Machine Learning is the main part of artificial intelligence. It creates computers get into a self-learning mode without clear programming. When machine learning served new data, these computers learn, grow, change, and develop by themselves. Machine Learning is the most interesting concept in recent days; it has been around for a while now. However, the ability to repeatedly and quickly apply mathematical calculations to big data is now ahead a bit of drive.
Data Science is one of the most dynamic fields in technology attracting innumerable candidates towards it. However, not everyone ends up landing on a good Data Scientist profile. With the cut-throat competition among the candidates, you need to have the edge to have an upper hand. Therefore, it is very much important for the aspirants to know those common and tricky questions that are asked by the interviews. Before going through the Interview question, it is suggested that you get you acquire the fundamental knowledge of Data Science.
DataFlair has published a series of top data science interview questions and answers which contains 130 questions of all the levels. This is the second part of the Data Science Interview Questions and Answers series. In our first part, we discussed some basic level questions which could be asked in your next interview, especially if you are a fresher in Data Science. Today, I am sharing the top 71 Data Science Interview Questions and Answers. This is the only part where you will get best scenario-based interview questions for data scientist interviews. A Data Science Interview is not a test of your knowledge, but your ability to do it at the right time. Every data science interview has many Python-related questions, so if you really want to crack your next data science interview, you need to master Python. Q.1 What is a lambda expression in Python? With the help of lambda expression, you can create an anonymous function. Unlike conventional functions, lambda functions occupy a single line of code. We obtain the output of 25. Q.2 How will you measure the Euclidean distance between the two arrays in numpy?
Python's growing adoption in data science has pitched it as a competitor to R programming language. With its various libraries maturing over time to suit all data science needs, a lot of people are shifting towards Python from R. This might seem like the logical scenario. But R would still come out as the popular choice for data scientists. People are shifting towards Python but not as many as to disregard R altogether. We have highlighted the pros and cons of both these languages used in Data Science in our Python vs R article.
We will start with Python Installation and a few basics of Python. Once you reach here you can start the new journey to learn domain-specific python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras for machine learning. By the end of the course, you'll be able to apply in confidence for Python programming jobs with the right skills which you will learn in this course. Here's what a few students have told us about the Python programming course after going through it "This course is so recommended to anyone who wants to learn python. It clearly teaches you several important things even experts fail to deliver. It also teaches so many different ways and how to tackle some interview questions. Very thorough and easy to understand. "That was a very thorough and informative course.