Top 20 Data Analyst Interview Questions You Must Know

Are you going to attend an interview for a data analyst position and wondering what questions you will be asked and what will the discussion be about? It is fine to think that you have all the knowledge to crack the interview yourself. But taking into consideration the level of competition one has to face these days during interviews, it is better to be well-prepared before appearing for the interview. Well, in order to crack the interview in one go, you should practice some mock data analyst interview questions to gain the confidence you need during the interview.

 

List of data analyst interview questions

 

Almost 2.5 quintillion bytes of data are generated every day. Now, what do you think is done with this data? Well, this data is utilized in a number of areas, including business, professions, research, education, media, development, science, etc.

In order to utilize this data to put into work, some sort of professionals such as data analysts is hired who help to get insights that help in decision-making processes. Make sure you go through this article thoroughly to gain insights into the most common data analyst interview questions to get the role of a data analyst.

The World Economic Forum has found that about 96% of companies today are planning to hire people who have knowledge of data analysis in the future. On the grounds of this, it is anticipated that the modern role of data analyst will rank among some of the top jobs in demand in the next years.

According to a career advisory platform AmbitionBox, the average salary of a data analyst in India can range somewhere from ₹ 1.9 lakhs to ₹ 11.3 lakhs per annum. The data analyst interview questions in this piece of writing will help you get your desired role with ease.

We assume that you are already aware of all of these facts since you are here on this page looking for data analyst interview questions. Thus, we have compiled this list of 20 data analyst interview questions to assist you in the interview process. Lastly, we’ll discuss some guidelines and tips for acing data analyst interviews. Let’s get started.

 

Top Data Analyst Interview Questions:

 

1. What Do You Mean by Logistic Regression?

Logistic Regression is basically a mathematical model that can be utilized to analyze datasets containing one or more independent variables that affect a certain outcome. The model forecasts a dependent data variable by examining the connection between many independent variables.

 

2. What Makes Overfitting Distinct from Underfitting?

 

Overfitting:

Overfitting happens when our machine learning model tries to include more data points than are necessary for the current dataset. It is characterized by high variance and low bias. It can be avoided by cross-validation, training with more data, removing features, early stopping the training, regularization, and ensembling.

 

Underfitting:

Underfitting happens when our machine learning model cannot detect the underlying trend in the data. It is characterized by low variance and high bias. It can be avoided by increasing the training time of the model and also the number of features.

 

3. Why is It Necessary to Analyse Algorithms?

Algorithm analysis makes it possible to choose the optimal algorithm among others in terms of time and space consumption. The time complexity of an algorithm measures the amount of time taken for an algorithm to run as a function of the length of the input. The space complexity measures how much memory or space an algorithm needs to operate as a function of the length of the input.

It is considerably more convenient to have simple measures for an algorithm’s efficacy than to execute the algorithm and assess its effectiveness each time a specific parameter in the underlying computer system alters.

 

4. How Do File Structure and Storage Structure Differ From One Another?

 

File Structure:

File Structure has the data stored in the secondary or auxiliary memory, i.e., hard disk or pen drives. The data remains intact until it is manually deleted.

 

Storage Structure:

Storage Structure has data stored in the memory of the computer system, i.e., RAM. The data gets deleted once the function that uses this data gets completely executed.

 

5. What Are the KPIS (Key Performance Indicators) for Supply Chain?

Some important Supply Chain KPIs are Cash to Cash Cycle Time, Perfect Order Rate, Fill Rate, Customer Order Cycle Time, Inventory Days of Supply, Inventory Turnover, Reasons for Return, On-Time Delivery, On-Time Shipment, Supply Chain Costs as a Percentage of Sales, etc.

 

6. How Are You Going to Use Consumer Analytics at Work?

Customer Analytics is used at different stages of the customer journey, such as acquisition to determine which channels bring in the newest customers, revenue to determine which channels bring in the most revenue, retention to determine where and why we lose customers, and engagement to determine which features customers find enticing.

