Grand Rounds Interview Questions

Grand Rounds Interview Guides

Click or hover over a slice to explore questions for that topic.
Data Structures & Algorithms
(16)
Machine Learning
(12)
Behavioral
(4)
Statistics
(4)
Business Case
(4)

Grand Rounds Interview Questions

Practice for the Grand Rounds interview with these recently asked interview questions.

QuestionTopicDifficulty
Machine Learning
Hard

Implement the k-means clustering algorithm in python from scratch, given the following:

  • A two-dimensional NumPy array data_points that is an arbitrary number of data points (rows) n and an arbitrary number of columns m.
  • Number of k clusters k.
  • The initial centroids value of the data points at each cluster initial_centroids.

Return a list of the cluster of each point in the original list data_points with the same order (as a integer).

Example

before clustering

After clustering the points with two clusters, the points will be clustered as follows.

after clustering

Note: There could be an infinite number of separating lines in this example.

Example


#Input
data_points = [(0,0),(3,4),(4,4),(1,0),(0,1),(4,3)]
k = 2
initial_centroids = [(1,1),(4,5)]


#Output 

k_means_clustering(data_points,k,initial_centroids) -> [0,1,1,0,0,1]

SQL
Easy
A/B Testing
Medium
Loading pricing options

View all Grand Rounds, Inc. questions

Challenge

Check your skills...
How prepared are you for working at Grand Rounds, Inc.?

Grand Rounds Salaries by Position

$156K
Data Scientist
Median: $156K
Mean (Average): $156K
Data points: 9

Most data science positions fall under different position titles depending on the actual role.

From the graph we can see that on average the Data Scientist role pays the most with a $155,800 base salary while the Data Scientist role on average pays the least with a $155,800 base salary.

Discussion & Interview Experiences

?
There are no comments yet. Start the conversation by leaving a comment.

Discussion & Interview Experiences

There are no comments yet. Start the conversation by leaving a comment.

Jump to Discussion