- What does data analysis primarily involve?
- Why do businesses need data analysts?
- How would you differentiate between a data analyst and a data scientist?
- What is the difference between data mining and data analysis?
- What do you understand by data cleaning?
- What are some best practices for data cleansing?
- What are some data validation methods used in data analysis?
- Onwhatcriteriaswouldyoujudgeagooddatamodel
- What would you do with missing or suspicious data?
- What is KNN imputation?
- What do you know about the kmeans algorithm?
- How would you distinguish between univariate, bivariate and multivariate analysis?
- Can you explain some types of hypothesis testing?
- How would you define normal distribution?
- Name a few statistical methods used for data analysis
- Are you familiar with SAS?
- Which important data analytics tools are you familiar with?
- What is a pivot table and what are its sections?
- What is metadata?
- Can you name some python libraries used in data analytics?
- What do you understand by data aggregation?
- Can you elaborate on DBMS and its types?
- How is SQL related to relational databases?
- In what ways have you used SQL in the past?
- Can you write a query for a function?
- How would you explain the acronym ACID with relation to a database?
- Can you estimate how many umbrellas are sold in Mumbai in August each year?
- Which data analytics tools do you usually work with?
- What has been the most challenging project for you?
- What sort of problems do data analysts often run into?
- What made you choose data analysis as a career?
- Interview tips to get a data analyst job
With data analysis taking the front seat in driving all business decisions, the demand for data professionals is at an all-time high in the current scenario.
If you are preparing to become a data analyst, this blog is for you!
Here are 30 crucial data analyst interview questions and answers to help you bag your dream data analyst job.
What does data analysis primarily involve?
Data analysis involves collecting, cleansing, interpreting, and then transforming and modeling the data to get relevant insights.
Reports are generated to be used by organizations to make effective business decisions.
Why do businesses need data analysts?
This is a common data analyst interview question asked to understand your perspective about the field.
Answer to reflect interest, understanding of the profile and required skills that a data analyst must possess.
You can also include real-world examples with your answer instead of solely relying on general data analyst roles.
Sample answer 1
Businesses have always collected and analyzed data at some level, but skilled professionals use data analysis tools to get more bang for the buck.
Data analysts can provide valuable insights that can help a business track internal operations, gauge employee performances, understand markets, make decision-making processes faster and sharper, lead to the creation of new products and services, generate more profit, etc.
Sample answer 2
Data analysts increase efficiency in data collection and analysis using modern tools and ensure that no valuable data is lost.
They bring critical thinking and creativity to a business to get useful information on opportunities it can cash in on and to spot loopholes to prevent any major losses.
How would you differentiate between a data analyst and a data scientist?
Although both the roles may have some overlapping responsibilities, there are some differences between a data analyst and a data scientist.
This includes their sources of data, data visualization requirements, and dealing with business questions.
Data scientists are often expected to know about machine learning too, whereas a data analyst is not.
What is the difference between data mining and data analysis?
Data mining refers to a process that recognizes patterns in stored data, whereas data analysis is used to organize raw data before storing it.
Data analysis also involves data cleaning, while mining is done on cleaned data.
In terms of data interpretation, results from data analysis are easier to interpret than data mining results.
What do you understand by data cleaning?
Data cleaning is a function that involves exploring correlations to fill in data, introducing dummy variables to fill the empty spaces, replacing with mean/median, leaving a record as it is, or even removing a record.
It is done to identify and remove errors from the given data to improve quality.
What are some best practices for data cleansing?
Data cleansing should begin by making a cleaning plan.
Firstly, a data analyst needs to understand where common errors happen, followed by standardizing the data at the point of entry to make it more ordered.
Data cleansing should focus on accuracy by maintaining value types, providing mandatory constraints, setting cross-field validation, and removing duplicates.
It is also important to create a set of data analysis tools or scripts to handle common data cleaning tasks.
What are some data validation methods used in data analysis?
Some data validation methods used in data analysis are
- Field-level validation (done in each field).
- Form-level validation (done upon completion of the form).
- Data saving validation (done during the saving process of the file or database).
