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Data Analyst Interview Questions and Answers

In today’s day and age, with such a spurt of growth in the technology sector, mastering the art of extracting insights from any kind of data to sharpen your business skills is known as data analysis.

The process of data analysis comprises of the following stages:

  • Choosing the data to be examined
  • Collection of information
  • Preparation for the analysis of data by cleaning
  • Analyzing of information
  • Interpretation of the findings

It is always a good idea to have a sense of the types of data analyst interview questions one can get asked. Let us take a look at some of the data analyst interview questions and answers.

Below we have compiled.

  • Basic interview questions for a data analyst
  • Interview questions on SQL for a data analyst
  • Advanced interview questions for a data analyst
  • and some insights on data analysis certification.


1. Brief us about the key requirements in order to become a data analyst

This type of data analyst interview question tests one’s knowledge about this particular area.

  • Powerful knowledge of the programming languages (Java, ETL), reporting packages (SQL, SQLite) and databases.
  • Evaluation, organization, collection and communication of big data.
  • Data mining techniques are also a must.

2. Give examples of the tools used for data analysis.

This is the most frequently asked data analyst interview question, and the examples can be:

  • R programming
  • Tableau
  • SAS
  • Rapid Miner and so on…

3. What would you do in a situation if you have missed some data?

For answering this type of data analyst interview question, one’s answer must be clear-cut and precise.

For finding out the missing data:

  • one needs to collect all the relevant information about the missing data and prepare a validation report to determine its credibility using approaches like the deletion method or the single imputation method.
  • The suspicious data should be examined by experienced experts to determine their acceptability.
  • Invalid data should be issued a validation code and replaced.
  • To work with missing data, the optimal use of analysis strategies such as deletion, single imputation, model-based techniques, and so on should be followed.

4. Define the characteristics of a good data model

This is a very straightforward and data analyst interview question, and the answer to this must also be pretty straightforward:

  • The data should be predictable and accurate.
  • It should be adaptive to change according to one’s needs.
  • It should be consumable for the clients to earn profitable remuneration.

5. Explain with the help of data analytics how many buses operate daily in Bangalore

Many employers use such questions to see what the thinking of an analyst is. Such a data analyst interview question includes how you identified the data, articulation and how you came at the solution part of the same.

For example, one would first calculate the number of people living in that city and categorize them on the basis of age, gender, income group and working group. Also one can take a look at the bus depot offices.

6. Define the process of analyzing a particular data

It can be defined as the process of gathering, cleaning, interpreting, and modeling data in a way to maximize the reports. Take a look at the image below for more understanding of the same.


7. What is your area of preference, and why?

You must always stick to one type of area while answering such a data analyst interview question. They have an array of choices of shapes, sizes including operations, finances, and marketing, among others.

An example of the same can be by mentioning that your area of interest lies in operation analysis. It goes well with your knowledge and that this particular area has a lot of potential for growth.

8. Explain data cleaning

Data wrangling or data cleaning is a method of securely locating faulty data and deleting it to make sure the quality of data is not compromised. Cleaning of data can be done in the following ways:

  • Quality data plans are created to discover where the most data quality mistakes have occurred, examine the main cause, and to develop the plan accordingly.
  • Before the creation of a database, a defined approach for checking critical data is defined.
  • To save time during analysis of the data, finding duplicates and confirming the data’s accuracy.
  • It is critical to keep track of all the data cleaning procedures so that one can repeat or eliminate them as and when needed.

9. How will you validate a particular data given?

Data validation can be defined as the establishment of the source, quality and accuracy of the data. Data validation can be done in two ways:

Data screening – maintaining the accuracy of the data and checking for redundancies.
Data verification – checking the redundancy in data by reviewing processes until the presence of data is assured.

10. Differentiate between data analysis and data mining

Cleaning, organising, and using data to develop insights is what data analysis entails, whereas data mining is looking into hidden patterns in data.

The outcomes associated with data analysis are far more in number and reach as compared to those in data mining.


11. Can you brief us about some of the challenges you faced during data analysis?

  • Poor quality data with missing values.
  • Unrealistic schedules of stakeholders.
  • Integration of data from various sources.
  • Choosing the wrong tools to achieve correct data analysis.

12. Explain one of your recent projects

This type of data analyst interview question can only be answered by professionals who have worked in this area.

  • Begin your answer by defining the project
  • Explain how cleaning, exploration and preparation for the data you chose was done
  • Explain how you would approach the project
  • Elaborate the final value and impact of the project

13. Please define the KNN imputation method.

The K nearest neighbour or the “nearest neighbour imputation” method is the identification and replacement of missing values from each column. The distance functions are used to determine the similarity of the two qualities.

14. Can you explain VLOOKUP?

VLOOKUP is an abbreviated form of Vertical Lookup. It is a function that instructs Excel to look for a specific value in a column in order to return a value from another column in the same row. It consists of four parts: 

  • The value you are looking for
  • The range in which the value lies
  • The number of the column that carries the return value within the selected range

VLOOKUP = ([value], [range], [column number], [false or true])

15. Define a pivot table and its sections

For answering these types of data analyst interview questions, one must include pictures from their projects to give a clear idea of their knowledge to the recruiter.

It is a table of grouped values that forms the individual components of a more extensive database into one or more categories. This could include sums, averages, or other data that the pivot table aggregates using the grouped values and a selected aggregation algorithm.

16. Can you define a waterfall chart and its uses?

A waterfall chart depicts both negative and positive values that lead to the final outcome value. For example, if you want to look at the net income of any company, you should include all the figures given in the chart from which you can clearly understand how the value from revenue to net income is obtained after all the deductions.

17. Explain time series analysis

There are two types of time series analysis, namely:

  • Frequency domain
  • Time-domain

The output of any given process can be forecasted using time series analysis by examination of past data by using approaches like smoothing, log-linear regression and many more.

18. How can you define machine learning?

It is a type of Artificial Intelligence (AI) that teaches machines to learn from past data and develop the ability to protect future outcomes. Machine learning is used in a variety of industries like healthcare, financial services, automotive and so on.

This is one of the advanced interview questions for a data analyst, which professionals can answer well with an example.

19. Define a data warehouse

It is a depository holding vast amounts of data from many sources throughout the company. It is designed to conduct analysis, business intelligence, and data mining tasks by providing a consolidated view of all the data that has been collected.

Some of the qualities of a data warehouse are as follows:

  • Structured and standard format: The Extraction-Transformation-Load (ETL) process ensures that the data is loaded into the system in a structured and standard format.
  • Time-variant: when data is placed into a data warehouse, it is given a timestamp, making it easier to compare data from different times.

20. What do you mean by an outlier?

While answering this data analyst interview question, you could say; An outlier can be defined as a phrase used by data analysts to describe a result in a sample that appears to be far from and divergent from a set pattern. Univariate and multivariate outliers are the two types of outliers.

The two methods used for detecting outliers are as follows:

1. Box plot method:

The value is considered an outlier if it is greater or lower than 1.5*IQR and sits above the upper quartile or below the lower quartile.

2. Standard deviation method:

It specifies that an outlier is a result that is greater or lower than the mean (3*standard deviation)

21. How would you prepare data for analysis?

This question comes under basic interview questions for a data analyst and can be considered an important part of data analytics. The employer is curious about how one will plan to clean and transform the raw data before its processing and analysis. One should discuss the model that will be utilised and the logical reasoning behind it while responding to such data analyst interview questions.

22. Give some examples of the questions you should ask the client before creating a dashboard.

For answering such types of data analyst interview questions, one should ask the client before developing the dashboard. The purpose of the dashboard is as follows:

  • The primary purpose behind creating a dashboard
  • Should you have a real-time dashboard or a retrospective one?
  • Should the dashboard be detailed?
  • Are the users tech and data-savvy?
  • Should you segment the data?
  • Should you describe the design of the dashboard?


SQL or Structured Query Language is one of the most in-demand talents among recruiters in the tech industry, particularly in areas like analytics and data sciences. For analysing huge data sets, mostly in relational databases, the language used to connect databases is required. Here are some of the interview questions on SQL for a data analyst

23. Define SQL and relational database

This would be the most unavoidable data analyst interview question, and so recruiters are looking for the most convincing explanation from the candidate.

The relational database is a collection of related and connected data tables that are used to store various types of data that may be used to retrieve specific data analyst interview questions. Whereas SQL is a language that helps in interacting with a relational database.

24. How would you use INSERT, UPDATE and DELETE in SQL databases?

As the name implies, these options let you add new rows, edit existing data, and permanently delete entries from a database.

25. Elaborate a query locating and eliminating duplicates in a table without creating a new table

Cleaning up, de-duplication and other aspects of running a SQL query might be difficult. Recruiters look for people that can write a query as simple as detecting and eliminating duplicates from a given table. Duplicates are usually ignored in a table with a primary key, but if the key seems to be disabled for some reason, it may become necessary to remove duplicates.

26. Explain normalisation and denormalization in SQL

Answer this type of data analyst interview question by defining how well you can utilise them in SQL. This shows that as a candidate, you know how to use and apply them effectively. Normalisation can be defined as grouping data into relevant tables, establishing relationships between them, and eliminating redundancy. In comparison, de-normalisation is a process that involves adding of redundant data to eliminate difficult joining and improvement of database speed.

27. Tell us about the five basic commands in SQL.

This data analyst interview question is a very common and easy one, is asked by almost everyone and can be answered in the following way:

  • DDL: (Data Definition Language) generating, changing or deleting data. This command alters the structure of a given table.
  • DML: (Data Manipulation Language) modifications made to relational databases that are not saved permanently and can be undone or altered.
  • DCL: (Data Control Language) access to the database can be granted or revoked using this.
  • TCL: (Transaction Control Language) instructions used to control given transactions.

28. Define SQL view

It is a virtual table whose contents are derived from a base table(s). the given data is retrieved from a SQL query included in the view. Hence, a view object can be defined as a window into the base table. This kind of view does not include any actual data but data that is stored electronically in the base of the database. The view only shows the data provided in the base table.

29. Why would you want to use a foreign key?

This is one of the specific interview questions on SQL for a data analyst. Noticing the fact that the candidate is quite familiar with SQL constraints and Keys mean in general, instead, you would be asked to demonstrate the ability to explain a specific form of SQL constraint being the foreign key constraint. It is basically a collection of rules (restrictions) that ensures that the data in both the child and the parent tables should be consistent, which means that the foreign key constraint will keep the database’s referential integrity.

30. How would you import and export MySQL databases?

A phpMyAdmin or mySQLdump table or database command-line application can be used to export a MySQL database to a file. The MySQLdump table or database tool can also be used to export the MySQL database from the command line.

After the creation of a new database in cPanel, the contents of the database can be imported using the phpMyAdmin or the mySQL command-line software.

31. Tell us about the various types of tables present in MySQL.

MySQL supports two types of tables:

  • Transaction safe tables: BDB and InnoDB
  • Non-transaction safe tables: ISAM, HEAP, MERGE.

Whereas the default table type for MySQL is MyISAM.

One can specify the table type MySQL should use when creating a new table. The table and column definitions are always stored in a .frm file created by MySQL itself.

The index and data will be placed in different files depending on the type of table.

32. What do you mean by a trigger in MySQL? How can you use it?

A trigger is basically a database item that is associated with a table by name. It is activated when a particular event occurs. Triggers can also be used to check values before they are given a slot in a table or execute computations on values tied to an update. When a statement is updated, deleted, or entries inserted in the linked table, a trigger is supposed to set fire. These operations are then known to be trigger events.

33. Can you differentiate between MySQL and Oracle?

MySQL can be defined as an open-source relational database management system, whereas Oracle can be defined as a multi-model database that has one integrated back-end (RDBMS). The database of Oracle supports a variety of data models, including document, relational, graph, and key-value. Whereas the essential components of MySQL are tables, triggers, constraints, stored procedures, roles, and views.

34. Differentiate between CHAR and VARCHAR in MySQL

CHAR can be defined as a fixed-length character, whereas VARCHAR is a variable-length character. The CHAR and VARCHAR have different storage, maximum length and retrieval methods. The length of a CHAR column, unlike VARCHAR, is fixed to the dimension declared when the table is created.

35. Define ACLs in MySQL

ACL can be defined as Access Control List, which is a list of permissions linked with a system resource. For all the connections and comparable actions, MySQL employs ACL based security.

36. Explain the different types of joins

This one is also a very common data analyst interview question. The join clause is used to combine or join rows from two or more tables together based on a standard column. There are several sorts of joins that can be used to extract data, depending on the relationship of the tables.

There are four different kinds of joins:

  • INNER JOIN: it returns records that contain values in both tables that are the same.
  • LEFT JOIN: it takes back all the rows from the left-hand-side table, although there are no matches found in the right-hand-side, as well as rows that are common between the tables.
  • RIGHT JOIN: it returns all rows from the right-hand-side table, even if there are no matches in the left-hand-side table, as well as rows that are common between the tables.
  • FULL JOIN: it returns all rows from the left-hand table as well as all rows from the right-hand table.

37. Define a cursor and its uses

A cursor is a control that allows the user to navigate between rows or documents in a table. It can be thought of as a pointer to a specific row inside the set of rows. Cursors are in handy for doing database operations like extraction, insertion and deletion.

The uses of the cursor are as follows:

  • DECLARATION of a cursor after any variable.
  • The cursor declaration must be aligned with the SELECT statement.
  • Before collecting data from the result table, OPEN statements must be called to initialise the result set.
  • Use the FETCH statement to grab and switch to the next row in the result set.
  • Use the CLOSE expression to deactivate the cursor.
  • Lastly, make use of the DEALLOCATE clause to uninstall the cursor and eliminate the resources along with it.

38. Define white box testing and black box testing database

The white box testing method is basically a database’s internal structure, where users conceal specified details. Here are the steps for the same:

  • Elimination of internal faults as code errors can be found in the testing of the white box.
  • Selection of the default table values to check the consistency of the database.
  • It verifies the referential integrity rule.
  • Module testing of database functions, triggers and SQL queries is made easier.

The black box testing method involves an interface approach followed by database integration. It involves the following:

  • Details of napping.
  • Verification of the received data.
  • Verification of the data sent out by other queries.

39. Differentiate in BETWEEN and IN operators in SQL

The BETWEEN operator is used to express rows based on a set of values like numbers, text, and dates. It delivers the total number of values between two specified ranges.
The IN operator is used to find values within a specified range of values. It is used when there are multiple values to choose from.

40. What do you mean by a stored procedure?

It is a SQL code that is prepared and can be saved and reused. It can also be defined as a function that consists of a number of SQL statements that are used to access the database system. We can combine many SQL statements in a stored procedure and run it whenever and wherever it is needed.

It can also be used to provide modular programming, which is writing it once, saving it and then calling it as many times as needed. This particular function allows for speedy execution as compared to other numerous queries.


The demand for data scientists and analysts has risen in recent years as interest in technologies such as Big Data, and Artificial Intelligence (AI) has skyrocketed.

Organisations can use professionals with data analytics certification to study information relevant to them and determine what works and what does not. Companies are better positioned to set future goals and devise plans to attain them by compiling, organising and analysing critical data available with them.

Individuals with the perfect combination of data analysis expertise and talents are still uncommon, which is why they may command premium pay. Data analytics certification is a wonderful option if you want to pursue a Data Analytics job and gain an advantage over your colleagues. One may get the knowledge and abilities you need to succeed in this booming area by enrolling in a data analytics certification course.

Here are some of the top Data Analytics Certification courses which will give you knowledge and skills for the same.

  • Certification of Professional Achievement in Data Sciences:

This unique programme is a non-degree programme designed to help people gain proficiency in data science fundamentals. Algorithms for Data Sciences and Exploratory Data Analysis and Visualisation are the four courses that make up the curriculum.

COLUMBIA UNIVERSITY is the organisation that conducts this course.

  • IBM Data Science Professional Certificate:

It is a beginner-level certificate that shows a person’s knowledge of data science topics such as open-source tools and libraries, methods, Python, databases, SQL, data visualisation, and machine learning. It requires the candidate to complete 9 courses (12 hours per week for 3 months), during which they will complete multiple hands-on assignments and construct a portfolio of data science projects. Candidates receive a Coursera Professional Certificate and an IBM digital badge recognising Data Science expertise at the completion of the programme.

COURSERA is the organisation that conducts this course.

  • Open Certified Data Scientist:

The open CDS is an independent global certification that recognises data scientists’ abilities, knowledge, and expertise. Through written applications and peer interviews, candidates for Open CDS must demonstrate their abilities and experience against a set of conformance requirements. There are no training courses included and no written tests as well.

For obtaining the certificate, three steps are required:

  • 4-5 milestone badges must be achieved by the candidate.
  • Completion of the experience application form.
  • Attending a board review.

This data analytics certification is valid for three years, after which the time bearer can recertify.

THE OPEN GROUP is the organisation that conducts this course.

  • Microsoft Certified Solutions Expert (MCSE): data management and analytics:

This data analytics certification course demonstrates subject matter knowledge in assisting businesses to use their data assets using Microsoft Power BI. The holders of this certificate can develop and build scalable data models, clean and transform data and enable sophisticated analysis.

MICROSOFT is the organisation that conducts this course.

  • Cloudera Certified Associate (CCA) Data Analyst:

This course requires a SQL developer to demonstrate essential abilities of an analyst like loading, transforming, and modelling Hadoop data to create relationships and extract meaningful findings from a raw output. The passing of the CCA Data Analyst Exam (CCA 159), consisting of 8-12 performance-based, hands-on tasks on a CDH 5 cluster that are proctored remotely, is required. Candidates have a total of 120 minutes for each job to implement a technological solution. Assessment of the situation should be done to come up with the best possible solution in the time period allotted.

CLOUDERA is the organisation that conducts this course.

  • Certified Analytics Professional (CAP):

This credential is a general data analytics certification that verifies end-to-end knowledge of the analytics process, from problem framing to data acquisition, methodology, model creation, deployment and model lifecycle management. It necessitates passing the CAP exam and abiding by the CAP Code of Ethics.

INFORMS is the organisation that conducts this course.

  • EMC Proven Professional Data Scientist Associate (EMCDSA):

This data analytics certification course validates a person’s ability to engage and contribute to big data projects as a data science team member. It includes implementing a data analytics lifecycle, rephrasing a business problem as an analytics one, choosing the right data visualisations and more.


Data analysts have a wide range of opportunities, and they can advance as far as their abilities allow. According to the World Economic Forum’s analysis, the demand for data analysts will increase in the coming years, and this will be one of the most sought-after profiles over the years. All of this is for a very basic purpose.

For both evaluation and evolution of data science programme components, data analytics certification approaches applied to specific programmes can be used. As new pathways arise at institutions, it is critical that this process be continued lifelong.

When we look at our usage of technology, we can see the amount of data we generate is growing year after year. This huge amount of big data should be organised and examined, and that is why data analysis is in such high demand.

Improved coordination could open up new chances for collaboration and cross-pollination as professional groups adapt to data science. It would be beneficial to have a group or conference with bridging skills. Professional societies could find it advantageous to combine in order to provide such training and networking opportunities to their shared communities.


More Resources: Job vacancies in Kochi | Job vacancies in Kuwait | Short resignation letter | Technical interview questions and answers

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