Use tidyverse packages to transform and load data from a CSV, then make complex visualizations to aid in discovering which forest fire factors correlate strongly with increase frequency of fire, or increased intensity (using area burned as a proxy).
Using SQLite3, load the dataset with the given schema diagram, then generate a view of table statistics. Use this data, and no python functions, to do a sales analysis on to discover which models are best sellers, which product lines are best to expand, and how much can we spend on a marketting campaign per new customer to and continue to drive a profit. Use only SQL.
DataQuest guided projects provide a dataset, and a target for analysis.
DataQuest provides vidualization snippets and/or implementation metrics to allow for checking milestone progreess.
Largely, the student is responsible for sanity checking the analysis.
Example1: In the python-mapReduce project they provided the implementation metric: execution time for each method.
Example2: In the R-visualizations-forest-fire-metrics project, DataQuest provided two months with highest fire frequency, and .png examples of one of the plots. They do not provide any code or code base.
DataQuest started Sept 2023
Initial commit Mar 2024