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日期：2019-07-13 10:55

Challenge and Expectations

Isn’t data just more fun when you can interact and play with it? Well, that’s exactly how

we challenge our data analysts at Capital One. We need to find great people to join our

team as we develop software data products across our three key areas of data work:

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.

This challenge is your next step in showing Capital One what you can do. After

receiving data instructions you’re putting hands-to-keyboard and have 1 week to submit

a working data product, per the submission instructions, including:

Working code with documentation

Documentation of metadata and data quality

Visualizations of key insights

Ready to show off your data chops? Let’s go!

Problem Statement and Instructions

Problem Statement

You are consulting for a real estate company that has a niche in purchasing properties to

rent out short-term as part of their business model specifically within New York City. The

real estate company has already concluded that two bedroom properties are the most

profitable; however, they do not know which zip codes are the best to invest in.

The real estate company has engaged your firm to build out a data product and provide

your conclusions to help them understand which zip codes would generate the most

profit on short term rentals within New York City.

. You will be looking at publicly available data from Zillow and AirBnB:

Cost data: Zillow provides us an estimate of value for two-bedroom properties

Revenue data: AirBnB is the medium through which the investor plans to lease

out their investment property. Fortunately for you, we are able to see how

much properties in certain neighborhoods rent out for in New York City

You can assume an occupancy rate of 75% or you can come up with your own

model to calculate occupancy; just let us know how you came to that

calculation

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After meeting with the strategy team, you’ve got an idea of where to start, key concerns,

and how you can help this real estate company with the market data while keeping the

following assumptions in mind:

The investor will pay for the property in cash (i.e. no mortgage/interest rate will

need to be accounted for).

The time value of money discount rate is 0% (i.e. $1 today is worth the same

100 years from now).

All properties and all square feet within each locale can be assumed to be

homogeneous (i.e. a 1000 square foot property in a locale such as Bronx or

Manhattan generates twice the revenue and costs twice as much as any other

500 square foot property within that same locale.)

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Instructions

As you start the challenge, realize that this is real-world, imperfect data. We recommend

planning about 4 hours to complete the Data Challenge, but it’s not timed, and you are

judged on the quality of the work submitted. If you find yourself uncertain of what the

“right” answer is, use your best judgment, make an assumption (document the

assumption), and keep going.

Overall, we first ask you to show your data skills in three areas at a basic level, and then,

in the last step, tell us what you would do next to provide a better conclusion.

1. Quality Check – bad data is worse than no data at all

a. Understand the data while keeping your final output in mind

b. Highlight two to three data quality insights based on your analysis of the

data

c. Create metadata for any derived fields or metrics used to complete your

analysis

2. Data munging – get the data

a. The datasets do have different units of time – in order to complete the

analysis, you will need to determine a common unit of time

b. Write a function that can link the data together in a scalable way when

new data is available or for when we are ready to approach a new market

3. Craft a visual data narrative – Charts and plots must be generated from your

code; not from produced in external standalone software like Excel

a. Visualize metrics for profitability on short term rentals by zip code

b. Summarize your key insights and conclusions based on the data and your

analysis

4. What’s Next – We recognize that 4 hours isn’t a lot of time… and you’ve

probably come up with a number of great ideas from an analytical or visualization

perspective that you don’t have time to do. Tell us (but don’t do any work) what

you would/could do next to inform a better decision or deliver a better product to

the real estate company.

Data and Tools

Solutions that require purchase of a software license or purchased access to data will

not be accepted regardless of whether or not Capital One uses said software or data.

Abide by all applicable laws and regulations regarding the use of software or external

data sources. If you have questions about a particular software package, please contact

your recruiter immediately.

Data

Downloading the data is a simple two-step process: Please use the same web browser for

both links.

1. Please access the Capital One Data Challenge GitHub account via

https://github.com/login using the Username and Password provided in the email

from your recruiter.

2. Once logged in GitHub, please copy and paste the following link into your web

browser and press enter to download the ZIP file.

a. https://github.com/c1-data-analytics/airbnb-zillow-datachallenge/archive/master.zip

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The ZIP file will contain the following list of data and metadata files necessary for you to work

through this Data Challenge. Please do not change the username or password while accessing

this account.

Data

Resource You should see

This document AirBnB_Zillow – Data Challenge.docx

Technical Considerations Data_Challenge_Technical_Considerations.html

Main input data sets AirBnb

Link provided in “AirBnB Dataset Link.txt” file.

Copy and paste the link and the download will

begin

Zillow

Zip_Zhvi_2bedroom.csv.zip

Metadata AirBnB_Zillow – Metadata.docx

Tools

Here are some example platforms you should feel free to use. By no means are you

limited to this list, and our solution review team will be able to evaluate solutions in most

languages. If you really do have a question about the platform you would like to use to

solve the problem, contact your recruiter with the exact setup you’d like to use (including

OS and specific versions when applicable), your backup choice, and they can seek

verification for the platform

Platform example Notable packages

Anaconda Python Distribution notebook, pandas, matplotlib, bokeh

R R, Shiny, plyr, ggplot

Javascript D3, nvd3, node.js, Tableau

Java virtual machine Groovy, Scala

Other software packages with which you are familiar

How to submit

Congratulations on completing the Data Challenge! Please see the

following instructions for how to submit your work.

Submission is easy – just email to dataanalysisrecruitingmailbox@capitalone.com a

single ZIP file (< 10 MB) containing:

1. Working source code file with documentation

Code

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Source documentation (e.g., a README file)

Any generated graphics files

If you added data: if you added more than a couple of MB of data, provide

a program or script, with documentation, to download the data set

2. Documentation including metadata for any data created and your data quality

insights

3. Visualizations and key insights from those visualizations

Please do not post your code or documents to any public repositories.

Acknowledgements

The data for this challenge were sourced from:

Zillow Group, Inc. (2016)

Airbnb