Jupyter Coding Challenge

At aspaara a squad of superheroes work on giving superpowers to planning teams. As part of this, we give insights into data through our product dashboar – a true super-vision superpower. Join forces with us and generate the insights of the future!

Goal

Create a simple notebook that allows a planner to get insights into client and planning information.

You will find the corresponding data to display in planning.json, which contains around 10k records.

Requirements

The notebook should

• give an overview of the data
• provide in-depth statistics for at least one of the following attributes:
  • booking grade
  • office city
  • skills
  • industry
• give insights into what you find most noteworthy ;)

Data Model

• ID: integer (unique, required)
• Original ID: string (unique, required)
• Talent ID: string (optional)
• Talent Name: string (optional)
• Talent Grade: string (optional)
• Booking Grade: string (optional)
• Operating Unit: string (required)
• Office City: string (optional)
• Office Postal Code: string (required)
• Job Manager Name: string (optional)
• Job Manager ID: string (optional)
• Total Hours: float (required)
• Start Date: datetime (required)
• End Date: datetime (required)
• Client Name: string (optional)
• Client ID: string (required)
• Industry: string (optional)
• Required Skills: array of key-value pair (optional)
• Optional Skills: array of key-value pair (optional)
• Is Unassigned: boolean (optional)

Tech Stack

• Python
• Jupyter notebook (or similar)
• any other python library you want to use

Submission

• Please fork the project, commit and push your implementation and add [email protected] as a contributor.
• Please update the README with any additional details or steps that are requried to run your implementation.
• We understand that there is a limited amount of time, so it does not have to be perfect or 100% finished. Plan to spend no more than 1-2 hours on it.

For any additional questions on the task please feel free to email [email protected].

We are looking forward to see what insights you can gain from the data!

Setup

Setting up a virtual Python environment using Anaconda:

conda create -n aspaara python=3.8 pandas matplotlib seaborn plotly notebook
conda activate aspaara