COMPETENCIES
4033.1.1 : Advanced Elements of Data Visualization
The graduate describes the fundamentals of effective story-telling through data visualization.
4033.1.2 : Configure Data for Visualization
The graduate configures data for visualization.
4033.1.3 : Visualizing Data Patterns and Proportions
The graduate creates patterns and proportions for effective data visualization.
4033.1.4 : Visualizing Relationships and Differences
The graduate creates visualizations representing the relationships and differences of the data.
4033.1.5 : Data Visualization Presentation
The graduate implements best practices using data visualization techniques for effective storytelling.
INTRODUCTION
The purpose of this task is for you to visually showcase data by identifying appropriate sources of data, scraping and cleaning data, and presenting those data in different formats. As you complete this task, you will create a dashboard with multiple visualization formats, use your data to effectively tell a story, and justify the choices you make. Your choices will be based on the criteria in the scenario and requirements sections below. You will also submit a brief written response to explain how you chose to present your data and why your methods are appropriate.
You must submit all your data files (both raw and cleaned) in spreadsheet format (e.g., .csv, .xls, .xlsx). To scrape your data, choose reliable web sources and use tools common to the industry such as Python, R, or other industry standard tools. You should use Tableau to create all your graphic representations (e.g., scatter plots, bubble charts, density plots, histogram matrices, heat maps, parallel coordinate plots, multidimensional scaling). Submit these Tableau outputs as .pdf files. Use a word processor to create your accompanying written report, as outlined in the requirements. This report should be two pages long, formatted in 12-point Arial font, and double spaced. Submit the reports as a .doc, .docx, or .pdf file.
The following artifacts are required to be submitted as part of this task:
- data files (raw and cleaned) in spreadsheet format (.csv, .xls, or .xlsx)
- dashboard and graphical representations of each data set, as created in Tableau (.pdf)
- written response or data summary (.doc, .docx, or .pdf)
Your submission will not be evaluated if these three artifacts are not submitted in the proper format.
SCENARIO
You have been hired as a data analyst and one of your first tasks includes selecting a car, primarily for your use, that will become part of the company fleet. Your manager has already narrowed down the choices to four vehicles: a 2019 Ford Escape, 2019 Honda CRV, 2019 Hyundai Santa Fe, or 2019 Toyota Rav 4. Because you will purchase the car with company funds, your manager has asked you to select a vehicle based on the company’s criteria; however, your own criteria for choosing a car are different. You must use data visualization best practices to create a dashboard, using data you will scrape, and tell a story for your manager to support your car selection.
Criteria 1 (Company Criteria):
Safety features – weighted at 10
Maintenance cost – weighted at 5
Price point – weighted at 7
Criteria 2 (Your Criteria):
Insurance
Fuel Economy
Resale Value
Note: You should include your personal weighting for the individual aspects of your criteria as part of your response to the requirements below.
REQUIREMENTS
Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. An originality report is provided when you submit your task that can be used as a guide.
You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.
Data Scrape
- Scrape and submit the data for criteria 1 in the scenario.
- Scrape and submit the data for criteria 2 in the scenario.
Data Configuration
- Clean the data for criteria 1 and submit with labeled ranges and weights.
- Clean the data for criteria 2 and submit with labeled ranges and weights. Include the weights you have selected for all three aspects of criteria 2.
- Combine all the data and submit with labeled ranges and weightings for all sixaspects of the criteria sets.
Data Presentation
- Present your data visually based on criteria 1 in threedata visualization or comparative graphic formats.
- Present your data visually based on criteria 2 in threedata visualization or comparative graphic formats.
- Create a dashboard with fourgraphic representations, using accurate patterns and proportions, of all of your data for all six aspects of the criteria in the scenario.
Written Response
- Justify your choice of the best vehicle based on all aspects from the two data sets, citing specific examples from your data to support your claims.
- Explain how you used elements of effective storytelling in your presentation. Be sure to include specific examples of how you used these elements to persuade your manager to purchase the car you have chosen.
- Record the web sources you used to scrape the data in parts A1 and A2. Be sure the web sources are reliable.
Sources
- Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.
File Restrictions
File name may contain only letters, numbers, spaces, and these symbols: ! – _ . * ‘ ( )
File size limit: 200 MB
File types allowed: doc, docx, rtf, xls, xlsx, ppt, pptx, odt, pdf, txt, qt, mov, mpg, avi, mp3, wav, mp4, wma, flv, asf, mpeg, wmv, m4v, svg, tif, tiff, jpeg, jpg, gif, png, zip, rar, tar, 7z
RUBRIC
PROFESSIONAL COMMUNICATION:ARTICULATION OF RESPONSE (CLARITY, ORGANIZATION, MECHANICS)
Not Evident
Responses are unstructured or disjointed. Vocabulary and tone are unprofessional or distract from the topic. Responses contain pervasive errors in mechanics, usage, or grammar.
APPROACHING COMPETENCE
Responses are poorly organized or difficult to follow. Terminology is misused or ineffective. Responses contain errors in mechanics, usage, or grammar that cause confusion.
