Answer each prompt with a paragraph: (do not use Ai)
1. In this module, you learned about data visualization what attributes make them most effective. For this week’s discussion post, locate a data visualization and analyze it. Use what you have learned from the module readings and insights material to craft your response.
To successfully complete this assignment, please address the following:
Locate and select a data visualization.
Upload and/or copy and paste the data visualization in your response.
Provide a link to where you found the data visualization.
Answer the prompts.
Prompts
Answer all of the following in your analysis:
Identify what elements allow you to identify what is going on in the data visualization.
What is the purpose of the data visualization?
Who is the intended audience?
Identify what information the chart is meant to convey.
Identify patterns or trends in the data.
How are text and labels used effectively or ineffectively in this data visualization?
Are colors used? If so, what do they represent?
Do you believe this is an effective visualization? Why or why not?
How would this data be used by stakeholders?
2. In this module, you began exploring data and thinking critically about how to assess the quality. In this week’s discussion post, you are asked to explore how data can be used to fit a given case study. Rather than getting stuck on the nitty gritty details, try to think broadly and creatively about how data can be used within the parameters of the described case study.
Select one of the case studies from the following resources and follow the instructions provided below to write your post
Analytics Enabled: Getting Value From Every Link [PDF]Download Analytics Enabled: Getting Value From Every Link [PDF]
Prompts
Based on the case description you choose, propose a potential analysis to address a question or issue raised in the selected case. Describe the type of data that would be needed and describe 2-3 data quality issues that should be checked given your choice of data. The goal of this assignment is not to get a quote and quote correct answer, to think more deeply about data quality in a real-world scenario. When working in industry, data quality issues are typically ambiguous and not clearly defined and always run the risk of a new issue coming up that is not already documented. Hence, in the following case studies, the goal of reading these case studies is to think outside the box and think analytically about where an issue may come up.
3. In this module, you learned different methods for summarizing data. In this week’s discussion post, you will be asked to reflect on and discuss the different methods for summarizing data. Use what you have learned from the module readings and insights material to craft your response.
For this discussion, select a data set (you may use the same data set that you used in the M2: Assignment or select a different data set). Refer back to what you learned in Module 1 and Module 2 about qualitative and quantitative data. For this discussion, you will only be using quantitative data.
Prompts
For your initial post:
Provide a description of the data set.
Select three quantitative variables that are directly related to the data description.
For example, if your data set is on restaurant rating, the number of views and average score given is what you should focus on rather than if your restaurant is in the guidebook.
For each of the quantitative variables, identify the summary statistics (mean, median, and mode).
In addition, review and respond to at least two of your classmate’s descriptions.
Ask a question that you think about be interesting to investigate based on the data set being described.
4. In the M3: Discussion—Summarizing Data, you selected three quantitative variables from a data set and completed summary statistics. You can consider this as a starting point for exploratory data analysis. Now you are going to take this one step further, you are going to create and use data visualizations to investigate potential insights we can get from data sets.
You will also be evaluating each other’s visualizations and using the results to generate a new question. For this discussion, address all the prompts below using what you have learned from the module readings and insights material to craft your response.
As a reminder, Chapter 7: Exploratory Data Analysis introduces an iterative cycle for EDA:
Generate questions about your data.
Search for answers by visualizing, transforming, and modeling your data.
Use what you learn to refine your questions and/or generate new questions.
You will use this process to explore data in this week’s module.
Prompts
For your initial post complete the following:
Open the dataset from last week or select a new one.
Create a question that you can answer using this data set.
For example, if your data set is about visitors to your website, you could pose the question, what is the average age of site visitors?
Create a visualization that helps answer that question or produces insights that can help you refine the questions or generate new questions.
For example, you can create a histogram or boxplot that shows the distribution of the age of your visitors.
In response:
You will provide a critique. Use the following questions to form your response:
Was there anything that was unclear about the data visualization?
Was there anything missing from their visualization?
Was the source listed?
Was the data visualization labeled?
Was the variable the right one you chose?
Does the visualization that was selected match the description they wrote about it?
Pose a new question that will help you further analyze the data based on the visualization being presented.
