Top 17 Data Analyst Interview Questions and Answers

Are you aspiring to build a career in data science and data analytics? If your answer is yes, let’s tell you that your timing couldn’t be better. In this article, we will be looking at some most important data analytics interview questions.

Organizations around the world are leveraging Big Data to enhance their overall productivity and efficiency, which inevitably means that the demand for expert data professionals such as data analysts, data engineers, and data scientists is also exponentially increasing.

However, to bag these jobs, only having the right qualifications isn’t enough. You need to accomplish the trickiest part – the interview. To help you with that, let’s look at a list of most asked data analyst interview questions.

Let’s get started with Data Analyst Interview Questions:

1. What are the key requirements for becoming a Data Analyst?

This data analyst interview question tests your knowledge about the required skill set to become a data scientist.
To become a data analyst, you need to:

• Be well-versed with programming languages (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, Db2, etc.), and also have extensive knowledge on reporting packages (Business Objects).
• Be able to analyze, organize, collect and disseminate Big Data efficiently.
• You must have substantial technical knowledge in fields like database design, data mining, and segmentation techniques.
• Have a sound knowledge of statistical packages for analyzing massive datasets such as SAS, Excel, and SPSS, to name a few.

2. What are the important responsibilities of a data analyst?

This is the most commonly asked data analyst interview question. You must have a clear idea as to what your job entails.
A data analyst is required to perform the following tasks:

• Collect and interpret data from multiple sources and analyze results.
• Filter and “clean” data gathered from multiple sources.
• Offer support to every aspect of data analysis.
• Analyze complex datasets and identify the hidden patterns in them.
• Keep databases secured.

3. What does “Data Cleansing” mean? What are the best ways to practice this?

If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions.
Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality.
The best ways to clean data are:

• Segregating data, according to their respective attributes.
• Breaking large chunks of data into small datasets and then cleaning them.
• Analyzing the statistics of each data column.
• Creating a set of utility functions or scripts for dealing with common cleaning tasks.
• Keeping track of all the data cleansing operations to facilitate easy addition or removal from the datasets, if required.

4. Name the best tools used for data analysis.

A question on the most used tool is something you’ll mostly find in any data analytics interview questions.
The most useful tools for data analysis are:

• Tableau
• Google Fusion Tables
• Google Search Operators
• KNIME
• RapidMiner
• Solver
• OpenRefine
• NodeXL
• io

5. What is the difference between data profiling and data mining?

Data Profiling focuses on analyzing individual attributes of data, thereby providing valuable information on data attributes such as data type, frequency, length, along with their discrete values and value ranges. On the contrary, data mining aims to identify unusual records, analyze data clusters, and sequence discovery, to name a few.

6. What is KNN imputation method?

KNN imputation method seeks to impute the values of the missing attributes using those attribute values that are nearest to the missing attribute values. The similarity between two attribute values is determined using the distance function.

7. What should you do with missing or suspected data?

In such a case, a data analyst needs to:

• Use data analysis strategies like deletion method, single imputation methods, and model-based methods to detect missing data.
• Prepare a validation report containing all information about the suspected or missing data.
• Scrutinize the suspicious data to assess their validity.
• Replace all the invalid data (if any) with a proper validation code.

8. Name the data validation methods used by data analysts.

Data analysts approach data validation in two ways:

• Data Screening – Screening or inspecting the data for any possible errors and removing them prior to conducting data analysis.
• Data Verification – After the completion of data migration, data verification is done to check the accuracy of data and remove any inconsistencies, if any.

9. Define “Outlier”.

An outlier is a term commonly used by data analysts when referring to a value that appears to be far removed and divergent from a set pattern in a sample. There are two kinds of outliers – Univariate and Multivariate.

10. What is “Clustering?” Name the properties of clustering algorithms.

Clustering is a method in which data is classified into clusters and groups. A clustering algorithm has the following properties:

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

11. What is K-mean Algorithm?

K-mean is a partitioning technique in which objects are categorized into K groups. In this algorithm, the clusters are spherical with the data points are aligned around that cluster, and the variance of the clusters is similar to one another.

12. Define “Collaborative Filtering”.

Collaborative filtering is an algorithm that creates a recommendation system based on the behavioral data of a user. For instance, online shopping sites usually compile a list of items under “recommended for you” based on your browsing history and previous purchases. The crucial components of this algorithm include users, objects, and their interest.

13. Name the statistical methods that are highly beneficial for data analysts?

The statistical methods that are mostly used by data analysts are:

• Bayesian method
• Markov process
• Simplex algorithm
• Imputation
• Spatial and cluster processes
• Rank statistics, percentile, outliers detection
• Mathematical optimization

14. What is an N-gram?

An n-gram is a connected sequence of n items in a given text or speech. Precisely, an N-gram is a probabilistic language model used to predict the next item in a particular sequence, as in (n-1).

15. What is a hash table collision? How can it be prevented?

When two separate keys hash to a common value, a hash table collision occurs. This means that two different data cannot be stored in the same slot.
Hash collisions can be avoided by:

• Separate chaining – In this method, a data structure is used to store multiple items hashing to a common slot.
• Open addressing – This method seeks out empty slots and stores the item in the first empty slot available.

16. Define “Time Series Analysis”.

Series analysis can usually be performed in two domains – time domain and frequency domain.
Time series analysis is the method where the output forecast of a process is done by analyzing the data collected in the past using techniques like exponential smoothening, log-linear regression method, etc.

17. How should you tackle multi-source problems?

To tackle multi-source problems, you need to:

• Identify similar data records and combine them into one record that will contain all the useful attributes, minus the redundancy.
• Facilitate schema integration through schema restructuring.

With that, we come to the end of our list of data analyst interview questions. Although these data analyst interview questions are selected from a vast pool of probable questions, these are the ones you are most likely to face if you’re an aspiring data analyst. These questions set the base for any data analyst interview, and knowing the answers to them is sure to take you a long way!
Data Analysts: Myths vs. Realities

For further reference, you can also download the PDF version of the Data Analyst Interview Questions and Answers mentioned below.

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Abhinav Rai

Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.

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