- Search criteria validation (done to match what a user is searching for to ensure correct results).
On what criteria would you judge a good data model?
A good data model can be judged based on
- Intuitiveness.
- Ease of data consumption.
- Scalability of data changes.
- Predictability of performance.
- Ability to evolve and support new cases.
What would you do with missing or suspicious data?
If there is suspicious or missing data, I would make a validation report to give clear information and ensure that the invalid data is updated with a validation code.
I would use the best analytical strategy for missing data involving simple imputation, deletion or case wise imputation.
In the case of uncertainty, I would ask an experienced person for a second opinion to determine its acceptability.
What is KNN imputation?
KNN is an algorithm that is used to match a point with its closest k-neighbors in a multidimensional space.
It is used in a method to impute missing attribute values which are imputed by the attribute values most similar to the attributes with missing values
What do you know about the kmeans algorithm?
The kmeans algorithm divides a data set into clusters to make each a homogeneous cluster that contains points that are close.
It is also used to separate algorithms from each other.
How would you distinguish between univariate, bivariate and multivariate analysis?
The three modes of descriptive statistical techniques made for analysis are based on the variable count at a given point in time.
As the terms suggest, the univariate analysis takes into account data which has only one variable, bivariate analysis finds differences between two variables, and multivariate analysis deals with multiple variables.
Can you explain some types of hypothesis testing?
The 3 most common types of hypothesis testing are
- T-test- used in case of small sample size and unknown standard deviation,
- Analysis of Variance or ANOVA- analyses differences between the means across groups
- Chi-Square Test for Independence- calculates the significance of the association between categorical variables in a population sample, etc.
How would you define normal distribution?
Normal distribution is what we most commonly see and represent as a bell curve. It is a probability distribution that is symmetric around the mean.
Normal distribution measures standard deviation and the degree to which the values differ in their mean, often indicating that data close to the mean has a higher frequency than away from it.
Name a few statistical methods used for data analysis.
Several methods can be used according to requirements, but some of the more popular ones are
- Linear regression
- Non-linear models
- Classification
- Resampling
- Subset selection
- Tree-based methods
- Shrinkage
Tip- Study and revise some methods in detail before the interview if the job profile calls for in-depth statistical knowledge.
Are you familiar with SAS?
Yes, SAS or Statistical Analysis System is a group of software applications which makes up for some of the most popular statistical tools.
SAS may also refer to the company which has built this software. It is capable of processing complicated data and extract useful information out of it.
It also lets analysts mine, modify and manage data from multiple sources.
Tip- It might also be useful to have a working knowledge of SAS, in case you are asked about it in the interview.
Which important data analytics tools are you familiar with?
Although skill sets might vary from analyst to analyst, data analysts need to be up to date with important data analytics tools.
Focus on the tools required for the job profile you are interviewing for.
Sample answer
The most crucial data analytics tools are Hadoop, programming languages, statistical methods, and MS Excel.
Tools like RapidMiner, Tableau, NodeXL, and Google Search Operators can also be useful.
What is a pivot table and what are its sections?
The pivot table is a feature of MS Excel that summarizes big datasets to make it comprehensive.
The summarization may include statistics like sums and averages. Pivot tables sort, count, group and reorganize data in a database.
It has four sections, namely values area, row area, column area, and filter area.
What is metadata?
Generally speaking, metadata refers to the set of data that gives information about other data or the data system.
It defines the type of data available which will be sorted during analysis.
Tip- You can illustrate this answer further by using an example of your choice.
Can you name some python libraries used in data analytics?
To answer this question, mention the libraries you are proficient with, in case you are asked more detailed follow-up questions on any of the python libraries.
Sure! Some crucial python libraries used in data analytics are
- NumPy
- Bokeh
- SciKit
- Matplotlib
- SciPy
- TensorFlow
What do you understand by data aggregation?
Data aggregation refers to the process where information is viewed or expressed in a summarised form.
In data analysis, data aggregation can involve the compilation of information from databases to create data sets for processing.
Can you elaborate on DBMS and its types?
Yes, DBMS stands for Data Base Management System. This software interacts with users, applications, and databases to collect and analyze data.
