Conduct an exploratory data analysis and provide a draft outline
describing the key features of the data and any significant
relationships and information contained in the data set that you found.
You are required to include specific screenshots of graphs, tables,
etc., that are provided:
- How did you verify that the data was reliable before proceeding?
- What problems did you find and how did you address them?
- What relationships did you find in the data?
- Are there any missing data?
- How are you going to summarize data samples?
- Analyze
trends with respect to any appropriate characteristics that you may
have discovered. Include relevant line graphs, pie charts, bar charts,
and scatter plots. - What have you done to prevent the Simpson’s paradox?
- Next,
you will work on a descriptive analytics. Supplement your description
with appropriate charts/figures and finalize by creating an appropriate
dashboard with PowerBI or Tableau. Include a summary that provides a
detailed overview of the data behavior you have identified based upon
the analysis. Indicate any causal relationships you found. - Segment
the data accordingly, if needed, to help describe the data behavior.
Did you have to redo your sample? Can you identify any data anomalies?
If there are anomalies, what do they represent and how do you avoid
them? - Indicate the steps you have taken to investigate the
quality of the data and indicate any variables you have transformed or
discarded as a result.
Provide the raw software files (Tableau or PowerBI) that you used for this assignment.
Synthesize
the information from your draft outline to complete, in 1,500–2,000
words, the relevant components in the Data Diagnostics and Descriptive
Summary section of the “Capstone Project Thesis Template.”
Business Problem: Our organization does not have a current plan in place for the amount of inventory they have on sight at any given time. Stores work off of their previous projections, but the organization has gained a large amount of business in the last 3-5 years and does not have a way currently to predict the amount of inventory required, causing frequent under and over stocking problems.
Analytical Problem: Perform a demand forecast to determine what the number of items are expected to be purchased in a given month. Use linear optimization to find the ideal number of inventory items to purchase.
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