conduct some representative data manipulation, visualization, and analysis tasks using publicly available data analytic technologies.

Thank you for taking the time to complete the technical interviews thus far to explore Data Analysis opportunities with Capital One. I have enjoyed speaking with you and look forward to advancing you forward to complete our Data Challenge as the next step in this process.
The Data Challenge (attached) will give you an opportunity to showcase your skills and abilities in 3 areas that align with how we challenge our Data Analysts at Capital One. We need great people to join our team developing software data products across these key areas:
Builder Mindset: Leverages creative and adaptive problem solving to selecting the right tool for the job; seeks automated and efficient solutions to manual or repetitive processes.
Data Management: Strategically leads efforts to systematically evaluate, and document; monitors our data in a sustained and organizationally recognized way.
Business Intent: Translates business needs into actionable solutions or data products; effectively communicate results to stakeholders and technical partners.
During the challenge, you will be asked to conduct some representative data manipulation, visualization, and analysis tasks using publicly available data analytic technologies. Below you will find attached all of the documents and data sets required as part of this challenge. Also attached are the instructions to guide you through the Data Challenge and more information about how you should think about choosing your toolkit.
Please note that the Data Challenge is not timed.  You will be judged on the quality of the work you submit and not on the time spent to complete the challenge.  We ask that you return the project within 10 days and we recommend planning for about 8-10 hours to complete the challenge.  There are a number of dimensions of quality that are described in the attached instructions, but in brief, we are looking for efficient, repeatable, and well-documented solutions.
We encourage the reuse of code when appropriate. If you include code directly in your submission that was written by someone else (not including imported modules), please be sure to provide proper attribution; include the URL, text, author, etc. in the code comments.

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