Data Science Interview Questions For 2022

Data Science is among the leading and most popular technologies in the world today. Major organizations are hiring professionals in this field. With high demand and low availability of these professionals, Data Scientists are among the highest-paid IT professionals. This Data Science Interview preparation blog includes most frequently asked questions in Data Science job interviews.  

Q1)What is Logistic Regression? What is the loss function in LR? 
 Logistic Regression is the Binary Classification. It is a statistical model that uses the logit function on the top of the probability to give 0 or 1 as a result. 
 The Loss Function in LR as the Log Loss Function. The equation for which is given as:
Q2) What is Linear Regression. What are the assumptions involved in it?
Linear Regression is a mathematical relationship between an independent and dependent variable. The relationship is a direct proportion, relation making it the most simple relationship between the variables.
Y=mX+c
Y - Dependent Variable
X - Independent Variable
m and c are constants.

Assumptions of Linear Regression:
1) The relationship between Y and X must be linear.
2) The features must be independent of each other.
3) Homoscedasticity - The variation between the output must be constant different input data.
4) The distribution of Y and X should be the Normal Distribution.

Q3) What is Natural Language processing? State some real life examples of NLP.
Natural Language Processing is a branch of Artificial Intelligence that deals with the conversation of Human Language to Machine Understandable language so that it can be processed by ML models.

Examples – NLP has so many practical applications including chatbots, google translate,  and many other real time applications like Alexa.

Q4) What is Overfitting ?
Overfitting in Machine Learning occurs when your model is not generalized well. The model is too focused on the training set. It captures a lot of detail or even noise in the training set. Thus, it fails to capture the general trend or the relationships in the data. If a model is too complex compared to the data, it will probably be overfitting.
A strong indicator of overfitting is the high difference between the accuracy of training and test sets. Overfit models usually have very high accuracy is usually unpredictable and much lower than the training accuracy.

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