Top 50 Data Analyst Interview Questions and Answers (2023)

There is nothing new if we say that more and more companies are relying heavily on consumer data for running their day-to-day operations. Be it bringing major updates for the existing products, building the next new product,  or even scheduling the date and time of the product launch. It’s all data that companies trust now.

To operate on such data there are many roles for which companies hire exceptional talent. And one of those job roles is Data Analyst.

A data analyst gathers, cleans, and evaluates data sets to answer questions or solve problems. They work in a variety of fields such as business, finance, criminal justice, science, medical, and government.

This blog aims to get you an overview of the typically asked Data Analyst Interview questions. You can be assured that if you go over all of these questions, you'll have a solid idea of the kind of questions and topics you can encounter in your next data analyst interview.

Topics around which Data Analyst Interview Questions revolve

Before we directly jump on the questions, down below are the top 7 skills that a data analyst is checked on in an interview. So before sitting in an interview or starting your preparation do revise these topics.

1. SQL
2. Statistical programming(R or Python)
3. Machine learning
4. Probability and statistics
5. Data management
6. Data visualization
7. Presentation skills and Critical thinking

Apart from the above-mentioned skills, don’t forget to work on your communication and presentation skills. If you know an answer, communicate it to the interviewer in the best possible way, this might increase your chance of selection.

Basic Level Data Analyst Interview Questions

1. What is the Data Analysis Process?

Data analysis is a five-stage data-driven process to gather insights and generate reports for an organization to gain business profits:

1. Collecting,
2. Cleansing,
3. Interpreting,
4. Transforming, and
5. Modeling data.

2. What is the basic difference between Data Analysis and Data Mining?

Data mining is the process of identifying significant patterns from massive datasets, whereas data analysis is the process of analysing and structuring raw data to provide valuable information and choices. The finest example of a data mining application is in the E-commerce industry, where websites offer options of individuals who purchased and saw a certain product, and the best example of data analysis is the census research.

3. What should be done with Suspicious or Missing Data?

The common approaches for handling missing or suspicious data are:

1. Recover the Values from the data source.
2. Delete the empty or null values and continue your analysis with the remaining available data.
3. Try to guess a missing value, as in if a participant of a data survey replies with all "4"s, presume that the missing number is a 4. Removing the whole column for one missing value might not be a wise decision.
4. Try to fill the missing value with the average of the column or the row, whichever you think might suit the best.
5. Use KNN to determine the most common value among neighbours.

4. Define the term Outlier?

An outlier is a data point that is exceptionally high or extremely low in comparison to the adjacent data points and the rest of the nearby coexisting values in a data graph or dataset.

5. What exactly is Data Cleansing, and why is it needed?

The practice of repairing or deleting inaccurate, corrupted, improperly formatted, duplicate, or incomplete data from a dataset is known as data cleansing.

There are several ways for data to be duplicated or mislabeled when merging data from different sources. We need data cleansing as if the data is inaccurate, the results and methods are untrustworthy, even if they appear to be correct.

6. What are the 5 common steps involved in any Analytics project?

The 5 common steps are-

1. Understanding the Issue
2. Data Collection
3. Data Cleaning
4. Data Exploration and Analysis
5. Results Interpretation

First understand the business issue, determine the corporate goals, and devise a profitable solution. Then gather the necessary data and information from multiple sources based on your priorities. Once gathering is done, then clean the data to eliminate any undesired, redundant, or missing variables before analysing it. The last step is to evaluate data, use data visualisation and business intelligence tools, data mining techniques, and predictive modeling. Interpret the findings to uncover hidden patterns, forecast future trends, and acquire insights.

7. What are the few common mistakes that are common during Data Analysis?

The first common error in every data analysis project is dealing with duplicate data and gathering relevant data at the right time. The second and most typical issue is dealing with data cleansing and storage issues. The third is ensuring data security and dealing with compliance concerns.

8. Define Data Validation?

As the name implies, data validation is the process of establishing the correctness of data as well as the quality of the source. There are several steps involved in data validation, but the two most important are data screening and data verification. Data screening entails using a number of models to check that the data is valid and that there are no redundancies. And data verification is used if there is a redundancy, it is reviewed using various processes and then a call is made to confirm the data item's presence.

9. Define Data Profiling?

Data profiling is the process of examining, analysing, and synthesising data into useful summaries. The approach produces a high-level overview that may be used to identify data quality concerns, hazards, and general trends.

10. Mention few benefits of Data Profiling?

The four most notable advantages of data profiling are as follows:

1. Improved data quality and trustworthiness
2. Making Predictive Decisions
3. Crisis management that is proactive
4. Well-organized Sorting

Intermediate-Level Data Analyst Interview Questions

11. Explain what is Clustering, its forms, and examples of the most used Clustering Algorithms?

Clustering is the process of separating a population or set of data points into groups so that data points in the same group are more similar to other data points in the same group and different from data points in other groups. In short, it is a collection of items based on their similarity and dissimilarity.

Clustering is classified into two categories:

First is Hard Clustering, where each data point either totally or partially belongs to a cluster. And the other is Soft Clustering, where instead of assigning each data point to a separate cluster, a chance or likelihood of that data point being in those clusters is assigned.

There are 100 plus clustering algorithms, and amongst them, the most important one includes Connectivity models, Centroid models, Distribution models, and density models.

12. What is Collaborative Filtering?

Collaborative filtering filters information by utilising the system's interactions and data acquired from other users. It is based on the assumption that persons who agreed on particular products' evaluations are likely to agree again in the future.

Collaborative filtering may be visible on online buying sites when you see sections like "recommended for you".

13. What is A/B Testing?

A/B testing, also known as split testing or bucket testing, examines the performance of two versions of material to determine which version is more appealing to visitors/viewers. It compares a control (A) version to a variation (B) version to determine which is the most successful based on your key metrics.

14. Define an Alternative Hypothesis?

The alternate hypothesis is just an alternative to the null hypothesis. For example, if your null is "I'll win up to ₹1,000," your alternative is "I'll win ₹1,000 or more". Essentially, you're determining whether there's enough difference (with the alternate hypothesis) to reject the null hypothesis.

15. What is the difference between Variance and Covariance?

The spread of a data set about its mean value is referred to as variance, whereas covariance is a measure of the directional connection between two random variables. 

16. What exactly is Correlogram Analysis?

In geography, the most prevalent type of spatial analysis is correlogram analysis. It is made up of a set of estimated autocorrelation coefficients derived for each geographical link. When the raw data is reported as distance rather than values at individual locations, it may be utilised to create a correlogram for distance-based data.

17. What do you mean when you say "Normal Distribution"?

The normal distribution, also known as the Gaussian distribution, is a symmetric probability distribution about the mean, indicating that data near the mean occur more frequently than data distant from the mean.

18. Define ACID property in a Database?

The abbreviation ACID in the database refers to a transaction's four essential properties: atomicity, consistency, isolation, and durability.

19. Define Eigenvectors and Eigenvalues?

Eigenvalues are a subset of scalar values associated with a set of linear equations, most likely in matrix equations whereas eigenvectors are characteristic roots. It is a non-zero vector that can only be altered by its scalar factor after linear operations.

20. Define the term Data Wrangling?

The act of eliminating mistakes and merging complicated data sets to make them more accessible and easier to analyse is known as data wrangling. This procedure involves rearranging, converting, and mapping data from one "raw" form to another in order to make it more useful and valuable for a range of downstream applications, including analytics.

21. What are the benefits of using version control and do Data Analysts require it?

It is a system that keeps track of modifications to a file or collection of files. The term "system" refers to a group of software tools that enable the software team to monitor source code modifications as needed.

When dealing with any dataset, data analysts should employ version control. This guarantees that original datasets are preserved and that you may restore to a prior version even if a new action corrupts the data in some way.

22. In Data Analytics, which Data Validation procedures are used?

There are several methods for validating datasets. Data Analysts frequently utilise field level, form level, data saving, and search criteria validation methods.

23. How Can You Tell the difference between Overfitting and Underfitting?

Overfitting occurs when a statistical model describes random error or noise rather than the underlying relationship. It happens when a model has too many parameters in comparison to the quantity of data, resulting in poor predicting performance. Whereas, underfitting happens when a model is unable to capture the underlying trend in the data or when fitting a linear model to nonlinear data.

24. Define Logistic and Linear Regression?

Linear regression produces continuous results, whereas logistic regression produces discrete results. Linear regression seeks the best-fitted line, but logistic regression goes one step further by fitting the line values to the sigmoid curve.

