Manual Scoring of Data Science Questions

Overview

This article explains the different evaluation methods to enable filtering candidates at scale. HackerRank provides candidate performance scores based on model performance metrics applicable to the Data Science questions. As Data Science evaluation is very subjective and emphasizes the approach a candidate takes to solve a question, HackerRank recommends that Hiring Managers evaluate filtered candidate submissions manually.

Data Science questions are scored primarily after thoroughly analyzing the candidates' solution Jupyter Notebook, available in the candidates' Test Report. 

You can read more about accessing and evaluating a Candidate's Test Report here. 

Manually Scoring a Data Science Question

Prerequisites

• You must have a HackerRank for Work account.
• You must have at least one test attempted by Candidates and their submissions pending for further evaluation.

Scoring Using the HackerRank Scoring Rubric

Data Science questions are manually evaluated. Therefore, the candidate test report experience offers a scoring rubric for each question to help the Hiring Manager perform an efficient, consistent manual evaluation of data science solutions. 

To access the scoring rubric, click on the Interviewer Guidelines section of the detailed candidate report tab. Use the solution Jupyter Notebook, the evaluation script, and so on for all HackerRank Data Science questions.

1. Navigate to Tests and select the required Test. 
2. Click the Candidates tab, and select a Candidate entry pending evaluation. 
3. On the Candidates Test Summary page, click View report for a particular question. 
   
   
4. For a detailed Candidate Test Report, expand the Problem Statement.
5. Here you can view the question description.
6. Scroll down to the Interviewer Guidelines (containing the solution Notebook, the evaluation script, and so on for all the library Data Science questions). You can also review the candidate's submitted Jupyter notebook by either downloading a zip file or launching a temporary Jupyter session with the candidate's code.
7. Launching the Jupyter session enables you to review all the files in the submission, run data cells, run any scripts, and try different aspects of the candidate submission.
8. Upon completing your evaluation, manually enter your score, and leave any candidate feedback. This completes the scoring for the particular question.

Learn about how to analyze a Data Science Test Report in detail here.

HackerRank also offers auto-scoring for Data Science questions. Learn more about it here