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exercise.txt
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exercise.txt
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Welcome to the TaskRabbit data analysis exercise. We appreciate your
time and effort in completing this exercise. It helps TaskRabbit
focus on candidates who are the best fit for the Data Analyst role,
and also helps the candidate get an idea of the type of data we work
with and the technical skills required to be successful in the role.
For this exercise, a sample data file is provided which contains
information about "recommendations". A "recommendation" is the group
of Taskers from which the Client can choose one to book.
The sample data file is a CSV file of the form:
recommendation_id timestamp tasker_id position hourly_rate num_completed_tasks hired category
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1009185352 1 38 151 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1006892359 2 40 193 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1012023956 3 28 0 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1009733517 4 43 303 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1013579273 5 29 39 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1012043028 6 28 2 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1013470741 7 29 9 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1009557645 8 29 8 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1010800768 9 28 0 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1009072269 10 35 59 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1008733253 11 40 68 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1009198916 12 27 17 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1009856900 13 50 914 1 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1012314913 14 29 5 0 Furniture Assembly
0-0-70cf97d7-37af-4834-901c-ce3ad4893b8c 2017-09-01 00:32:25 1013307670 15 25 0 0 Furniture Assembly
The columns are as follows:
recommendation_id unique identifier for this recommendation, or set of taskers shown
timestamp when this recommendation was shown to the client
tasker_id unique identifier for the Tasker
position the position of the Tasker in the recommendation set, 1 - first, 2 - second, etc.
hourly_rate the hourly rate for the Tasker when they were shown
num_completed_tasks the number of Tasks the Tasker had completed in that category, when they were shown
hired was the Tasker hired or not? Only 1 tasker out of a set of recommendations can be hired
category the category of work the Client needs help with
Please answer the following questions about the data in the CSV sample file:
1. How many recommendation sets are in this data sample?
2. Each recommendation set shows from 1 to 15 Taskers, what is:
- average number of Taskers shown
- median number of Taskers shown
3. How many total unique Taskers are there in this data sample?
4. Which Tasker has been shown the most?
Which Tasker has been shown the least?
5. Which Tasker has been hired the most?
Which Tasker has been hired the least?
6. If we define the "Tasker conversion rate" as the number of times a
Tasker has been hired, out of the number of times the Tasker has
been shown, how many Taskers have a conversion rate of 100%
7. Would it be possible for all Taskers to have a conversion rate of 100%
Please explain your reasoning.
8. For each category, what is the average position of the Tasker who is hired?
9. For each category, what is the average hourly rate and average number of completed tasks for the Taskers who are hired?
10. Based on the previous, how would you approach the question of:
How can we use market data to suggest hourly rates to Taskers that would
maximize their opportunity to be hired?
Please describe in detail, with code and formulas that support your model.