Top 60 Data Analyst Interview Questions and Answers in 2021

Data analysis is defined as a process of inspecting, transforming, cleansing, and modeling the data with a goal to discover useful information and support decision-making.

Who is a Data Analyst? The data analyst acts as a gatekeeper for the organization’s data or information so that the stakeholders can understand the data and can use it to make strategic business decisions. It is a kind of technical role that requires an undergraduate degree or master’s degree in analytics, computer modeling, math, or science.

Data analysis tools are mainly used to extract useful information from business data and help the data analysts to make the data analysis process easy.

We have noted the most commonly asked Data analyst Questions and Answers in our blog. Make sure you go through our Top 60 Data Analyst Interview Questions and Answers.

Table of Contents

TOP Data Analyst Interview Questions and Answers

1. Can you tell us a few problems that data analysts usually encounter while performing the analysis?

A few of the challenges that the data analysts face are listed below:

  1. The amount of data being collected: The organization or company receives information on each and every incident and interaction on a daily basis, leaving the analysts with thousands of interlocking data sets.
  2. Collecting meaningful and real-time data: With a lot of data available, it’s difficult for the analysts to dig down the entire data and access the insights that are needed most.
  3. Visual representation of data: Strong data systems needs to enable report building at the click of the button.  The Employees and decision-makers will have access to the real-time information that they need in an appealing and educational format.
  4. Data from multiple sources: Next, a problem we have is trying to analyze the data across various, multiple, disjointed sources. Here, different pieces of data are often housed in different systems.
  5. Inaccessible data: Moving the data into one centralized system has an impact if it is not easily available to the users that need it.
  6. Poor quality data: Without good input, the output will be unreliable. So the that they collect has to be accurate.
  7. Lack of support: Data analytics cannot be effective without organizational support, both from the top and lower-level employees.

2. Can you explain the Aggregation and Disaggregation of data?

Aggregate data refers to the numerical or non-numerical information that is collected from multiple sources or on multiple measures, variables, or individuals and compiled into the data summaries or the summary reports, mainly for the purposes of public reporting or statistical analysis.

Disaggregated data is the data that has been broken down by the detailed sub-categories, for example, by marginalized group, region, gender, or level of education. Disaggregated data can reveal the deprivations and inequalities that may not be fully reflected in the aggregated data.

3. Can you explain the steps to take to handle slow Excel workbooks?

The steps taken to handle slow Excel workbooks are given below:

  1. Use Excel Tables and Named Ranges.
  2. Use Manual Calculation Mode whenever possible.
  3. Avoid Volatile Functions.
  4. Avoid Array Formulas.
  5. Avoid Using Entire Row or Column in References.
  6. Convert Unused Formulas to Values.
  7. Make use of  Faster Formula Techniques.
  8. Make sure you keep All Referenced Data in One Sheet.
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4. Can you explain How PROC SQL works? 

PROC SQL is defined as a powerful Base SAS7 Procedure that combines both the functionality of PROC and DATA steps into a single step. PROC SQL is used to sort, summarize, subset, join, and concatenate the datasets, create new variables, and it also prints the results or creates a new table or view all in one step.

5. Can you tell me how to create stories in Tableau?

We use stories to make our case more compelling by showing how facts are connected and how decisions are related to the outcomes. We can then publish The story on the web, or we can present it to the audience.

Here, each story point can relate to a different view or dashboard, or the entire story can relate to the same visualization that is seen at different stages, with different annotations and filters.

To create a story, follow the steps listed below:

  1. Click on the  New Story tab. Then, Tableau opens a new story for you as your starting point.
  2. In the lower-left corner of the screen, you have to choose a size for your story. Then, choose from one of the predefined sizes or from the set of a custom size in pixels.
  3. Your story gets its title name from the sheet name default. To edit the name, you have to right-click on the sheet tab and then choose Rename Sheet.
  4. To start building your story,  you have to double-click a sheet on the left side to add it to your story point.
  5. Now,  you need to click on the add a caption to summarize the story point.
  6. To further highlight the primary idea of this story point, you can change the filter or sort on the field in the view. Then you can save your changes by clicking on update on the story toolbar that is present above the navigator box.

Check out our Tableau Interview questions and answers for more insight on Tableau.

Data Analyst Interview Questions and Answers

6. Name the different types of Hypothesis Testing?

A few of the types of hypothesis testing are listed below:

  1. Normality Test: This test is used for normal distribution in a population sample.
  2. Chi-Square Test for Independence: It is used for the association of significance between the two categorical variables in a given population sample.
  3. T-test: It is used in a normally distributed population where the standard deviation is unknown, and the sample size is comparatively smaller.
  4. Welch’s T-test: It is used to test for equality of means between the two population samples. It is also called Welch’s unequal variances t-test.

7. What do you mean by Print Area, and how can you set it in Excel?

The  Print Area in Excel is the range of cells that you want to print whenever you print that particular worksheet.

To set the print area in excel, follow the given below steps:

  1. You have to select the cells for which you want to set the Print Area.
  2. Then, you need to click on the Page Layout Tab.
  3. Next, click on the Print Area.
  4. Click on Set Print Area.

8. Can you tell us what the criteria to say whether a developed data model is good or not are?

Below mentioned are the criteria to say about the model, whether it is good or bad( Note: The answer to this question may vary from person to person).

  1. The data in a good model should be easily consumed.
  2. The data has to be scalable for a good data model.
  3. A good data model has to provide predictable performance.
  4. It should easily get adapted to the changes in the requirements.

9. Can you tell us how to select all blank cells in Excel?

You need to follow the given steps to select blank cells in Excel:

  1. First, you have to select the entire dataset and then press F5. That will open a Go To Dialog Box.
  2. Now, click on the Special button, which will open the  Go-To special Dialog box.
  3. There, you need to select the Blanks and click on OK.

10. What are the different types of joins? 

The different types of joins are listed below:

  1. Inner Join: This join returns the records that have matching values in both tables.
  2. LEFT outer JOIN: This join returns all the records from the left table, and the matched records from the right table
  3. RIGHT Outer JOIN: This join returns all the records from the right table, and the matched records from the left table
  4. FULL Outer JOIN: This join returns all the records when there is a match in either the left or right table.
Data Analyst Interview Questions - SQL Joins

Data Analyst Interview Questions and Answers

11. Can you explain the  ANYDIGIT function in SAS?

The ANYDIGIT function in SAS is used to search a string for the first occurrence of any character, which is a digit. If any such character is found, ANYDIGIT will return the position in the string of that specified character. If no such kind of character is found, then ANYDIGIT will return a value of 0.

12. Explain Pivot Table, and what are the different sections of a Pivot Table?

A Pivot Table is a feature of Microsoft Excel that allows us to quickly summarize vast datasets. It is easy to use as it requires dragging and dropping rows or column headers to create reports.

We have four sections in a pivot table, namely,

  1. Row area
  2. Column area
  3. Filter area
  4. Values area

13. Explain the term Normalization and different types of Normalization?

Normalization can be defined as a database design technique that aims to reduce data redundancy and eliminates any undesirable characteristics like Insertion, Update, and Deletion Anomalies. Normalization rules divide a  larger table into smaller tables and link them using the relationships.

Types of Normalization are listed below:

  1. 1NF (First Normal Form): Here, each table cell should contain a single value, and each record has to be unique.
  2. 2NF (Second Normal Form): It should be in 1NF, and it should have a single-column primary key.
  3. 3NF (Third Normal Form):  It has no transitive functional dependencies.
  4. BCNF (Boyce-Codd Normal Form): A table is in BCNF if it is 3NF, and for each and every X ->Y, relation X should always be the super key of the table.
  5. 4NF (Fourth Normal Form): If no database table instance consists of two or more independent and multivalued data specifying the relevant entity, then we say it is in the 4th Normal Form.
  6. 5NF (Fifth Normal Form): A table is in the 5th Normal Form only if it is in 4NF, and it can’t be divided into any number of smaller tables without loss of data.
  7. 6NF (Sixth Normal Form): It is still under discussion by database experts.
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14. What is the Alternative Hypothesis? Explain?

An alternative hypothesis is stated as a difference between two or more variables that are predicted by the researchers; that is, the noticed pattern of the data is not due to the chance occurrence.

15. What is the Null Hypothesis?

A null hypothesis is defined as a type of conjecture that is used in statistics that proposes that there is no difference between specific characteristics of a population or a data-generating process.

Data Analyst Interview Questions and Answers

16. What is a hash table collision?

A situation where the resultant hashes for two or more data elements in the data set U maps to a similar location in the hash table is known as a hash collision. This means that it will not allow two different data to be stored in the same slot.

17. Can you explain the key differences between Data Analysis and Data Mining?

Data AnalysisData Mining
It gives the insights or tests hypothesis or model from a dataset.It identifies and discovers a hidden pattern in huge datasets.
It is  done on both structured, semi-structured, or unstructured dataIts studies are mostly based on structured data.
The main is to improve hypotheses or make business decisions.Data Mining aims to make the data more usable.
Data Analysis makes use of business intelligence and analytics models.Data mining is mainly based on Mathematical and scientific methods to identify patterns or trends.

18. Can you explain Data Cleaning in brief?

Data cleaning is defined as the process of preparing the data for analysis by removing or modifying the data that is incorrect, duplicated, incomplete, irrelevant, or improperly formatted.

 A few ways to do data cleaning are listed below:

  1. Remove duplicate or irrelevant observations
  2. Fix structural errors
  3. Filter the unwanted outliers
  4. Handle missing data
  5. Validate and QA

19. What is Data Profiling?

Data profiling is the mechanism of examining the data that is available from an existing information source like a database or the file and collecting the statistics or informative summaries about that specific data. 

20. What is Data Validation?

Data validation is a process for checking the accuracy and quality of our data, mainly performed prior to the importing and processing. It is also considered a  form of data cleansing. It ensures us that when you perform an analysis, your results will be accurate.

Data Analyst Interview Questions and Answers

21. Can you name some of the top tools that are used to perform Data Analysis?

Some of the top tools that are used to perform data analysis are listed below:

  1. Python
  2. R
  3. SAS
  4. Excel
  5. Power BI
  6. Tableau
  7. Apache Spark

22. Can you name the steps involved when working with a Data Analysis project?

A few of the important steps are listed below:

  1. Problem statement
  2. Data cleaning/preprocessing
  3. Data exploration
  4. Modeling
  5. Data validation
  6. Implementation
  7. Verification

23. Can you name some of the popular tools used in Big Data?

There are many tools available for Big data. We have listed a few:

  1. HPCC
  2. Qubole
  3. Statwing
  4. Pentaho
  5. Hadoop

24. What is Time Series Analysis, and where do we use it?

Time series analysis is defined as a statistical technique that mainly deals with time-series data or trend analysis. In simple terms, the Time series data is the data that is in a series of specific time periods or intervals.

Time series analysis is used in:

  1.  Economic
  2.  Forecasting
  3. Sales Forecasting.
  4. Budgetary Analysis.

25. Can you name some of the properties of clustering algorithms?

The properties of cluster algorithms are as follows:

  1. Iterative
  2. Disjunctive
  3. Hard and soft
  4. Flat or hierarchical

Data Analyst Interview Questions and Answers

26. Can you tell us what outliers are and how they are detected?

An outlier in data analysis is defined as an observation that lies an irregular distance from other different values in a random sample from a given population.

Examination of the data for the unusual observations that are removed from the mass of data, these points are often known as outliers.

