Decision Making via Data Analysis
Instructions:
Please submit your working R file (“Midterm_LastName_FirstName.R”), the dataset files you used for your data analysis, and the corresponding PDF file with your data manipulation steps and insights learned from the dataset. If the files are not properly uploaded, points will be deducted from your grade.
There are three datasets available, select your topic on interest and elaborate the visualizations required in the Grading
Scheme Section. Datasets available in Blackboard
• Dataset 1- FIFA Players
FIFA (video game series) is a soccer video game released annually by Electronic Arts under the EA Sports label. This dataset contains a full list of the characteristics of the most skilled soccer players in the world. Imagine you are asked to develop a report summarizing the dataset, what insights can you provide to your stakeholder?
• US-Accidents: A Countrywide Traffic Accident Dataset “This is a countrywide traffic accident dataset, which covers 49 states of the United States. The data is continuously being collected from February 2016, using several data providers for data analysis, including two APIs which provide streaming traffic event data.
These APIs broadcast traffic events captured by a variety of entities, such as the US and state departments of
transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road-networks. Currently, thereare about 3.0 million accident records in this dataset. Check the below descriptions for more detailed information.” Data analysis
Data by: Sobhan Moosavi: https://smoosavi.org/datasets/us_accidents
Grading Scheme:
• 25 Points – Project description, a clear explanation of the question(s) you seek to answer with your data analysis.
What are the visualizations telling you? Remember to implement the storytelling approach (CRISP-DM).
• 15 Points – Scatterplot
• 15 Points – Heatmap
• 15 Points – Treemap
• 25 Points – Geographic map
• 5 Points – Your graph of choice – explicitly mention what is your graph of choice
Evaluation Criteria:
• Complexity of analysis, amount of variable selection, scope of analysis
• Data Transformation steps for data analysis
• Grammar of graphics: Apply the appropriate graphic variable types for the data type and scale.
• Importance: The extent to which the visualization addresses problems facilitate decision making.
• Relevancy: Visualization contains no color, symbolism, or text that is irrelevant to the question the visualization
seeks to answer.
• Aesthetic Design: Meticulous care given to colors, shape, size, background, annotation, and overall design
Collaborations:
• This is an individual, take-home project.
• Copying another student work or colluding to prepare the exam will carry a grade of F
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