Data visualization, you will be extracting and processing real data (string, integer, and…

Data visualization, you will be extracting and processing real data (string, integer, and floating-point) related to your chosen topic(s); analyzing the data in various ways; and then creating data visualizations for the data, such as a line graph, bar chart, and histogram. You may choose the same or different topics for the three graphs.

Your data will be organized into containers or groupings such as lists. Although Python lists can contain different types of data, please make your lists all the same type of data. You may find your original data in the form of structures called dictionaries, composed of key: value pairs. Keys are similar to the names of properties you included in your abstraction tables. Both lists and dictionaries are examples of data structures.

You may just use defined lists for data processing and visualization in your project, or add additional processing steps for extra credit, such as the following options:

  • Include code in your project to extract data from one or more external data files or other sources
  • Include code in your project to extract data represented in a dictionary structure

Processing of each element in lists and/or dictionaries may be performed using conditional (branching) statements and iteration (looping). We will be practicing with these types of processing, and you will be completing exercises on these in zyBook chapters. Separate functions should be developed to create each of the three different data visualizations.

Below are some examples from the think.cs.vt.edu course site. The first shows 3 lists, each containing a different type of data (integer, floating point, and string):

temperatures = [45, 33, 20, 11, -7, 15, 3]

magnitudes = [2.1, 1.1, 3.7, 4.2, 2.0, 1.7 ]

cities = [ ‘Blacksburg, VA’, ‘New York, NY’, ‘Seattle, WA’ ]

(Python supports single quotes or double quotes for strings; other languages such as C and Java support only double quotes for strings)

Another example shows a basic dictionary structure with key:value pairs for a movie:

movieOne = {“title”: “Jurassic Park”, “year”: 1993, “length”: 127, “genre”: “SciFi”, “format”: “DVD”, “price”: 12.5}

Provide and explain answers for the following questions, placed inside a Jupyter Notebook for your program, to evaluate your data sources and your own initial ideas about the data:

  1. What problem are you trying to solve or questions you are trying to answer with these data visualizations? Why did you select the topic(s)?
  2. Document all of your data sources. For each data source, evaluate the quantity and quality of the data. For example, is there an abundance of data or relatively little? Is the data clear and well-organized, or disorganized or hard to extract? How old is the data?
  3. For each data source, identify and evaluate the reliability of the author and/or sponsoring organization. Are there biases that you should be aware of when using their data? If so, what are they? Are the data sources clearly cited? Does the author/organization have the credentials (degrees, professional or other experience, etc.) to serve as a trustworthy source?
  4. What factors influenced your choice data for each of the graphs?
  5. Why did you choose to filter data as you did? Explain. (Filtering refers to leaving out some data value(s) based on specific criteria.)
  6. What were your initial hypotheses about what patterns the data would reveal? For example, did you believe the line graph would show data widely varying, or trending in an upward or downward direction? Why?
  7. Do you have any biases about the data topic you selected? Did they influence your project, and if so, how?

Provide and explain answers for the following questions, placed inside your Jupyter Notebook, after you have completed your code:

  1. Did the visualizations support your initial hypotheses? Why or why not?
  2. How could your visualizations be used to help solve your original problem or answer your initial questions? Explain in detail for each visualization.
  3. In the line graph, how can the line showing the average of all the values be used for data analysis?
  4. Exactly what does the histogram display about the data?
  5. Do your data visualization results raise new questions that might be investigated by gathering/examining new data? What are some examples?
  6. Analyze any limitations on your visualization results due to the constraints either of the data availability or the visualization software itself.
  7. What pattern(s) or trend(s) did your line graph reveal? What reasonable explanation for possible cause(s) could be inferred? What would you predict a continuation of your line graph (with more data added) might look like, and why?
  8. Are there any implications (positive or negative) of your visualizations that would possibly require careful consideration and possible action by individuals or society as a whole? (Example implications: social, ethical, scientific, etc.)? Explain.
  9. Evaluate your final project and visualization project. Include any difficulties you encountered during the project. In your opinion, which data visualization is most effective for displaying your data? Why? If you were able to make improvements/enhancements, what would they be?
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