A few of the methodologies that are used to detect outliers are as follows:

  1. Standard deviation method
  2. Box plot method

27. Can you explain the disadvantages of Data Analytics?

The disadvantages of Data Analytics are given below:

  1. The information that is obtained using data analytics can be misused.
  2. One of the toughest jobs in data analytics is to select the correct analytics tool.
  3. The price of the tools usually depends on the features and applications that they support. Few tools are complex and require proper training.

28. Explain Collaborative filtering?

Collaborative filtering has the ability to generate more personalized recommendations by analyzing the information from the past activity of a particular user or the history of other users that is of similar taste to a given user.

29. Can you name a few statistical methodologies used by Data Analysts?

A few of the statistical methodologies used by data analysts are given below:

  1. Cluster analysis
  2. Imputation techniques
  3. Rank statistic
  4. Bayesian methodologies
  5. Markov process

30. Explain the K-means algorithm?

K-Means Clustering is defined as an Unsupervised Learning algorithm that groups the unlabeled dataset into various clusters. It can be defined as an iterative algorithm that divides the unlabeled dataset into k different clusters in a way that each dataset belongs to only one group that has the same properties.

Data Analyst Interview Questions and Answers

31. Explain the KNN Imputation method?

With the help of the KNN method, a categorical missing value can be imputed (assigned)  with the majority among its k nearest neighbors. The average value of the k nearest neighbors is considered as the prediction for a numerical missing value, known as the majority of mean rule. 

32. What is an N-gram? 

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

33. Can you name some of the data validation methodologies used in Data Analysis?

Some of the data validation methodologies used on data analysis are given below:

  1. Form-level validation
  2. Field-level validation
  3. Search criteria validation
  4. Data saving validation

34. Explain Normal Distribution? 

The normal distribution, also called the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that the data near the mean are more frequent in occurrence than data that is far from the mean. Normal distribution, when represented in a graph, appears as a bell curve.

35. Can you explain the advantages of version control?

Version control allows us to identify differences, compare files, and merge the changes prior to committing any code.

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Few advantages of version control are listed below:

  1.  It helps us to keep track of application builds by we will be able to identify which version is in use in development, QA, and production.
  2. It helps us to maintain a complete history of project files so that it will be helpful when there’s a central server breakdown.
  3. It allows us to see the changes made in the content of various files.
  4. It is excellent when it comes to storing and maintaining multiple versions and variants of code files safely.

36. Can you differentiate between Variance and Covariance?

Variance Covariance
It is defined as the spread of a data set around its mean value.It is the measure of the directional relationship between the two random variables.
It is used to measure an asset’s volatility.It specifies two different investments’ returns over the period of time when it is compared to different variables.

37. Can you tell us how to tackle multi-source problems?

  1. You need to know what data to combine
  2. Make use of data visualization
  3. Turn to data blending tools.
  4. Create virtual database services via abstraction

38. Can you tell the difference between Data profiling and Data mining?

Data profilingData mining
It is done at different stages of data warehouse development stages.It is a  process of identifying the patterns in the pre-built database.
The main purpose of data profiling is to identify the corrupt data at the initial stage of the data so that we can correct it at the right time.It is the mechanism of evaluating the existing database and turning the raw data into useful information.

39. Can you tell us few important responsibilities of a data analyst?

  1. They should be Collecting and interpreting the data.
  2. One has to analyze the results.
  3. They have to report the results back to the relevant members of the business.
  4. They have to identify patterns and trends in data sets.
  5. They have to define new data collection and analysis processes.

40. Can you explain the Affinity Diagram?

The affinity diagram organizes a huge number of ideas into their natural relationships. It is an organized output from a brainstorming session. We mainly use it to generate, consolidate, and organize information that is related to a product, complex issue, or problem.

Data Analyst Interview Questions and Answers

41. Can you tell us about Data visualization?

Data visualization is defined as the process of putting the data in the form of a  chart, graph, or other visual formats which help information analysis and interpretation. Data visuals help to present the analyzed data in such a way that they are accessible to and engage various stakeholders.

42. What is a Data collection plan?

A data collection plan ensures us that the data that is collected during the analysis or improvement project is useful and is appropriately collected.

43. Can you explain Hadoop Ecosystem?

Hadoop Ecosystem is a platform that provides various services to solve big data problems. It includes the Apache projects and different commercial tools and solutions.

We have four major elements of Hadoop, namely,

  1. HDFS 
  2. MapReduce 
  3. YARN 
  4. Hadoop Common

44. Explain the term Imputation?

 Imputation is the technique of replacing the missing data with substituted values. While analyzing the data, the missing data may cause a problem.

The common imputation methods are:

  1. Single Imputation
  2. Mean Imputation
  3. Cold deck imputation
  4. Regression imputation
  5. Stochastic regression imputation
  6. Substitution
  7. Cold deck imputation

45. Can you tell us the basic syntax style of writing code in SAS?

  1. Make Use of proper space to separate components in a SAS program statement.
  2. Make sure to end all statements with a semi-colon.
  3. Write a DATA statement to name the dataset.
  4. Write an INPUT statement to name the variables in the given data set.
  5. End the SAS program with a RUN statement.

46. What is interleaving in SAS?

Interleaving in SAS is defined as combining the individual sorted SAS data sets into one large sorted data set. Data sets can be interleaved by using the  SET statement and the BY statement.

47. Explain the term Clustering?

Clustering

Clustering is the mechanism of dividing the population or data points into a number of sets such that the data points in the same groups are kind of similar to that of other data points in the same group.

48.  What is the condition for using the T-test or the Z-test?

The T-test is used when we have a sample size of less than 30, and a Z-test is used when we have a sample test greater than 30.

49. What is the Truth Table?

Truth Table is a collection of facts that determines the truth or falsity of a proposition.

We have three types, namely,

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

50. What is Standard Deviation?

Standard deviation is used to measure any degree of variation in a data set. It measures the average spread of data around the mean accurately.

Data Analyst Interview Questions and Answers

51. What are collisions in hash tables?

A collision is said to occur when a hash function maps to two different keys to the same table address. It is a simple re-hashing scheme where the next slot in the table is checked on a collision.

52. Why is ‘naïve Bayes’ naïve?

It is naïve because it assumes that all the datasets are equally important and independent, which is not the case in the real-world scenario.

53. Explain the term Data Wrangling?

Data wrangling can be defined as the process of cleaning and unifying messy and complex data sets for easy access and analysis.

54. Explain the term Data blending?

Data blending is the technique of combining data from various sources into a functioning dataset.

55. Explain the term Data joining?

Data joining is carried out when the data comes from the same source.

56. Explain Descriptive analytics?

Descriptive analytics is defined as the interpretation of historical data to better understand the changes that have occurred in a business. It describes the use of a range of historical data to draw comparisons.

It gives you an idea of the distribution of the data. It helps you to detect outliers and typos and enables you to identify associations among variables, therefore making you ready to conduct further statistical analyses.

57. Explain Predictive analytics?

Predictive analytics is defined as the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes that are based on historical data.

58. Explain Prescriptive analytics?

Prescriptive analytics makes use of machine learning to help businesses decide the course of action based on the computer program’s predictions. It works with predictive analytics, which uses the data to determine near-term outcomes.

59. Name the different types of sampling techniques?

The different types of sampling techniques are listed below.

  1. Simple random sampling
  2. Systematic sampling
  3. Cluster sampling
  4. Stratified sampling
  5. Judgmental or purposive sampling

60. Explain the term Overfitting?

Overfitting refers to the model that models the training data very well. It means that the noise or the random fluctuations in the training data is picked up, and they are learned as concepts by the model. The problem here is that these concepts don’t apply to the new data, and it has a negative impact on the model’s ability to generalize.

Good luck with your data Analyst interview. We hope our Data Analysis interview questions and answers were of some help to you. You can also check out Business Analyst Interview questions and answers, which might be of some help to you.

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