Probability Foundations
Chapter 6.3: T-Tests for Mean Comparisons
Chapter 6.4: Correlation Analysis and Relationship Interpretation
Chapter 6.5: Simple Linear Regression and Prediction Modeling
Chapter 6.6: Statistical Significance versus Practical Significance and Professional Communication
Chapter 6.7: Key Takeaways - Statistical Analysis and Inference
Chapter 7.1: Automated Data Workflows
Chapter 7.2: Data Workflow Concepts and Automation Benefits
Chapter 7.3: KNIME Interface Mastery and Basic Operations
Chapter 7.4: Data Transformation Nodes and Processing Components
Chapter 7.5: Advanced KNIME Workflows and Data Integration
Chapter 7.6: Workflow Documentation and Sharing Practices
Chapter 7.7: Reproducible Research and Workflow Organization
Chapter 7.8: Key Takeaways - Automated Data Workflows
Chapter 8.1: Professional Data Communication
Chapter 8.2: Executive Summary and Report Construction
Chapter 8.3: Multi-Audience Presentation Design and Delivery
Chapter 8.4: Data Storytelling and Compelling Narratives
Chapter 8.5: Dashboard Design and Interactive Communication
Chapter 8.6: Communication Effectiveness Measurement and Iteration
Chapter 8.7: Key Takeaways - Professional Data Communication
Conclusion: Foundations for Professional Data Science Practice
Accessibility Statement
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.