Data Scientist Interview Questions

Saurabh K. - "Goals : Determine if the TV series should be renewed If it should be renewed, how much should Netflix be willing to pay for this series Let's assume that the goal is to maximize subscriber retention and engagement while paying a reasonable amount for the licensing costs that is justified by the value added by the series. Assumptions : The show is exclusive to Netflix for a particular region (for eg. US) It has been on the platform for an year Netflix has subscriber level data around"See full answer

Judy W. - "Situation: COVID has impacted everyone's lives, especially small businesses. Earlier this year, during the second lockdown in Malaysia, it was estimated that 50%-70% of small businesses have closed. It got me thinking, beyond the existing training programmes, what can my company do to support small businesses? Task: So, I took the initiative to gather our Comms and Government Affairs team, to work together and explore how we can: 1) meaningfully demonstrate our company's commitment in"See full answer

Laksitha R. - "public static int maxProfitGreedy(int[] stockPrices) { int maxProfit = 0; for(int i = 1; i todayPrice) { maxProfit += tomorrowPrice - todayPrice; } } return maxProfit; } "See full answer

Jiin S. - "I would use A/B testing to see if the new feature would be incrementally beneficial. To begin the testing, we should define what's the goal of this testing. Let's say the new feature would increase the average number of trade by X. Then randomly assign the clients to two groups, control and test group. Control group doesn't see the new feature and the test group see the new feature. We could also stratified sampling if we want to make sure cover different customer segmentation. During this desig"See full answer

Jerry O. - "\# Definition for a binary tree node. class TreeNode: def init(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def maxPathSum(self, root: TreeNode) -> int: self.max_sum = float('-inf')"See full answer

Rick E. - " from typing import List def getnumberof_islands(binaryMatrix: List[List[int]]) -> int: if not binaryMatrix: return 0 rows = len(binaryMatrix) cols = len(binaryMatrix[0]) islands = 0 for r in range(rows): for c in range(cols): if binaryMatrixr == 1: islands += 1 dfs(binaryMatrix, r, c) return islands def dfs(grid, r, c): if ( r = len(grid) "See full answer

Vineet M. - "The distribution of daily minutes spent on Facebook per user is heavily right-skewed with a long tail. Most users spend a short amount of time while a smaller segment of heavy users push up the average with 2–3+ hours daily."See full answer

Cagdas A. - " class ListNode: def init(self, val=0, next=None): self.val = val self.next = next def has_cycle(head: ListNode) -> bool: pass # your code goes here if head is None: return False previousNodes = set() iter = head while iter: if iter.val in previousNodes: return True previousNodes.add(iter.val) iter = iter.next; return False debug your code below node1 = ListNode(1) node2 = ListNode(2) n"See full answer

Anonymous Roadrunner - "from typing import List def find_primes(n: int) -> List[int]: if n < 2: # Handle edge cases explicitly return [] Initialize a boolean list to track primality is_prime = [True] * (n + 1) isprime[0] = isprime[1] = False # 0 and 1 are not primes p = 2 while p * p <= n: # Loop until sqrt(n) if is_prime[p]: # Only process if 'p' is still marked as prime Mark multiples of p as non-prime for multiple in range(p * p,"See full answer

Abdurhman M. - "If you effectively listen and understand their point of view, then take action to address the issue quickly. Don't let too much time slip between the conflict and the resolution. If resolving the concern will take more time, communicate the current status and next steps with the stakeholder."See full answer

Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias. Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit. There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer

Anonymous Roadrunner - "from typing import List def two_sum(nums: List[int], target: int) -> List[int]: prevMap = {} for i, n in enumerate(nums): diff = target - n if diff in prevMap: return [prevMap[diff], i] else: prevMap[n] = i return [] debug your code below print(two_sum([2, 7, 11, 15], 9)) `"See full answer

Bhavna S. - " with youngsuccrate as( select strftime('%m', postdate) AS postmonth, round(sum(issuccessfulpost)*1.0/count(issuccessfulpost),2)as yascrate from post where userid in (select userid from post_user where age between 0 and 18) group by post_month ), nonyoungsucc_rate as( select strftime('%m', postdate) AS postmonth, round(sum(issuccessfulpost)*1.0/count(issuccessfulpost),2)as nonyasc_rate from post where user_id in (select"See full answer

G B. - " select user_id, b.marketing_channel from user_sessions a Left join attribution b on b.sessionid = a.sessionid group by 1,2 HAVING sum(purchasevalue)>100 and min(adclick_timestamp) `"See full answer

Kanishkan V. - "I responded with a project that I was a part of during my capstone class. I described how I used HTML, Python, and PostGRESQL in conjunction to create a functioning website using SCRUM."See full answer