Initial Data Analysis

[brylie]

Explore methods for visualizing, processing, and aggregating data. The goal is to create a reproducible JupyterLab notebook that includes detailed narrative explanations for each step.

Goals and Purpose:

• Develop a comprehensive initial analysis of the dataset.
• Create a reproducible JupyterLab notebook for consistent analysis.
• Educate the reader on the process, including the reasoning behind each step.

Steps:

1. Set Up the Environment:
   • Load the dataset into a JupyterLab notebook.
   • Import necessary libraries (e.g., pandas, matplotlib, seaborn).
2. Data Visualization:
   • Create initial visualizations to understand data distribution and relationships (e.g., histograms, scatter plots).
   • Add narrative explanations for each visualization, describing what is being shown and why it is important.
3. Data Processing:
   • Perform initial data cleaning (e.g., handling missing values, removing duplicates).
   • Transform and normalize data as needed.
   • Add narrative explanations for each processing step, detailing what is being done and why.
4. Data Aggregation:
   • Explore methods for aggregating data to uncover trends and patterns (e.g., grouping by categories, calculating summary statistics).
   • Add narrative explanations for aggregation methods, explaining the rationale behind each approach.
5. Document Findings:
   • Summarize the findings from the visualizations, processing, and aggregation.
   • Ensure all narrative explanations are clear and informative.
6. Reproducibility:
   • Ensure the notebook runs from start to finish without errors.
   • Verify that all steps are well-documented and can be repeated by others.
7. Review and Share:
   • Review the notebook with the team and incorporate feedback.
   • Share the final version of the JupyterLab notebook with the project repository.