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data-tech-challenge

Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.

The Data

(see repository)

You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of

  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)

where

  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice

As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.

Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister. To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.

The Tasks:

  1. Extract a list of all distinct Jägermeister (JM) item names

  2. What are the top 10 most ordered JM items in terms of

    • item count?
    • turnover?

  3. Create a histogram illustrating the number of ordered JM items per hour.

  4. For each venue find

    4.1. the weekday on which the most JM items (in terms of item count) are ordered

    4.2. the item name which is ordered the most in terms of turnover

  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?

If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.

Hint

Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.

And now what?

Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap. Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.

Good luck and happy coding!

About

This is the tech challenge for the orderbird data team

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

6 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
About
This is the tech challenge for the orderbird data team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
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GitHub - orderbird/data-tech-challenge: This is the tech challenge for the orderbird data team · GitHub
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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
About
This is the tech challenge for the orderbird data team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - orderbird/data-tech-challenge: This is the tech challenge for the orderbird data team · GitHub
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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
About
This is the tech challenge for the orderbird data team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
About
This is the tech challenge for the orderbird data team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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})();
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try {
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var __re = new RegExp('^' + ".*" + '
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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
About
This is the tech challenge for the orderbird data team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
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data-tech-challenge
Hey Data Scientist. Welcome. Your mission, should you choose to accept it is to analyse the following data and get back to us with the results.
The Data
(see repository)
You are given a sample data dump (csv-format) comprising of ordered items of various orderbird customers. A row consists of
  • venue_id (string)
  • item_name (string)
  • datetime_ordered (string)
  • turnover (double)
  • invoice_id (string)
where
  • datetime_ordered denotes the timestamp when the item was ordered
  • turnover (in EUR) is the price
  • invoice_id is an UUID of the associated invoice
As you will see, the item names do not follow any conventions, could be misspelled or even abbreviated.
Your task is to create some statistics and figures for a report for a German beverage company called Jägermeister.
To this end, we ask you to create a jupyter/IPython notebook in which you solve the tasks listed below. Popular tools for tackling such tasks are Pandas or pySpark. If you prefer another tool, please explain why. Finally, for a better understanding of your approaches and ideas, please comment your code.
The Tasks:
  1. Extract a list of all distinct Jägermeister (JM) item names
  2. What are the top 10 most ordered JM items in terms of
    • item count?
    • turnover?
  3. Create a histogram illustrating the number of ordered JM items per hour.
  4. For each venue find
    4.1. the weekday on which the most JM items (in terms of item count) are ordered
    4.2. the item name which is ordered the most in terms of turnover
  5. BONUS: Come up with an additional (potentially interesting) insight based on the given data. What else could you compare? What would be interesting to learn for Jägermeister?
If you have any questions regarding these tasks, please just get in touch ( Tech-challenge@orderbird.com ) so we can clarify.
Hint
Regarding task no 1. : please do not aim for 100% accuracy here, since this can be a real time-sucker :-) . Start with a "good enough" solution first and describe future improvements without implementing these.
And now what?
Please send your jupyter/IPython notebook to Tech-challenge@orderbird.com and we will get back to you asap.
Please do not create a pull request or fork this repository as your solution should not be end up being public afterwards.
Good luck and happy coding!
About
This is the tech challenge for the orderbird data team
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors