All our efforts, perseverance and training boil down to one moment – the moment of truth – our interview with a potential recruiter. You would have gone to training institutes, worked intensely on your communication skills, grooming, and spent the entire night on fine tuning your technical expertise on a subject just for that one big break in your career. However, when you reach the company, submit your resumé and sit for the interview with the boss, he stumps you with a question you least expected.

This has been predominantly happening in the analytics sector, where the industry is booming massively and companies from startups to tech giants are excited at employing skilled data scientists to work on niche responsibilities. Since the industry is completely technical and data scientists need to be multidisciplinary, companies really take their interview sessions up a notch and ensure only the best candidates make it through to the final round.

Apart from just Big Data, companies are keen on knowing if candidates know Machine Learning, multiple analytics tools, artificial intelligence, IoT and more and play around these topics to really gauge a candidate’s competency.

Also, the role of a data scientist is very crucial to business organizations. Most of their concerns will be addressed and resolved by data scientists and if they need to take informed business decisions, they need to have the best data scientists in their team. That’s why the interviews in analytics tend to be quite complicated. The interviews give equal preferences to communication skills, aptitude, attitude, business acumen and technical knowledge of candidates.

But you don’t have to get alarmed by this. Every question that is shot at you is from what you would’ve already learned. If you’re someone preparing for an analytics interview, we have compiled some of the most common questions asked and this will give you an idea of the approaches recruiters take in assessing your expertise. Check them out.

  1. What do you mean by the term linear regression?

It is a technique in statistics to model the connection or relationship between one or multiple exploratory variables, attributed as x, and a scalar dependant variable attributed as y.

  1. What is the difference between extrapolation and interpolation?

You have a list of values and when you want to estimate a value from two known values from the list, it’s called interpolation. When you extend known sets of facts or values to approximate a value, it’s called extrapolation.

  1. What is the purpose of A/B testing?

The purpose of A/B testing is to generate crucial insights by testing two variables (A and B) of a purpose-driven campaign. The purpose is to identify which variable performs better than the other and achieves a set goal. This paves way for informed decisions.

  1. How different is a mean value different from expected value?

Mean and expected values are similar but are used in different contexts. While expected values are usually referred to in a random variable context, mean values are referred in the contexts of sample population or probability distribution.

  1. Why is it mandatory to clean a data set?

Cleaning data makes it into a format that allows data scientists to work on it. This is crucial because if data sets are not cleaned, it may lead to biased information that can alter business decisions. Over 80% of the time is spent by data scientists to clean data.

  1. What are the steps involved in analytics projects?

Any analytics problem involves the following steps:

  • Understanding a business problem
  • Data exploration
  • Data preparation for modeling
  • Running the model and analysis of results
  • Model validation using new data sets
  • Model implementation and tracking of results for a set period of time
  1. What do you understand by the term recommender systems?

Recommender systems are part of an information filtering system that is used to predict and anticipate the ratings or preferences a user is most likely to give to a product or service. You can see recommender systems at work on eCommerce websites, movie websites, research articles, music apps, news and more.

  1. If you had to choose between the programming languages R and Python, Which one would you use for text analytics?

Personally, I would choose Python for text analytics as it offers solid data analysis tools and simple data structures, thanks to its Panda library.

  1. For linear regression, what are some of the assumptions a data scientist is most likely to make?

Some of the assumptions include the following:

  • Linear relationship
  • Multivariate normality
  • No auto-correlation
  • No or little multicollinearity
  • Homoscedasticity
  1. How do you find the correlation between a categorical variable and a continuous variable?

It is possible to find the correlation between a categorical variable and a continuous variable using the analysis of covariance technique.

So, these were some of the most common analytics interview questions. Apart from these, there are also questions like coding and writing a program on languages. If you didn’t know answers to these questions, read and understand them. If you know, pass this on to people you think would benefit from this. Also, if you’ve been asked any unique analytics question, share it in the comments below.

Good luck!

Also Read 

The A to Z of Analytics: The Complete List of Analytics Terms

7 Core Articles on Data Science You must Read


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