COMPETENT
Responses are organized and focus on the main ideas presented in the assessment. Word choice is pertinent and clearly conveys the intended meaning to the audience. Responses reflect attention to detail. Mechanics, usage, and grammar promote understanding and readability.
A:SCRAPED DATA: CRITERIA 1
NOT EVIDENT
No scraped data are submitted.
APPROACHING COMPETENCE
The submission contains scraped data, but the data do not match the components of criteria 1 in the scenario.
COMPETENT
The submission contains scraped data for criteria 1 in the scenario.
B:SCRAPED DATA: CRITERIA 2
NOT EVIDENT
No scraped data are submitted.
APPROACHING COMPETENCE
The submission contains scraped data, but the data do not match the components of criteria 2 in the scenario.
COMPETENT
The submission contains scraped data for criteria 2 in the scenario.
C:CLEANED DATA: CRITERIA 1
NOT EVIDENT
No cleaned data are submitted.
APPROACHING COMPETENCE
The submission contains cleaned data for criteria 1, but the data are not cleaned accurately or the submission does not show corresponding ranges.
COMPETENT
The submission contains accurately cleaned data for criteria 1 and shows the corresponding ranges and weights.
D:CLEANED DATA: CRITERIA 2
NOT EVIDENT
No cleaned data are submitted.
APPROACHING COMPETENCE
The submission contains cleaned data for criteria 2, but the data are not cleaned accurately or the submission does not show corresponding ranges and weights.
COMPETENT
The submission contains accurately cleaned data for criteria 2 and shows the corresponding ranges and weights.
E:COMBINED DATA
NOT EVIDENT
No combined data are submitted, or the submission does not include all 6 data sets.
APPROACHING COMPETENCE
The submission combines data and includes ranges and weights for all 6 data sets, but the data is inaccurate.
COMPETENT
The submission accurately combines data with corresponding ranges and weights labeled for all 6 data sets.
F:VISUAL PRESENTATION: CRITERIA 1
NOT EVIDENT
Visual representations of data for criteria 1 are not submitted or are not presented in at least 3 formats.
APPROACHING COMPETENCE
The submission represents data visually for criteria 1, but the graphics presented are either not accurate descriptive visualizations or comparative graphics or do not include labels or units of measure.
COMPETENT
The submission represents data using accurate descriptive visualizations and comparative graphics for criteria 1 in at least 3 data visualization formats and includes accurate labels and units of measure.
G:VISUAL PRESENTATION: CRITERIA 2
NOT EVIDENT
Visual presentations of data for criteria 2 are not submitted.
APPROACHING COMPETENCE
The submission represents data visually for criteria 2, but the graphics presented are either not accurate descriptive visualizations or comparative graphics or do not include labels or units of measure.
COMPETENT
The submission represents data using descriptive visualizations and comparative graphics for criteria 2 in at least 3 data visualization formats and includes accurate labels and units of measure.
H:DASHBOARD
NOT EVIDENT
A dashboard is not submitted, or the dashboard contains fewer than 4 graphic representations or does not represent all 6 aspects of the criteria in the scenario.
APPROACHING COMPETENCE
The dashboard contains graphic representations, but the patterns and proportions shown are inaccurate or inappropriate for all 6 aspects of the criteria in the scenario. The representation does not support effective storytelling.
COMPETENT
The dashboard contains at least 4 graphic representations, using accurate patterns and proportions for all 6 aspects of the criteria in the scenario. The representation supports effective storytelling.
I:VEHICLE SELECTION
NOT EVIDENT
A vehicle choice is not provided.
APPROACHING COMPETENCE
The submission indicates the chosen vehicle but either does not justify the choice or does not support the justification by citing specific examples from the data.
COMPETENT
The submission justifies the choice of the chosen vehicle based on all 6 aspects for the two data sets and supports the justification by citing specific examples from the data.
J:STORYTELLING METHODS
NOT EVIDENT
An explanation is not provided.
APPROACHING COMPETENCE
The submission explains elements of storytelling, but the elements are not evidenced in the presentation. The explanation does not address how the dashboard communicates the intended outcome.
COMPETENT
The submission explains the elements of effective storytelling, using examples implemented in the presentation, and addresses how the dashboard communicates the intended outcome.
K:WEB SOURCES
NOT EVIDENT
Web sources are not provided.
APPROACHING COMPETENCE
The submission includes web sources used for the scraped data in parts A1 and A2, but the sources are not reliable.
COMPETENT
The submission includes reliable web sources used for the scraped data in parts A1 and A2.
L:SOURCES
NOT EVIDENT
The submission does not include both in-text citations and a reference list for sources that are quoted, paraphrased, or summarized.
APPROACHING COMPETENCE
The submission includes in-text citations for sources that are quoted, paraphrased, or summarized, and a reference list; however, the citations and/or reference list is incomplete or inaccurate.
COMPETENT
The submission includes in-text citations for sources that are properly quoted, paraphrased, or summarized and a reference list that accurately identifies the author, date, title, and source location as available.
