Data Analysis and Visualization Programming Challenge
Your task is to analyze and visualize a dataset about country delays in NSDP dissemination using Python for data processing and Power BI for visualization. The dataset is contained in a spreadsheet in our GitHub repository.
The data in the spreadsheet is fictional and does not correspond to real-world values. Here are the specific steps you need to follow to complete this challenge:
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Clone the Repository: Start by cloning our GitHub repository, which can be found at the following URL: github.com/deodoro/programming_challenge
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Analyze the Data: The spreadsheet contains data rows for each country, year, and type (periodicity and timeliness). Your task is to write a Python script to process this data. Specifically, you need to generate output for each country, calculating the average delay per year. Focus only on the 'timeliness' type for this analysis. Note: All data processing must be done in the Python script. No pre-processing or manual manipulation of the spreadsheet is allowed before running your script.
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Commit Your Changes: Once you have written your Python script and processed the data, commit your changes and push them to the repository. Ensure your commit message is clear and descriptive of the changes you have made.
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Visualize the Results in Power BI: Take the output of your script and create a Power BI report to visualize the results. The layout of this report is up to you, but it should clearly present the average delay per year for each country.
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Add Report to Repository: Once your report is complete, add the source file to the repository and commit this change.
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Create a Pull Request: After pushing all your changes (including the Power BI report), create a new pull request to the original repository. This will allow us to review your work.
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Notify Us: After you have completed all of the above steps, please send us an email to let us know that you have finished the task. We will then review your work.
For background information on NSDPs, you may refer to the DSBB website.
Please ensure your code is well-documented with clear and helpful comments, and follows best practices for Python programming. You are allowed to make use of any Python libraries that you think will assist you in this task.
Good luck! We look forward to reviewing your submission.