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
data analysts作业代写、代做Python，Java程序语言作业、Java/Python课程设计作业代写
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
Capital One Confidential
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.)
Capital One Confidential
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
Capital One Confidential
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
Capital One Confidential
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
data analysts作业代写、代做Python，Java程序语言作业、Java/Python课程设计作业代写
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
Capital One Confidential
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.)
Capital One Confidential
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
Capital One Confidential
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
Capital One Confidential
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
http://www.6daixie.com/contents/9/3673.html