Mind Map
Visual overview of Data Science: Foundations, Definitions, Lifecycle, Ethics, and Cloud Computing
Mind Map
Interdisciplinary Definition of Data Science
Main Topic
💡
What Data Science Is: Definition, Scope, and Data-Driven Purpose
📚
Data Science vs Statistics vs Computer Science: Boundaries and Overlaps
🎯
The Data Scientist Role: Skills, Responsibilities, and Reproducibility
✨
Data Analysis Pipeline: EDA vs Confirmatory Analysis
🔍
Foundations and Workflow: From Cleaning to Feature Work to Communication
🌟
Lifecycle Frameworks (CRISP-DM): Turning Workflow into an End-to-End Process
📝
Cloud Computing for Data Science and Big Data: Scaling the Work
🧠
Ethics in Data Science: Privacy, Bias, Fairness, and Citing Data
🔑
Key Takeaways
EDA Drives What You Test
Reproducibility Is an Ethics Tool
Cloud Changes Feasibility, Not Validity
EDA Drives What You Test
Reproducibility Is an Ethics Tool
Bias Amplification Starts Before Training
Reproducibility Is an Ethics Tool
Bias Amplification Starts Before Training
Data-Centric AI Reorders Effort
EDA Drives What You Test
Reproducibility Is an Ethics Tool
Cloud Changes Feasibility, Not Validity
Cloud Changes Feasibility, Not Validity
Bias Amplification Starts Before Training
Data-Centric AI Reorders Effort
Cloud Changes Feasibility, Not Validity
Bias Amplification Starts Before Training
Data-Centric AI Reorders Effort
Reproducibility Is an Ethics Tool
Cloud Changes Feasibility, Not Validity
Bias Amplification Starts Before Training
Reproducibility Is an Ethics Tool
Bias Amplification Starts Before Training
Data-Centric AI Reorders Effort
Start with the definition and scope of dat…
Use the EDA vs confirmatory analysis disti…
Apply the foundations and data science wor…
Use lifecycle frameworks like CRISP-DM to …
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