Airbnb Challenge and Expectations

浅浅的花香味﹌ 2021-11-17 00:52 315阅读 0赞

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

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转载于:https://www.cnblogs.com/mkka/p/11202959.html