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Are you a data science student and ready to take the plunge into this field brimming with job opportunities? The first step for you is to invest your time into data science interview preparation.
Today, we’ll help you get closer to your dream job by listing out the most commonly asked data science interview questions and answers.
So, let’s get started.
What do you understand by data science? How can you differentiate between supervised and unsupervised learning?
A classic opening question asked in data analytics interview is what is Data Science?
You can prepare for a text book answer for the same. However, citing examples from real life will show the interview panel how you apply your theoretical knowledge to a real-world situation. You can start by defining data science.
Data Science is a field involving automated methods used to analyze data and extract knowledge. It is a combination of different fields like computer science, statistics and mathematics, visualization and one has to deal with vast amounts of data to draw insights useful for commercial and social purposes.
You can take an example of using the weather data generated over months to predict and forecast weather conditions in future.
What is the definition of Normal Distribution and how do you apply it?
The interview panel may be keen on testing your statistics conceptual knowledge. One commonly asked question is about Normal Distribution.
It is a fact that data can be distributed in various ways; it could be jumbled or biased to either left or right. Statistically, the data will be least affected in the centre and show an unbiased value as the normal distribution curve is in the shape of a bell. The normal distribution is almost symmetrical or flat where the mean equals the median value. The data point that is far away from mean has the least possibility to occur.
What is A/B testing. Why is it used?
This is a hallmark question in every data science interview. A/B testing is a concept used in many fields be it research or program testing, even Google Analytics employs it to test the performance of two pages. However, in data analytics it is also known as randomized controlled trial or RCT. A/B testing is a tool employed for product development and estimating the effect size. Another key use of A/B testing is to obtain the causality in data analytics.
How important is Python when considering data analysis?
Python is very important to data scientists as it’s a robust tool with numerous applications used in data science. It is also used in machine learning and deep learning. One benefit of Python that stands out is that it is efficient in data manipulation and performing repeated tasks.
To master Python for data analysis one must have a good understanding of built in data types and knowledge of N-dimensional NumPy arrays. Python adds a new dimension to traditional software coding concepts like ‘to’ and ‘for’ loops with NumPy arrays and matrix operations.
What are the types of biases that can occur during sampling?
The interviewers are keen to test your approach along with conceptual knowledge. Since data analysis is always susceptible to different types of biases, it is important to understand these biases than can occur during sampling. There are broadly three types of biases that can occur, namely Selection bias, Under coverage bias and Survivor bias. For instance, survivor bias occurs by focusing on supporting some processes that have survived and overlooking the one that did not. Hence called survivor bias. It can lead to wrong conclusions in numerous different means.
All in all, these questions can give you a start to preparing your strategy for cracking your Data Science interview. The mantra is to keep preparing and looking up for newer trends. Good Luck.