The exercise has four parts:
1. Gain familiarity with the system Download Tableau Public and one of its sample data sets. Familiarize yourself with the visualization techniques and the user interface of the system.
2. Examine the sample data sets You must work with the cereal data set and you are free to pick one Tableau sample dataset that is most interesting to you. Briefly scan the text of the files and familiarize yourself with the variables. Generate and write down (you will need to turn them in) 6-8 hypotheses to be considered or questions to be asked about the data elements in each dataset. Think about all the different kinds of analysis tasks that a person might want to perform in working with data sets such as these. For instance, someone working with a data set about hockey players might ask questions like:
• Identify the players who earn the highest salaries.
• How do defense players differ from offense players?
• What attributes likely lead to a high salary?
• What new player would you recommend a team try to acquire? Try not to make all of your questions be about correlations, which seems to be a common thing to do.

3. Load and examine the data sets into the system Load the cereals and other data set that you selected into Tableau Public, then use the tool to try to answer your hypotheses and questions. Also use the system to explore the data sets and see if you can discover other interesting or unexpected findings in the data sets. Put yourself in the shoes of a data analyst, and consider questions that such a person would confront.

4. Write a report on your findings
Write up a summary of your exploration process, findings, and impressions of the system.
Write up a summary of your exploration process, findings, and impressions of the system. You should include screenshots to help explain your analyses and critiques. Your report must be organized as follows:
Part A: Data and Task Analysis
Part B: Data insights and visualization design
Part C: Tool Critique

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The cereal dataset is a flat table with 14 attributes and 77 items of data. “Manufacturer name” is categorical, as manufacturer names do not have an implicit ordering to them; thus external information is necessary for them to be sorted. “Type” refers to whether Hot or Cold, hence categorical. “Calories”, “Protein”, “Fat”, “Sodium”, “Fibre”, “Complex_carbohydrates”, “Sugars”, “Potassium”, “Weight”, “Cups” - these are all quantitative attributes. These are all measures of magnitude, for a given attribute for a given item, that support arithmetic comparison. “Display shelf” is ordinal; that is, its ordering has implicit meaning. The shelf numbering can be used as a base for an analysis, as the data can be interpreted as say distance of the product from the floor. “Vitamin_minerals” are ordinal. Though numeric, this attribute does not represent a measurable quantity that pertains to the item. Thus it is unlikely for this “quantity” (0,25,100) to be used in the context of a meaningful arithmetic comparison....
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