data-analysis-workflow

Python Packages Used

• Numpy
• Scipy
• Pandas
• Matplotlib
• Scikit-learn
• Jupyter

Data Workflow

This is how I generally try to approach any data analysis task.

1. Define
   • Set clear objectives
2. Import
   • Support various data types
   • Explore raw data
   • Check for any inconsistency or corruption
3. Clean
   • Preprocess
   • Filter
   • Offset
   • Exclude outliers
4. Analyze
   • Postprocess
   • Derive custom metrics
   • Aggregate multiple values into one
   • Calculate common statistics that may shed insight
5. Export
   • Save a clean dataset
6. Report
   • Re-iterate process if needed
   • Generate deliverables

Data Source

Workflow inspired by "Using Python for Data Analysis" by Ian Eyre from Real Python, retrieved on May 2024, https://realpython.com/python-for-data-analysis/

Data sourced from Washington State Department of Licensing https://www.kaggle.com/datasets/sahirmaharajj/electric-vehicle-population

Common Analysis Requests

• Descriptive analysis: describe the past
• Diagnostic analysis: investigate the why
• Predictive analysis: try to predict the future
• Prescriptive analysis: plan a strategy