 

Also Read: Data Analytics Scope

 

7. What is the Confidence Interval?

The Confidence Interval is the range within which we anticipate the results to lie if we experiment again. It is the mean of the result plus and minus the anticipated variation. The latter is dependent on the standard error of the estimate, while the center of the interval concurs with the mean of the estimate. The usual confidence interval is 95%.

 

8. What, in Your Opinion, constitutes a Weak and Strong Entity Set?

A thing or object with a separate existence might be considered an entity. All of the entities found in a database are gathered into an entity set. A Weak Entity Set is one that lacks some of the characteristics required to construct key constraints and other logical relationships.

It is dependent on a strong entity.  A double rectangle is used to illustrate a weak entity. A double diamond illustrates the relationship between one strong and one weak entity. A Strong Entity Set is one that has all the characteristics required to construct the primary key and other constraints.

It is independent of all other entities. A single rectangle is used to illustrate a strong entity. A single diamond illustrates the relationship between two strong entities.

 

9. Which Tool is More Effective for Text Analytics?

Python has several libraries with much more functions for text analytics. It is recommended to use libraries like NLTK, Spacy, Gensim, Text Blob, and Stanford Core NLP.

 

10. What Exactly is a Data Collection Plan?

A data collection plan is used to collect all the crucial data in a system. It covers the types of data that need to be obtained or collected. It also describes different data sources for data analysis. It is often used in the present state analysis phase of a process analysis or improvement project. It could also be helpful when creating new metrics and the procedures needed to evaluate those metrics when a project is completed.

 

11. Explain Clustering in Machine Learning?

The process of clustering divides data points into groups. The data points are grouped based on their similarities in such a way that each group differs from the others significantly. A few types of clustering are K-Means Clustering, Hierarchical Clustering, Density-based Clustering, Expectation-Maximization (EM) Clustering, etc.

 

12. What Connection Types Does Tableau Software Offer?

Tableau offers mainly two types of connections: Live and Extract

 

Live Connections:

These represent a data source that has direct connections to real-time data.

These can be deployed in locations where the data is real-time data, which when updated, automatically updates our visualization.

These take longer for complex visualizations.

These are used particularly in less complex visualizations with smaller data sets, filters, calculations, etc.

 

Extract Connections:

These represent the local copy of the data source that you can use to make the view.

These can be deployed in places where a subset of the data source can be used to create the view.

These are significantly quicker for visualization.

These are particularly useful in more complex visualizations including larger datasets, filter calculations, etc.

 

Must Read: Data Analytics Books

 

13. What is a Waterfall Chart, and When Would We Use One?

The waterfall chart displays both positive and negative values that influence the outcome value. You might include all the cost figures in this chart, for instance, if you are analyzing the net income of a business. You can observe visually with this type of chart how the value from revenue to net income is derived after all costs are eliminated.

 

14. How does PROC SQL Work?

PROC SQL runs concurrently for each observation. The following processes take place when a PROC SQL is executed:

▪ SAS checks every statement in the SQL procedure for syntax issues.

▪ The SQL optimizer looks through the statement’s query. So, the SQL optimizer basically chooses how to execute the SQL query in order to reduce runtime.

▪ If the FROM statement contains any tables, they are put into the data engine and made available for memory access.

▪ Codes and calculations are carried out.

▪ The Final Table is created in the memory.

▪ The Final Table is transmitted to the output table specified in the SQL statement.

 

15. Why is Data Cleansing Essential for Data Visualization?

Data cleansing is essential to detect and remove errors and inconsistencies from data to enhance data quality. This step is important and emphasized as incorrect data might result in flawed analysis. This step makes sure that the quality standards are met in order to prepare the data for visualization.

 

16. Name Some of the Tools Widely Used in Big Data.

This is one of the most straightforward data analyst interview questions. You should name some of the most popular tools here.

Big Data is handled using a variety of tools and the popular ones being:

▪ Apache Hadoop

▪ Apache Spark

▪ Apache Flink

▪ Google Cloud Platform

▪ MongoDB

▪ Sisense

▪ RapidMiner

▪ Scala

▪ Hive

▪ Flume

▪ Mahout

 

17. What are the Differences Between Data Mining and Data Profiling?

Data Mining:

  • It basically involves analyzing data to discover previously undiscovered relations.
  • Inaccurate or erroneous data values cannot be identified via data mining.
  • It is also referred to as KDD (Knowledge Discovery in Databases).
  • In this process, some fundamental data mining tasks, including classification, regression, clustering, summarization, estimation, and description are carried out.
  • The tools used for data mining are Orange, SPSS, Rattle, RapidMiner, Sisense, Weka, etc.

 

Data Profiling:

It basically involves analyzing each attribute of that data separately.

  • Erroneous information can be identified via data profiling at the beginning of the analysis process.
  • It is also referred to as Data Archaeology.
  • In this process, statistics or summaries regarding the data are gathered using discoveries and analysis approaches.
  • The tools used for data profiling are IBM Information Analyzer, Microsoft Docs, Melisa Data Profiler, etc.

 

18. What is Time Series Analysis?

Time Series Analysis is a statistical approach that deals with the ordered sequence of values of a variable at uniformly spaced time intervals. Data from time series are collected at adjacent times. Thus, there is a link between the observations. This distinctive feature separates time-series data from cross-sectional data.

 

19. Describe Univariate, Bivariate, and Multivariate Analysis.

This is one of the most often asked data analyst interview questions and the interviewer wants you to respond with a detailed answer.

 

Univariate Analysis:

  • It is the simplified and easiest form of data analysis where there is just one variable in the data being evaluated. For instance, studying the heights of basketball players.
  • It can be illustrated using central tendency, dispersion, quartiles, bar charts, histograms, pie charts, and frequency distribution tables.

 

Bivariate Analysis:

  • In this, two variables are analyzed in order to discover their causes, relations, and correlations.  For instance, examining the sale of cold drinks based on the weather.
  • It can be illustrated using correlation coefficients, linear regression, logistic regression, scatter plots, and box plots.

 

Multivariate Analysis:

  • In this, three or more variables are analyzed to determine the relationship of each variable with the other variables. For instance, students get prizes in a sports event, their classes, ages, and genders.
  • It can be illustrated using multiple regression, factor analysis, classification and regression trees, cluster analysis, principal component analysis, dual-axis charts, etc.

 

You can also check Online Data Analytics Courses

 

20. What Differentiates a Data Lake from a Data Warehouse?

 

Data Lake:

  • It can store data indefinitely for present or future usage and contains all of an organization’s data in a raw and unstructured form.
  • Data from a data lake with a significant volume of unstructured data is usually accessed by data scientists and engineers who prefer to study data in its raw form in order to acquire new and unique business insights.
  • This process involves extracting the data from its source for storage in the data lake, and only structuring it as necessary.
  • Storage expenses are relatively low with data lakes as compared to data warehouses. Additionally, data lakes need less time to manage, which lowers operational costs.

 

Data Warehouse:

  • It has structured data that has been processed and cleaned, making it ready for strategic analysis based on established business requirements.
  • Data from a data warehouse is usually accessed by managers and business-end users looking to acquire insights from business KPIs since the data has already been structured to offer answers to pre-determined questions for analysis.
  • This process involves extracting data from its source, cleaning it up, and then structuring it for use in the business-end analysis.
  • Data warehouses are more expensive as compared to data lakes. They need more time to manage which drives up operational costs.

 

Here Are Some Questions You Can Ask Your Interviewer At The End:

When your interviewer is done asking you data analyst interview questions, they will probably ask if you have any further queries or questions for them. You should definitely prepare a few questions in advance. Asking thoughtful and insightful questions conveys your interest in the position and the organization, your ability for quick thinking, and your level of preparation.

It’s a good idea to have some questions ready in advance. However, the interviewer could go over these during the interview process, so be sure to pay attention and mentally note down anything that comes up. We have listed a few data analyst interview questions below that you can ask the interviewer at the end:

 

▪ What type of culture or goals does the company have?

▪ What tools is the team presently using for data analysis?

▪ What types of projects will I be likely to perform?

▪ Is there any scope for mentorship or personal growth?

▪ What am I expected to do during my first week, month or quarter in the position?

▪ How will my performance be measured in terms of objectives or metrics?

▪ What do you like best about working for the company?

 

Tips That Can Help in Interview Preparation:

While you prepare for the data analyst interview, do also follow these tips to help yourself for the interview:

 

Research the Business: Figure out what potential issues is the company or business facing at present or may face in the near future. For instance, what data issues might they be experiencing right now? What target groups do they intend to attract? Strategize your approach to resolving these issues based on your research and experience.

 

Observe the Interview Format: Take the opportunity to ask the recruiter for guidance at the beginning of the process. Use sources like discussion posts to sneak a peek at interview experiences and tips.

 

Figure Out Your Top Skills: Throughout the interview process, certain meetings will place a stronger emphasis on some attributes than others. For instance, in a technical interview, you need to demonstrate your familiarity with database languages like SQL. Acknowledge your skills as both combined and separate. Be prepared to talk about technical skills, analytics, and visualizations, as well as business expertise and soft skills.

 

Explore and Practice Data Analyst Interview Questions: Utilize this post to hone your technical skills, accumulate project experience and broaden your portfolio. Additionally, you can work on various business and analytics case studies.

 

Here are the best Data Analytics Courses in India

 

Before, During, and After the Interview: A Few Key Points to Remember

It is not sufficient to prepare only the data analyst interview questions in order to crack this interview. You should also keep in mind some of the following things during the whole interview preparation.

 

Before: Do as much research and preparation as you can to match your background, professional qualifications, and experience with the company and position that are being offered. If the interview is taking place online rather than in person, be sure to perform technical tests on your video and sound.

 

During: Pay close attention to the questions being addressed to you. Interviewers may ask generic questions like “tell me about yourself,” but keep in mind that every question ultimately leads to data analysis. And furthermore, you have to ask about the job role, the company, and your targets.

 

After: Send thank-you emails to hiring managers and recruiters after your interviews. Use this opportunity to connect with them thereafter to get feedback or to ask any questions you might have missed during the interview.

 

Frequently Asked Questions:

 

Q1. What kind of skills does a data analyst require?

A career in data analytics won’t yield rewarding results without intense training and work. To succeed in their line of work and become an in-demand data professional, a data analyst needs to possess a certain set of technical and soft skills, such as:

  • Microsoft Excel
  • Data Cleaning
  • Data Visualisation
  • MATLAB
  • R
  • Python
  • SQL and NoSQL
  • Machine Learning
  • Linear Algebra and Calculus
  • Critical Thinking
  • Communication

 

Q2. Can the data analyst job be done remotely?

Data analyst jobs often allow for remote work due to the nature of the work. So, if you enjoy working alone and have excellent time management skills, this is a great career choice for you.

 

Q3. In terms of salary, how much does a data analyst earn?

The average pay structure of a data analyst based on experience is as follows:

  • Fresher: ₹342,363/year
  • 1-4 yrs of experience: ₹422,408/year
  • 5-9 yrs of experience: ₹690,734/year
  • > 10 yrs of experience: ₹942,653 to ₹1,750,000/year

 

Q4. Is being a data analyst stressful?

The challenges in data analytics might make it a stressful job. However, it might be difficult to say if a job is actually stressful or not, depending on the circumstances, work environment, and project. People who are passionate about the job enjoy it, whilst others may encounter unnecessary stress.

 

Wind-up:

This brings us to the end of our list of data analyst interview questions and answers. The purpose of data analysis is to convert data into useful information that can be incorporated into decision-making processes. Data analysts are in great demand worldwide since they are deployed extensively in many different businesses for a broad variety of applications.  These data analyst interview questions were chosen from a large pool of to-be-expected questions, but if you’re an aspiring data analyst, these are the ones you’ll encounter most usually.

Knowing the answers to these questions will help you out immensely as they serve as the foundation for every interview. We hope you found our post useful while exploring the data analyst interview questions. If you have any doubts or need more information, do let us know in the comment section below. Happy Learning to you!

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