Operational Lead Data Scientist Interview Questions & Answers:

1. Please explain star schema?

It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.

2. Do you know what are Recommender Systems?

Recommender systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.

3. Tell us what are Recommender Systems?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

4. Explain me what are your technical competencies?

Before the interview, do your homework on the analytics environment that the interviewing company uses. During the IT interview, you will be asked to review your technical competencies and skillsets. How well the company feels your technical skills fit with the data analytics approaches and tools they use in their environment can have a make-or-break effect on whether you get the job.

5. Please explain what is Collaborative Filtering?

The process of filtering used by most recommender systems to find patterns and information by collaborating perspectives, numerous data sources, and several agents.

6. Tell us what are Eigenvalue and Eigenvector?

Eigenvectors are for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvalues are the directions along which a particular linear transformation acts by flipping, compressing or stretching.

7. Do you know what is selection Bias?

Selection bias occurs when sample obtained is not represantative of the population intended to be analyzed.

8. Tell me do gradient descent methods at all times converge to a similar point?

No, they do not because in some cases they reach a local minima or a local optima point. You would not reach the global optima point. This is governed by the data and the starting conditions.

9. Can you please explain survivorship bias?

It is the logical error of focusing aspects that support surviving some process and casually overlooking those that did not because of their lack of prominence. This can lead to wrong conclusions in numerous different means.

10. Do you know what is the Law of Large Numbers?

It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample mean, the sample variance and the sample standard deviation converge to what they are trying to estimate.