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8 Interview Questions & Answers to help you crack your next Data Analytics Interview

We all know how important it is to be well prepared before an interview and Data Analytics is no exception. You need to be thoroughly prepared and get you started, we have compiled a list of the top 8 Data Analytics Interview Questions and Answers.

1. Can you point out some statistical methods required to be used by a data analyst?

Some of the popular statistical methods that are usually used by data analysts are:
• Bayesian method.
• Simplex algorithm.
• Mathematical optimisation.
• Imputation techniques.
• Markov process.
• Spatial and cluster processes.
• Rank statistics, percentile, outlier detection.

2. Can you define the process of ‘clustering’? Please touch upon the various properties of clustering algorithms also.

Clustering can be defined as a method in which data is segregated into a series of clusters and groups. Accordingly, a clustering algorithm is characterised by the following four properties:

• Hard and soft
• Hierarchical or flat
• Disjunctive
• Iterative

3. Can you explain the data validation methods usually used by data analysts?

There are two prominent ways which are used by data analysts to approach data validation:

Data screening – This process involves screening or monitoring the data to check for the presence of any possible errors and subsequently getting rid of them before data analysis can be conducted.
Data verification – This process is usually conducted to verify the accuracy of data and to get rid of inconsistencies, if any, once the data migration has been completed.  

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4. Can you explain imputation and list out the various imputation techniques?

Imputation is used to replace missing information with substituted values. There are different imputation techniques such as:
• Single imputation.
• Hot-deck imputation.
• Cold-deck imputation.
• Regression imputation.
• Mean imputation.
• Stochastic regression.
• Multiple Imputation.

5. Can you explain what “Outlier” means and name the di8fferent types of outliers?

An outlier is a common concept often used by data analysts to refer to a value that appears to be diverging away from a set pattern in a sample. There are two distinct types of outliers:
• Univariate
• Multivariate

6. Please point out some of the tools that are used for Big Data.

Big Data analytics have become an integral component of most business’ workflow these days.

Accordingly, some of the most popularly used tools are:
• Mahout
• Cassandra
• Storm
• Apache Hadoop
• Flink
• Flume
• Hive
• Qubole
• CouchDB
• Sqoop

7. Can you describe what an N-gram is?

An n-gram is a connected sequence of n items from a set of speech or text. An N-gram can be described as a probabilistic language model used to predict the next item in a particular sequence, such as (n-1).

8. Can you explain what a hash table collision is and point out how it can be prevented?

A hash table collision is said to occur when two separate keys hash to a common value. What this basically means is that the same slot cannot be used to store two different types of data models. Hash table collisions can be avoided in two ways:

Separate chaining – Separate chaining is a method in which a data structure is used in order to store multiple items which is then hashed to a common slot.

Open addressing – Under this method, empty slots are sought out and data is stored in the first empty slot available.

Although it is difficult to predict all the questions that your interviewer may ask but preparation always helps. All the best! 

Looking for Data Analytics Jobs in top cities? Click at the links below:

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