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Matt’s Cars Data Project – Project Plan: Difference between revisions

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Created page with "= Matt’s Cars Data Project — Project Plan = == Overview == This project analyzes a dataset of 30 vehicles owned over a period of more than 30 years. The work serves multiple purposes, including skill demonstration, academic practice, structured personal documentation, and exploratory data analysis using SQL, R, and Python. ---- == Project Goals == * Create a dataset for exploratory analysis * Demonstrate database design and data ingestion skills * Apply statistica..."
 
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== Overview ==
== Overview ==
This project analyzes a dataset of 30 vehicles owned over a period of more than 30 years. The work serves multiple purposes, including skill demonstration, academic practice, structured personal documentation, and exploratory data analysis using SQL, R, and Python.
This project analyzes a dataset of 30 vehicles owned over a period of more than 30 years. The work serves multiple purposes, including skill demonstration, academic practice, structured personal documentation, and exploratory data analysis using SQL, R, and Python.
----


== Project Goals ==
== Project Goals ==
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* Produce technical documentation
* Produce technical documentation
* Publish pages to the Saxton Publishing documentation site
* Publish pages to the Saxton Publishing documentation site
----


== Phase 1: Planning ==
== Phase 1: Planning ==
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* This project plan page
* This project plan page
* A list of project phases with descriptions
* A list of project phases with descriptions
----


== Phase 2: Data Preparation and Normalization ==
== Phase 2: Data Preparation and Normalization ==
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* Data dictionary describing all attributes
* Data dictionary describing all attributes
* Documented standardization decisions
* Documented standardization decisions
----


== Phase 3: Database Development ==
== Phase 3: Database Development ==
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* SQL statements to build the database
* SQL statements to build the database
* A populated Postgres SQL dataset
* A populated Postgres SQL dataset
----


== Phase 4: Exploratory Analysis (SQL) ==
== Phase 4: Exploratory Analysis (SQL) ==
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* A documented set of SQL queries
* A documented set of SQL queries
* Descriptive analysis of the dataset
* Descriptive analysis of the dataset
----


== Phase 5: Statistical Analysis Using R ==
== Phase 5: Statistical Analysis Using R ==
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* R scripts and visualizations
* R scripts and visualizations
* Notes about the analysis, including limitations or assumptions
* Notes about the analysis, including limitations or assumptions
----


== Phase 6: Car Documentation ==
== Phase 6: Car Documentation ==
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* Individual pages for each vehicle
* Individual pages for each vehicle
* Links between cars and relevant analyses
* Links between cars and relevant analyses
----


== Phase 7: Reflection ==
== Phase 7: Reflection ==
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* Reflection page summarizing lessons learned
* Reflection page summarizing lessons learned
* Blog post describing the project and results of analysis
* Blog post describing the project and results of analysis
----


[[Category:Projects]]
[[Category:Projects]]

Revision as of 00:42, 30 December 2025

Matt’s Cars Data Project — Project Plan

Overview

This project analyzes a dataset of 30 vehicles owned over a period of more than 30 years. The work serves multiple purposes, including skill demonstration, academic practice, structured personal documentation, and exploratory data analysis using SQL, R, and Python.

Project Goals

  • Create a dataset for exploratory analysis
  • Demonstrate database design and data ingestion skills
  • Apply statistical and analytical techniques
  • Produce technical documentation
  • Publish pages to the Saxton Publishing documentation site

Phase 1: Planning

Objectives

  • Define the project
  • Establish project phases

Results

  • This project plan page
  • A list of project phases with descriptions

Phase 2: Data Preparation and Normalization

Objectives

  • Complete existing spreadsheet fields
  • Identify missing data or other anomalies

Results

  • Data dictionary describing all attributes
  • Documented standardization decisions

Phase 3: Database Development

Objectives

  • Populate the database from prepared data

Results

  • SQL statements to build the database
  • A populated Postgres SQL dataset

Phase 4: Exploratory Analysis (SQL)

Objectives

  • Create SQL queries to explore the dataset
  • Perform analysis using SQL queries
  • Identify patterns and anomalies
  • Record the findings

Results

  • A documented set of SQL queries
  • Descriptive analysis of the dataset

Phase 5: Statistical Analysis Using R

Objectives

  • Develop a list of questions for statistical analysis using R
  • Produce visualizations that demonstrate the results of the analysis

Results

  • R scripts and visualizations
  • Notes about the analysis, including limitations or assumptions

Phase 6: Car Documentation

Objectives

  • Create one page per vehicle for details

Results

  • Individual pages for each vehicle
  • Links between cars and relevant analyses

Phase 7: Reflection

Objectives

  • Identify what worked well, what changed, and what to improve next time

Results

  • Reflection page summarizing lessons learned
  • Blog post describing the project and results of analysis