25. What are the different types of Joins?

As is well known, join operations are classified into five types: inner, left, right, full, and cross joins.

26. What Is the Difference Between Univariate, Bivariate, and Multivariate Analysis?

Univariate statistics present information on only one variable at a time. Bivariate statistics are used to compare two variables. Multivariate statistics compare variables that are more than two.

27. Define Affinity Diagram?

An Affinity Diagram is a method for organising enormous volumes of linguistic data (ideas, views, concerns) into groups based on their natural links. Brainstorming ideas are frequently grouped using the Affinity approach.

28. What is Metadata?

Metadata is defined as "data/information about data." Metadata assists us in comprehending the structure, nature, and context of the data. It makes data searching and retrieval easier and also aids in the monitoring of data quality and dependability.

29. What Python libraries are used in Data Analysis?

Numpy and Scipy for Scientific Computing, Pandas for data manipulation and analysis, Plotting and Visualization with Matplotlib, Machine Learning and Data Mining using Scikit-learn, StatsModels for Statistical Modeling, Testing, and Analysis, and Seaborn for the Visualization of Statistical Data

30. What exactly do you mean by "Hadoop Ecosystem"?

Hadoop Ecosystem is a platform or a suite that offers a variety of services to handle big data challenges. It covers Apache projects as well as a variety of commercial tools and solutions. Hadoop consists of four essential components: HDFS, MapReduce, YARN, and Hadoop Common.

31. What are the criteria to use a t-test or a z-test?

The Z-test is advised when the population standard deviation is known and the sample size is more than 30. The T-test is recommended if the population standard deviation is known and the sample size is less than or equal to 30 and also in the case if the population standard deviation is unknown.

32. What exactly is the Truth Table?

A truth table is a collection of information used to determine if a statement is true or false. It functions as a comprehensive theorem-prover and comes in three types:

1. Photograph Truth Table
2. Accumulative Truth Table
3. Truthless Fact Table

33. What is the distinction between R-squared and R-squared Adjusted?

The most important distinction between adjusted R-squared and R-squared is that adjusted R-squared takes into account and tests several independent variables against the model, whereas R-squared does not.

When an independent variable is added to a model, the R-squared increases, even if the independent variable is minor. It never goes downhill. Adjusted R-squared, on the other hand, increases only when the independent variable is significant and impacts the dependent variable.

34. What exactly is a 'p-value'?

The p-value is the likelihood of receiving outcomes that are at least as severe as the observed results of a statistical hypothesis test, provided that the null hypothesis is valid. A lower p-value indicates that there is more evidence supporting the alternative hypothesis.

35. What is the distinction between the terms Recall and True positive rate?

The recall and True Positive Rate (TPR) are the same.

Here’s the formula for it:

Recall = (True positive)/(True positive + False negative)

36. What are the various sampling procedures used by Data Analysts

There are two primary types of sampling methods. First is Probability sampling, which includes random selection, which allows you to draw strong statistical conclusions about the whole group. And the second is Non-probability sampling, which entails non-random selection based on convenience or other criteria, making data collection easier.

37. What are the two most common strategies for Detecting Outliers?

Outlier detection techniques are classified into two types: Outlier detection using data point distance and density. Creating a model to anticipate the distribution of data points and flagging outliers that do not satisfy a user-defined threshold.

38. What exactly is Time Series Analysis and where is it used?

Time series analysis, or TSA, is a popular statistical approach for analysing trends and time series data in particular. The existence of data at specific intervals of time or specified periods is referred to as time-series data. Industries such as retail, banking, and economics regularly employ time series analysis.

Advanced Level Data Analyst Interview Questions

39. What are Hash table and Hash Table Collisions?

A Hash table is a data structure that implements the associative array abstract data type, which allows it to map keys to values. A hash table employs a hash function to generate an index, also known as a hash code, into an array of buckets or slots from which the necessary item may be extracted.

At times, two or more keys might provide the same hash value. This is referred to as a collision. A collision can be addressed in a variety of ways. Open Addressing Technique and Separate Chaining Technique

40. Explain KPI, experiment design, and the 80/20 rule?

KPI is an abbreviation for Key Performance Indicator, it is a measure that comprises any combination of spreadsheets, reports, or charts pertaining to business processes.

