Joins)Īpply previously defined data management strategiesĬreate Key metrics/Measurements/Statistics that summarize the dataįind out if there are any outliers/anomalies in your dataset, and if there are outliers, develop strategies for dealing with themĭeploy graphical tools (scatterplots, histograms, etc) and look for relationships (correlations) within the data.Īnalyze your data over time (if appropriate) ![]() ![]() Binning)ĭetermine if your character data needs conversion and regular expression manipulationĮnsure integrity of your data (entity integrity, referential integrity, type checking, format, bounds) Ĭonsider if your dataset behooves data reshaping maneuvers such as subsetĭetermine whether your dataset needs to be assembled from different sources and identify effective data frame mechanisms (ie. Identify outliers and develop strategies for dealing with themĭetermine if your numerical data needs special coding (ie. Identify missing values and develop suitable strategies for handling them Instructor will provide feedback and the expectation is those are integrated into the EDA before final submission at the end of term. The final version will be submitted to the stakeholder at the completion of the term. A review of the EDA with the LOB Stakeholder will occur periodically during the development to integrate any changes. A copy needs to reside in each team member's cloud folder. Code will be ran to validate a knitted Rmarkdown can be generated. ![]() ![]() The end product will need to be submitted as a word document, generated by a Rmarkdown, with each team mate's name on it to the canvas portal by one team member, but all artifacts (code, rmd, and knitted file) needs to reside in both the Rstudio Cloud project and gitlab "Documentation" Directory. It will need to complete a Rmarkdown, viewable from the Rstudio Cloud account as well as accessible from the team project gitlab within the "Documentation" directory.
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