Top Data Analyst Interview Questions

Top Data Analyst Interview Questions | Data Analytics | Emeritus

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore, author and consultant.

The contemporary era, closely associated with high-tech science and business, has a lot to do with data analysis. Data analysis uncovered the hidden patterns from the raw data and presented actionable insights to the company. Analytics means clubbing theoretical and practical knowledge to discover the best market strategies at its core. The massive reliance of organisations on data certainly suffices the rising demand for data analysts. Scads of youngsters aspire to become data analysts. While data analysis as a disciple is not easy to get through, the interview for such positions is a whole new problem to deal with.

We have shortlisted some of the frequently asked questions during a data analyst interview to polish your skills in the article below.

  1. What are the critical responsibilities of a data analyst?

It is perhaps not possible to piece together the responsibilities of a data analyst all at once as it varies as per the requirements of the venture. But here are some of the most outstanding services a data analyst provides in every firm.

  • Pre-processing of Data, including Data Ingestion, Data cleansing, Data Transformations, and Data Loading.
  • Mining the data
  • Automation of Pre-processing steps
  • Recognising and eliminating corrupted data and rectifying any coding errors or data-related problems.
  • Maintaining internal and external reports to help the company work on the loopholes.
  • Formatting the data in a reader-friendly format
  • Establishing relations and significant patterns amid provided data to provide meaningful insights.
  1. What are outliers, and can you suggest some ways to detect outliers?

Outlier, also known as surprising data, is an observation in sharp contrast to the other observations. Outliers in a data sample suggest measurability errors, experimental errors, or data points that are different from the rest.

  • Outliers can be detected in the following ways-
  • Data visualisation using graphs or other illustration tools is one most widely used detection methods for outliers. Representing data on charts helps present a more precise and comprehensive picture of data, thus quickly detecting outliers. The most commonly usedvisualisation tools are scattered plots and box plots.
  • Inter-quartile range (IQR) technique can also be used to detect outliers.
  1. What is data mining?

DATA MINING – The branch of data mining, often termed a subset of data analysis, is concerned with exploring knowledge from the massive quantity of data to find patterns. It is primarily used to examine hidden ways in data. Methods involved in data mining are usually mathematical or statistical.

  1. What is Data Cleaning, and why it is essential?

As the name suggests, data cleansing is a process of recognising, modifying, detecting, and rectifying any inaccuracies, faults, errors, or missing portions in a data set. In a nutshell, it is an effort of refining the given data to ensure data integrity, data independency, data redundancy and other significant indicators of an ideal data model. Major data cleansing tasks include introducing dummy variables in missing portions, verifying those insights are factual, ensuring no biased conclusions, etc. When data cleaning is done correctly, the accuracy of the models increases remarkably.

  1. Which tool is better for text analytics?

Python has more libraries having a lot of functions to do text analytics. Libraries like NLTK, Spacy, Gensim, TextBlob and Stanford Core NLP are preferred.

  1. Explain the difference between univariate, bivariate, and multivariate analysis.

In Univariate analysis, only one variable is used for research. At the same time, Bivariate refers to the study of 2 variables together to understand the relationship between them, and multi refers to more than two variable analyses done together.

  1. What are the ways to handle missing data and explain various imputation techniques?

If the missing data is less, we can remove the rows from the data. However, if many values are missing in a column, we can remove the queue to do our analysis.

However, the most widely used technique is the Imputation of data done when you have many values missing and probably in different columns.

Various techniques for Imputation are given below:

  • Statistical imputation techniques: Missing values can be replaced by Mean, Median and Mode.
  • Regression-based techniques: We can create a regression model to find the missing value based on the values in other columns for that row. One of the most common is Multiple Imputation by chained equations (used for values Missing at random), in which multiple imputations are iteratively done for one deal.
  • KNN: Values can be calculated based on the average of nearby values in a multi-dimensional plane.
  1. What are the important Data Analytics Tools?
  2. SQL
  3. Tableau/ PowerBI
  4. KNIME
  5. Python/R
  1. What are Joins, and what are the different types of joins?

Join refers to the action of extracting information from more than one table and joining them logically based on key matching columns.

  • Inner Join: In this method, only rows whose primary ids are there in both the tables are extracted and joined
  • Left Outer Join: In this method, all rows from the left table and corresponding rows present in the right table are extracted.
  • Right Outer Join: In this method, all rows from the right table and corresponding rows present in the left table are extracted.
  • Full Outer Join: All rows from both tables are extracted in this method.
  1. What is a lambda function in Python?

This is an anonymous function that the user can define independently and can be used in the program. It is pretty powerful and can be defined within another function.

  1. What are the most common libraries you have used in Python?

Most common libraries used are below:

  1. Numpy
  2. Pandas
  3. Matplotlib
  4. BeautifulSoup
  5. Pytorch

Data Analysis helps transform data into discovering valuable insights for making informed business decisions. Hope some of the questions and answers covered in this article will help you gain confidence when you walk into your data analyst interview.

~ Kapil Mahajan, Data Science Leader


Today across industries, data plays an essential role in every business decision. India reported 137,870 data science jobs in June 2021 and witnessed a 47.1% increase in open jobs requirements compared to June 2020 (Analytics India Magazine, 2021). This is the best time to upskill with a popular data science course from Emeritus offered by leading B-Schools in India and take your career to new heights.

About the Author

Senior Content Contributor, Emeritus Blog
Varun, a seasoned content creator with over 8 years of diverse experience, excels in crafting engaging content for various geographies and categories. Leveraging this expertise, he seamlessly translates complex concepts into enriching educational content for the EdTech domain. His keen understanding of research and life experiences helps him resonate with students and create fact-based content. He finds solace and inspiration in music, nurturing his creativity for content creation.
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