Design of experiments is the first step in dividing your data, sampling it, and preparing it for statistical analysis.

The 80/20 rule is a statistical guideline that claims that about 20% of causes produce 80% of the effects. In business, for example, it is commonly stated that 80% of revenues are generated by 20% of clients.

41. Define Exploratory Data Analysis (EDA) and its significance

Exploratory Data Analysis is the crucial process of doing preliminary investigations on data in order to uncover patterns, spot anomalies, test hypotheses, and validate assumptions using summary statistics and graphical representations.

EDA aids in the detection of flaws in data collection. Improves knowledge of the data set. Aids in the detection of outliers or unusual occurrences. Aids in the comprehension of data set variables and their relationships.

42. Mention some of the statistical approaches that Data Analysts employ?

Data analysis necessitates the use of several statistical approaches. Some examples are-

1. Cluster analysis
2. Markov process
3. Imputation methods
4. Bayesian approaches
5. Rank Statistics

43. Define Imputation and its types?

Imputation uses substituted values to replace missing data. The various imputation strategies are as follows:

Single imputation: It often identifies a specific record for a subject, such as a baseline or the last non-missing value, and then replicates it for the missing data points.

Multiple imputations: It employs statistical modeling of available data to provide a projected value for a specific subject and time point.

44. What is the technique to handle multi-source problems?

There are five approaches to dealing with various data sources in an application architecture: Determine what data you need to integrate, then employ data visualization, data blending tools, abstracted virtual database services, and where to host data sources.

45. Define MapReduce?

MapReduce is a framework that allows you to develop applications that process big data sets by breaking them down into subsets, processing each subset on a distinct server, and then combining the results from each. It is made up of two tasks: Map and Reduce. The map does filtering and sorting, whereas the reduction does a summary operation. As the name implies, the Reduce procedure always follows the map task.

46. What is n-gram?

In a document, N-grams are continuous sequences of words, symbols, or tokens. In technical words, they are the adjacent sequences of objects in a document. They are used when dealing with text data in NLP (Natural Language Processing) jobs.

47. Define a good Data Model?

To declare a model is excellent, the following factors must be examined.

1. The constructed model should perform predictably.
2. It should be easily responsive to changes in company requirements.
3. It should be adaptable to any changes in data.
4. For actionable outcomes, a model should be digested efficiently.

48. Differentiate between a Data lake and a Data warehouse?

A Data lake is a repository where all of your organization's data, both structured and unstructured, is stored. Consider it a large storage pool for data in its pure, uncooked form (like a lake). A data lake can accommodate the massive amounts of data that most businesses generate without the need to first arrange it. Data stored in a data lake may be used to construct data pipelines that allow data analytics tools to identify insights that guide crucial business decisions. A data warehouse, like a data lake, is a storehouse for business data. A data warehouse, unlike a data lake, houses only highly organized and unified data to meet particular business intelligence and analytics needs.

49. Explain the KNN Imputation method?

In a dataset, there are data rows when one or more of the values or columns are missing. The values may have a particular character or value, such as a question mark ("?"), or they may be totally absent. Values might be missing for a variety of causes, many of which are domain-specific and could include things like inaccurate measurements or unavailability. Regression models may be used for prediction if the input variables are numerical, and this situation is extremely typical. Although a variety of models can be utilized, tests have shown that a straightforward k-nearest neighbors (KNN) model is the most successful. "KNN imputation" is the process of predicting or filling in missing data using a KNN model.

50. Explain Hierarchical Clustering?

Data are grouped into groups in a tree structure in a hierarchical clustering approach. Every data point is first treated as a separate cluster in a hierarchical clustering process. The following steps are then repeatedly carried out by it: Choose the two clusters that are the most similar to one another, then merge them. These procedures must be repeated until all of the clusters are combined. The goal of hierarchical clustering is to create a hierarchy of nested clusters.

Conclusion

This brings us to the end of our top data analyst interview questions and answers blog. The main purpose of this blog was to make you aware of the many data analyst interview questions that might be asked in an interview and make it simpler for you to prepare for your upcoming interviews.

And if you are just starting and want to learn from industry pros from organizations like IBM, Airbnb, Flipkart, Microsoft, and Google, then feel free to explore Board Infinity's  Data Analyst course.