Project Selection and Data Preparedness The overall objective of this milestone

Project Selection and Data Preparedness
The overall objective of this milestone is to choose a project, clean the data for the project and come up with questions to tell a story about the data you have chosen. For this milestone, you will choose a project that you want to tell a story about. You will document the key and relevant steps/plans and include them in your report and appendices.
Prompt
The Make Over Monday community project is an amazing way to learn, grow, and even have fun. Now is your chance to dip a toe into this great experience!
First, you choose a data set you want to tell a story from the official Make Over Monday websiteLinks to an external site.. You need to join to get access to the data. The registration is free. You should choose a dataset from past projects after 2018. Then, explain why you have chosen this project.
There are a lot of data sets to choose from on the website, and they are not very searchable, so some data sets are listed here for you to choose from.
2020 Week 20Links to an external site.- Safe Houses: Places of Sanctuary for Girls and Women At Risk of FGM
2020 Week 19-Links to an external site. World Happiness Report 2020
2020 Week 16-Links to an external site. Food: Greenhouse Gas Emission across supply chain
2020 Week 15-Links to an external site. Soccerment
2020 Week 14-Links to an external site. Violence Against Women & Girls – perceptions in African, Asian and South American countries
2019 Week 15Links to an external site. - Ranking the States by Fiscal Condition
2019 Week 19Links to an external site. - Major League Baseball’s Most Efficient Batters
2019 Week 27Links to an external site. - Game of Thrones deaths
2019 Week 10Links to an external site. - World Development Indicators: Health & Equality
If you choose a dataset other than the list that is provided, make sure that your data set has at least 5 columns!
Next, you will do data cleaning. Please note that you might have to do a little or even a lot of data cleaning on your own, depending on which data set you use! Choose wisely. You can re-watch the Video: Data Prep with Excel FilesLinks to an external site. to remember how to prepare and clean Excel files with Tableau. A never version of this video is as follows Data Prep with Text and Excel filesLinks to an external site.. You need to register to get access to the public videos on Tableau. Registration is free.
Tableau is more compatible and efficient when working with datasets in the long (tall) format rather than the wide format. In the long format, each row corresponds to a single measure for a subject within a given variable. On the other hand, the wide format consists of rows where each row contains all measures for a given variable. To understand these formats better and their implications for data analysis in Tableau, I highly recommend watching the mini-lecture on long (tall) and wide format. This understanding will be particularly useful for your final project as it will enable you to conduct a more effective analysis using Tableau.
You can find the mini-lecture on long (tall) and wide format here:
Tall And Wide FormatLinks to an external site.
Answer the following questions to make sure your data is ready to be visualized:
Are there any introductory texts in your Excel file? If yes, you need to fix it.
Is the table in the tall format? If no, pivot your table to tall format.
Are the numbers formatted as such and not as text? If no, format numbers as numbers.
Are there any summary rows such as Average? If yes, you need to fix it.
Do you have any empty rows? If yes, you need to remove aggregation.
Do the columns have a meaningful heading? If no, title each column with a proper heading.
Next, you will come up with 10 questions that you can answer with the data set you have chosen. Then, you will identify and recommend one type of chart to answer each question. You WILL NOT build charts for this milestone. A piece of advice: Recommend the right chart for the right purpose, e.g. if your question is “Do car accidents increase over time?”, then you recommend a line chart and not a string of pie charts to answer this question. You can refer to Stephen Few’s Chart Selection MatrixLinks to an external site..
Finally, write a short paper on the data visualization process by addressing the following questions:
How was the data imported and analyzed?
What challenges did you run into as you conducted your analysis?
How did you identify key patterns or outliers?
How did you decide on the chart type for each question?
What is the purpose of your story and what do you want your viewer’s journey to be?
Remember, you will tell a story with the data you have chosen in the last module of this class. Thus, the questions you come up with should tell a story rather than they are chosen randomly. To help guide you on writing the purpose of your story the key thing is to know your data. Be sure to read the resource article on the Make Over Monday website!
To successfully complete this assignment, view the Milestone 1 Rubric Download Milestone 1 Rubricand the Final Project Rubric Download Final Project Rubric.
View Rubric
Milestone 1 Rubric
Milestone 1 Rubric
CriteriaRatingsPts
Data Selection and Cleaning12 pts
Proficient
Demonstrates a sophisticated knowledge of appropriate data selection and data cleaning by following the instructions and answering all questions related to the data cleaning process with a detailed and sophisticated explanation of the process.9.34 pts
Satisfactory
Selects a dataset that is appropriate and cleans the data by following the instructions, but answers most of the questions related to the data cleaning process with an explanation of the process.6.67 pts
Needs Improvement
Selects a dataset that is appropriate but inaccurately cleans the data by answering some of the questions related to the data cleaning process with an explanation which is lack in detail, or inappropriate0 pts
Not Evident
Neither selects dataset nor cleans a dataset./ 12 pts
Selecting Questions
13 pts
Proficient
Demonstrates a sophisticated knowledge of data analysis for selecting questions through a detailed explanation of why each question is related to the story in the data.10.12 pts
Satisfactory
Selects questions with an explanation of why each question is related to the story in the data, but the explanation lacks in detail.7.23 pts
Needs Improvement
Selects questions with some explanation which lacks in detail, or contains inaccuracies. The questions are not related to the story or cannot be used to tell a data story.0 pts
Not Evident
Did not select questions./ 13 pts
Selecting Charts
13 pts
Proficient
Demonstrates a sophisticated knowledge of data analysis for selecting charts through a detailed explanation of why each chart is appropriate for the question asked.10.12 pts
Satisfactory
Selects charts with an explanation of why each chart is appropriate for the question asked, but the explanation lacks in detail.7.23 pts
Needs Improvement
Selects charts with some explanation which lacks in detail or contains inaccuracies. The charts are inappropriate.0 pts
Not Evident
Did not select charts./ 13 pts
Content of Report
7 pts
Proficient
The content of the written submission is well-presented and argued; ideas are detailed, developed and supported with evidence, and data analysis. A non-technical audience can easily understand the content.5.44 pts
Satisfactory
The content of the written submission is sound and solid; ideas are present. Most ideas are developed or supported with evidence, and data analysis. It may include some content that a non-technical audience may find difficult to understand.3.89 pts
Needs Improvement
The content of the written submission is not sound. Few ideas are presented, and most of the ideas are not developed or supported with evidence, and data analysis. The report is too technical.0 pts
Not Evident
The content of the written submission is missing./ 7 pts
Mechanics
5 pts
Proficient
No grammar or spelling errors that distract the reader from the content. Required graphs or tables are clearly labeled.3.75 pts
Satisfactory
Minor errors in grammar or spelling that distract the reader from the content. Required graphs or tables may be missing or not clearly labeled.2.5 pts
Needs Improvement
Major errors in grammar or spelling that distract the reader from the content. Graphs or tables are not included.0 pts
Not Evident
Critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas./ 5 pts
Total Points: 0
Project topic:2020 Week 19-Links to an external site. World Happiness Report 2020