The objectives for the assignment include giving students the opportunity:
- to apply the material from the lectures/reading during the creation and self-evaluation of their own “data story”
- You may use any data visualization tool (e.g. Tableau, Excel, Power BI, R/ggplot, python/matplotlib) to complete the requirements for this assignment.
- You may use the same dataset and any relevant data visualizations you have created as part of the previous assignment(s).
For this assignment, students will use a business/organizational context and related data to create a “data story”. In addition to creating their “data story” students will self-evaluate using the provided “Data Story Evaluation Framework”.
Data Story Evaluation Framework
|Scoring Item/Category||Score*||Additional rationalization/evaluation comments|
|Step 1: Who, What, How||NA||NA (this is a category)|
|It is obvious who the intended audience is|
|What the audience needs to know and/or do is clear|
|The approach taken in the story for the audience to know and/or do is effective|
|Step 2: The Big Idea and 3-minute Story||NA||NA (this is a category)|
|The Big Idea is very concise and effective|
|(optional) There aren’t any unneeded words in the 3-minute story|
|Step 3: Storyboard||NA||NA (this is a category)|
|There is a clear beginning|
|There is a clear middle|
|There is a clear end|
|The narrative is only added as needed|
|There are no gaps which could be bridged with a narrative|
|Step 4: Create Data Visualization for Each Frame||NA||NA (this is a category)|
|All data visualizations are independently effective|
|Step 5: Assess Story Clarity||NA||NA (this is a category)|
|Horizontal logic assessment score|
|Vertical logic assessment score|
|(optional) External reviewer score|
* score includes NA, and 0 – 2; 2 = “perfect”, 1 = “not perfect”, 0 = “missing/incorrect”
- Choose a dataset; if you don’t have a dataset see:
- Analyze the dataset and determine what “data story” will be created
- create a “data story” including:
- The Big Idea
- A storyboard – this should be an image of the sticky notes
- The multi-frame data story – i.e. three or more frames are required
- Evaluate your “data story” with the provided “Data Story Evaluation Framework”
- See the section, “Submissions” for the submission requirements.
A single pdf file will be submitted for this assignment. The pdf will include:
- A title page including:
- Full name
- Submission date
- Course name
- Submission title (E.g. Assignment 3)
- Page numbers
- The Big Idea
- An image of student’s storyboard
- The multi-frame “data story”
- Student’s self-evaluation of their “data story” using the provided “Data Story Evaluation Framework”.
(see Rubric on Canvas Assignment)
Sample Submissions for Storyboard
The following sample submissions for the storyboard component of this assignment represent a range of different approaches. All approaches received full points for the given assignment component.
The Big Idea:
- Baseball is a popular sport in the United States, and the salary of baseball players is often discussed and compared. There are many factors that can affect salary, such as seniority, performance, scores, and the league or region a player is in that can change the level of salary. Based on the results of the chart and data analysis above, summarize the factors that affect salary most and give some suggestions to current players.
The player’s division has the greatest impact. The players are divided into two different areas, east and west. Use box plot in ggplot to observe these two areas.
Through this chart, we can find that the overall wage level in the east is higher than that in the west. At the same time, there are more outliers in the east than in the west. Moreover, I have calculated the T- test of East and West, that provided the mean of East is 624.2714, and 450.8769 for the West.
There is the salary analysis of different alliances.
This figure shows that the maximum value and the first quartile of League A are higher than those of League N. However, the median of N is higher than A, and the minimum wage level is not much different.
The relationship between the number of years an athlete has played baseball and his salary.
As shown in the figure, most of the time from 0 to 5 years are below 1000, and only a few exceed 1000, and there is an upward trend in this area. After 5 years, these points are very scattered, and it does not have much impact on wages.
The impact of athletes’ hits on wages. Overall, there is an upward trend, and the largest increase in the 100-150 region.
Conclusion: According to the statistics, which league to join makes little difference for athletes, but athletes in the east earn higher wages than those in the west. Excluding a very rare phenomenon, the wages of the athletes will increase in the first five years after the start of the competition, but after five years wages have little to do with the qualifications of the athletes. In terms of technology, the number of hits is directly proportional to salary, and the slope is higher between 100-150. In conclusion, the most important variables for an athlete’s salary are division and hits. Evaluation: