API CAN CODE �Doing Data Science

Final Project Part 1: Finding Data

This work was made possible through generous support from the National Science Foundation (Award # 2141655).

Warmup

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What do you notice? What do you wonder?

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View on �your screen

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The Final Project: �DOING DATA SCIENCE

Step into the role of a data scientist and �let your project showcase your skills

“Doing Data Science” Final Project

For the final project, you will take on the role of a data scientist! �

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This project will take several class periods, so try to choose a topic you wouldn’t mind working on for a while!

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Research Questions

Identify questions about this dataset

Finding �Data

Access and evaluate a relevant dataset

Data �Refining

Filter, clean and trim the data

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Data Visualization

Create data visualizations and interpret

Communicating Results

Share your conclusions and insights

Final Presentation Structure

For the final presentation, we will ask you to make a set of slides that includes:�

• Dataset Used and Trimming: Dataset used, information about your EduBlocks program and any filtering
• Questions for Investigation
• Visual Analysis/Graphing Insights: 3-5 graphs
• Conclusions: How do these graphs answer your questions about the dataset?�Presentations should be about 3 minutes in length.

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API Can Code Q&A Document!

• Using RapidAPI, EduBlocks, and/or CODAP, you may encounter errors that you haven’t seen before or don’t know how to fix. �
• Consider checking our Q&A document, which answers a number of common questions �about each of these three technologies

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Today’s focus

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Research Questions

Identify questions about this dataset

Finding �Data

Access and evaluate a relevant dataset

Data �Refining

Filter, clean and trim the data

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Data Visualization

Create data visualizations and interpret

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Communicating Results

Share your conclusions and insights

Accessing a Dataset [Review]

• The main focus of today’s class will be to find and access an API dataset that you’re interested in. �
• Before finding your dataset, let’s review an example using EduBlocks to access an API dataset.�
• Our example involves Library Usage �Data from OpenDataDC.

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OpenDataDC: Library Usage Data

• Open this example EduBlocks project, which draws in API data from OpenDataDC on Library Usage�
• Explore the program and run it. What do you notice? What do you wonder?

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OpenDataDC: Library Usage Data

• Note that the for loop uses �for i in myJSON[‘features’] instead of the prior �for i in myJSON. What do you think this is for?�
• The print statement uses i[‘attributes’][‘NAME’] for a library’s name instead of i[‘NAME’]. Why do you think this is?

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OpenDataDC: Library Usage Data

• Both the for loop and the print statement are specifically constructed to handle data nesting in this dataset. �
• Data nesting is when data in an API is organized in layers. ��In this example, all of the meaningful data is within the ‘features’ key, and each library’s meaningful data is within an ‘attributes’ key.

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OpenDataDC: Library Usage Data

• Now, clone the program and replace the for loop with a print statement that just says print(myJSON).
• Don’t forget to put your API Key located on RapidAPI
• What do you see when you run the program?
• Based on your understanding of data nesting, why is this happening?
• Why would this be a problem in your final project?

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Today’s Goals:

There are three main goals for Part 1 of the final project: �

• Find an API source of data

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• Design an EduBlocks program �to call the data from the API

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• Evaluate the dataset

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Find an API Source

• Browse the list of free APIs for a source of data you think might be interesting to investigate.�
• You might have a question you want �to answer with this data right now, �or a question might develop as you�explore the dataset.

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Share with Your Classmates

• Share with your classmates the API you found interesting and explain why.

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Design an EduBlocks Program

• Now, design an EduBlocks program to pull in the dataset you just found. �
• You can use this basic program to get started, but you’ll need to fill in essential info for your API source.

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• Test your program to make sure your API works! You may need to consider nesting or other issues.

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Data Evaluation

• Evaluate your data source using the 5Vs for K-12 Framework. �
• Use the following slides to guide this evaluation. �

Respond to the questions in today’s � worksheet.�

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Evaluating Data: The 5Vs for K-12

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Respond to these prompts in today’s worksheet.

Evaluating Data: Volume

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• How much data is included in the dataset? Or, How many data points/observations/rows are included?
• Is there enough data to answer the research questions?
• Is there enough data to draw meaningful insights or conclusions?
• Is there too much data or too many �observations/rows? Or is some of it not �relevant or not related to the main question?

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• Benchmark: Are there at least 50 rows, �entries or observations in your dataset?

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Evaluating Data: Velocity

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• When was the data curated or last updated?
• Does it includes real-time or recent data?
• Is the dataset relevant to the investigation period?
• How recent does the data need to be to be useful for the current investigation?

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• Benchmark: Is the dataset you found within a few years of the time period you’re interested in?

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Evaluating Data: Variety

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• What data types are included in the dataset (e.g., numerical, categorical, text, images, etc.)?
• Is the dataset structured and organized (e.g., tabular format, JSON, map)?
• Is the dataset well-documented? Are there details about the variables included?
• Is the data format consistent across observations �and easy to interpret with the available tools?

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• Benchmark: Does the dataset you’re planning �to use include at least 3 different types of variables? (Nominal, ordinal, continuous, discrete?)

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Evaluating Data: Veracity

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• Accuracy

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• Reliability

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• Completeness

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• Data biases

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Evaluating Data: Accuracy

• What do we know about where the data came from? For example, how was the data collected? By whom? For what purpose(s)?

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• Is the dataset accurate compared to known fact or external source?

Accurate, Reliable

Accurate, Unreliable

Inaccurate, Reliable

Inaccurate, Unreliable

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Evaluating Data: Reliability

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• Is the data source reliable? Is the source consistently measured? �
• Some measuring instruments and designs may reliably measure data; others may introduce measurement variability (such as a malfunctioning thermometer or poorly worded question on a survey)

Accurate, Reliable

Accurate, Unreliable

Inaccurate, Reliable

Inaccurate, Unreliable

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Evaluating Data: Completeness

• Is there any missing data?�
• Check to make sure the dataset does not have a lot of missing data points – particularly if they are enough to make the sample small, or if they are systematically missing in certain populations�
• Benchmark: Does your dataset represent all sub-populations you’re interested in?

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Evaluating Data: Data Biases

• Is the data potentially biased?�
• Are there potential biases in data sampling, reporting, or measurement? Are there any under- or over-represented populations? Are responses influenced by social context? Are tools used systematically mismeasuring a phenomenon?

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• If there are biases, how might these biases �potentially influence the results?

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Evaluating Data: Value

• Does the dataset contain the necessary information to address the scientific question or the phenomenon under study?
• Can it be analyzed to derive meaningful insights or evidence-based claims?
• Which variables are most helpful or insightful?
• What additional data could make the dataset more useful?

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Exit Ticket

• Submit a link to the dataset you chose to work with
• Answer the questions regarding the dataset evaluation
• Paste a link to your working EduBlocks program, and upload a screenshot of the program.
• Answer - Is your data "nested" in any particular way?

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Thanks!

apicancode@umd.edu

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This work was made possible through generous support from the National Science Foundation (Award # 2141655).