Data Science Interview Questions and Answers
Question - 81 : - What are Recommender Systems?
Answer - 81 : - A recommendation engine is a system, which on the basis of data analysis of the history of users and behaviour of similar users, suggests products, services, information to users. A recommendation can take user-user relationship, product-product relationships, product-user relationship etc. for recommendations.
Question - 82 : - List out the libraries in Python used for Data Analysis and Scientific Computations.
Answer - 82 : - The libraries NumPy, Scipy, Pandas, sklearn, Matplotlib which are most prevalent. For deep learning Pytorch, Tensorflow is great tools to learn.
Question - 83 : - State the difference between the expected value and the mean value.
Answer - 83 : - Mathematical expectation, also known as the expected value, is the summation or integration of possible values from a random variable. Mean value is the average of all data points.
Question - 84 : - How are NumPy and SciPy related?
Answer - 84 : - NumPy and SciPy are python libraries with support for arrays and mathematical functions. They are very handy tools for data science.
Question - 85 : - What do you mean by list comprehension?
Answer - 85 : - List comprehension is an elegant way to define and create a list in Python. These lists often have the qualities of sets but are not in all cases sets. List comprehension is a complete substitute for the lambda function as well as the functions map(), filter(), and reduce().
Question - 86 : - What is the difference between append() and extend() methods?
Answer - 86 : - append() is used to add items to list. extend() uses an iterator to iterate over its argument and adds each element in the argument to the list and extends it.
Question - 87 : - How will you read a random line in a file?
Answer - 87 : -
import random
def random_line(fname): lines = open(fname).read().splitlines()
return random.choice(lines) print(random_line('test.txt'))
Question - 88 : - Whenever you exit Python, is all memory de-allocated?
Answer - 88 : - Objects having circular references are not always free when python exits. Hence when we exit python all memory doesn’t necessarily get deallocated.
Question - 89 : - How would you create an empty NumPy array?
Answer - 89 : -
"import numpy as np
np.empty([2, 2])"
Question - 90 : - Treating a categorical variable as a continuous variable would result in a better predictive model?
Answer - 90 : - There is no substantial evidence for that, but in some cases, it might help. It’s totally a brute force approach. Also, it only works when the variables in question are ordinal in nature.