Skills in EDA with Python, Pandas and Jupyter
Reusable tech-specific guidance for this playbook
Skills 20
| Title | Capability Domain | Technology Stack | Activities | Actions |
|---|---|---|---|---|
| Analytical Pattern Recognition | Chart Design / Interpretation | — | 3 | |
| Chart-Type Selection per Analysis Type | Chart Design | — | 6 | |
| Data Analysis Sequencing | Problem Framing | — | 4 | |
| Data Combination (Merge/Join/Concat) | Data Integration | pandas | 1 | |
| Data Quality Assessment | Data Readiness | — | 2 | |
| Descriptive Statistics and Ranking | Exploratory Analysis | pandas, numpy | 2 | |
| Ensemble Modeling and Feature Importance | Predictive Modeling | scikit-learn | 1 | |
| Feature Engineering and Transformation | Feature Engineering | pandas | 2 | |
| Jupyter Notebook Storytelling | Communication | Jupyter | 4 | |
| Missing Data Handling | Data Cleaning | pandas | 1 | |
| Over-Plotting and Clutter Mitigation | Chart Construction | — | 1 | |
| Pandas Data Structure Manipulation | Tabular Data Engineering | pandas, numpy | 3 | |
| Plotly Visualization Engineering | Visualization Implementation | Plotly (plotly.express, graph_objects) | 4 | |
| Predictive Experiment Design and Model Validation | Machine Learning Methodology | scikit-learn | 2 | |
| Quantitative Scale and Reference-Line Design | Chart Design | — | 2 | |
| Regression and Classification Modeling | Predictive Modeling | scikit-learn | 1 | |
| Statistical Measures and Correlation Analysis | Statistics | scipy, pandas | 2 | |
| Trellis and Multi-View Layout Design | Storybook Layout | — | 1 | |
| Unsupervised Clustering | Predictive/Exploratory Modeling | scikit-learn | 1 | |
| Visual Perception Literacy | Chart Design | — | 1 |