Data Scientist Interview Questions

Anmol R. - "The example given is wrong. The 2nd test case should have answer 3, as we can get to 6 by using 3 coins of denomination 2."See full answer

Anonymous Flamingo - "In Python, an "oops" (Object-Oriented Programming) concept refers to a programming paradigm that is based on the idea of objects and classes. OOP allows developers to model real-world concepts and create reusable code blocks through the use of inheritance, polymorphism, and encapsulation. Here are some common OOP concepts in Python: Class: A class is a blueprint for creating objects. It defines the attributes and behaviors that objects of that class will have. Object: An object is an insta"See full answer

Vaibhav D. - "Make current as root. 2 while current is not null, if p and q are less than current, go left. If p and q are greater than current, go right. else return current. return null"See full answer

Chen J. - "Leetcode 347: Heap + Hashtable Follow up question: create heap with the length of K instead of N (more time complexity but less space )"See full answer

Sean L. - " import pandas as pd from datetime import datetime def findfastestlike(log: pd.DataFrame) -> pd.DataFrame: log=log.sortvalues(['userid','timestamp']) #get the prev event, time by user log['prevevent'] = log.groupby('userid')['event'].shift(1) log['prevtimestamp'] = log.groupby('userid')['timestamp'].shift(1) True only on rows where the previous event was a login and the current event is a like log['loginlike'] = (log['prevevent'] == 'log"See full answer

Sean L. - " debug your code below departments = pd.DataFrame({ 'id': [1, 2, 3, 4, 5], 'name': ['Reporting', 'Engineering', 'Marketing', 'Biz Dev', 'Silly Walks'] }) employees = pd.DataFrame({ 'id': [1, 2, 3, 4, 5, 6], 'first_name': ['John', 'Ava', 'Cailin', 'Mike', 'Ian', 'John'], 'last_name': ['Smith', 'Muffinson', 'Ninson', 'Peterson', 'Peterson', 'Mills'], 'salary': [20000, 10000, 30000, 20000, 80000, 50000], 'department_id': [1, 5, 2, 2, 2, 3] }) projects = p"See full answer

Sai R. - "def countuniqueoutfits(totalpants: int, uniquepants: int, totalshirts: int, uniqueshirts: int, totalhats: int, uniquehats: int) -> int: """ Number of unique outfits can simply be defined by (uniquepantschoose1uniqueshirtschoose1uniquehatschoose_1) (uniquepantschoose1*uniqueshirtschoose1) # Not wearing a hat nchoosek is n """ res = (uniquepants*uniqueshirtsuniquehats) + (uniquepantsunique_shirts) return res print(countuniqueoutfits(2, 1, 1, 1, 3, 2))"See full answer

Anonymous Hornet - "label #shape = [batchsize, posids] X #shape = [batch_size, ch] hidden = nn.Linear(batchsize, ch) #shape = [batchsize, ch] logits = nn.Linear(ch, C) #shape = [ch, C] #modifications start here neg_ids = torch.randint(0, C, 0.1 * C) #e.g. if we want to randomly sample only 10% of C outputneg = np.dot(logits[:, negids], hidden(X)) #shape [batch_size, C1] outputpos = np.dot(logits[:, posids], hidden(X)) #shape [batch_size, C2] output = torch.concat(outputpos, outputneg) #concatenate out"See full answer

Iris F. - "#include #include #include using namespace std; void printComs(int prev, int start, int end, int target) { if (start >= end) return; while (start target) { end--; } else { st"See full answer