The stored data in the databases, which can be numbers, images, etc., can be changed, retrieved and even deleted.
The main types of DBMS are relational, hierarchical, network and object-oriented.
How is SQL related to relational databases?
To answer effectively, explain your understanding of SQL by using concrete examples where relational databases are used along with the relationship between SQL and relational databases.
Sample Answer
A relational database is a set of tables from where data can be accessed or reorganized in multiple ways.
The linked data tables are used to store a variety of information, which can be used to answer particular analytical queries.
SQL or Structured Query Language is a programming language that is used as a standard tool to communicate with such a relational database.
SQL is designed to manage data in relational database management systems.
In what ways have you used SQL in the past?
This is another question to test how sound your practical experience with common data analysis tools is.
You should draw upon any situations or tasks where you have used SQL or have learned to use it.
Speak briefly about what you used the application for, which datasets you dealt with and for what purpose, and how it analyzed collected data.
Can you write a query for a function?
You can be given any function under the sun as a way to demonstrate your knowledge of running SQL queries.
Interviewers aim to gauge how well you know your programming. Remember to practice writing basic queries before you appear for the interview.
How would you explain the acronym ACID with relation to a database?
ACID stands for Atomicity, Consistency, Isolation, and Durability. It refers to a property used to ensure the reliability of data operations in a system.
Atomicity refers to data transactions that are either wholly successful or failed.
Consistency ensures that data meets all the rules of validation.
Isolation is used to keep data transactions separate until they are done.
Durability is used to check that committed transactions are never lost in case of any errors or crashes.
Can you estimate how many umbrellas are sold in Mumbai in August each year?
This is just a sample question, variations of which can be posed by an interviewer.
Such questions observe whether a data analyst’s thinking is quick and organized when asked to solve a problem without any given datasets or the help of a computer.
Your answer should show your ability to comprehend the situation, identify data segments and variables, formulate a solving process, and articulate it to the interviewer.
Which data analytics tools do you usually work with?
Your recruiter is also very interested in the hard skills you possess and are competent in.
When speaking about the software and tools you know, make sure you mention the data analytics software specified by the company.
If you know how to use it well, tell the interviewers about your experience using it. If not, let them know that you will require a bit of training.
Either way, the interviewers are most probably interested in how familiar you are with popular tools of the trade.
What has been the most challenging project for you?
To answer such a question, explain in detail the project given to you, why you found it challenging, and how you solved it.
It not only tells the interviewer about your experience and problem-solving approaches but also how you manage difficult tasks in the workplace.
Focus on convincing the recruiters that you are open to challenges and possess the skills to research and solve them.
Do not speak negatively of the project or whoever assigned it to you while answering.
What sort of problems do data analysts often run into?
There are multiple answers to this. You can either list the general problems which data analysts face or answer from your own experience as a data analyst.
Common problems include bad sources, low-quality data, incomplete data, errors, etc.
What made you choose data analysis as a career?
This question expects you to come forward as more than just a data analyst. It lets the company know you as a person, so be honest but careful about how you answer.
Do not meander too much while speaking about your interests.
Focus on the factors which led you to become a data analyst and any past work experiences which reinforced your decisions.
It is important to be concise and let your personality as a dedicated professional shine through here.
Interview tips to get a data analyst job
Here are a few tips to help you bag your dream data analyst job.
- Get to know the company
Do some in-depth research on the company you are interviewing with. Find out about the nature of work, data operations, work-culture, etc.
Research about new data-centric projects and objectives to know the company better. This will also help you build relevant talking points for the interview.
You can also find out the data analysts currently working with the organization and their reviews about the same.
- Strengthen your mathematical, statistical and programming knowledge
A solid base in applied math/statistics and languages like Python and R are very useful in landing a data analyst job.
Before the interview, revise any necessary formulae, functions, coding and programming steps as well as real-world applications of this knowledge.
The interviewers should get a sense of a strong foundation in math and programming when you answer technical questions.
- Show strong work experience
Simply knowing about tools and techniques is not impressive in an interview.
Support your answers with what you have learned while working with data.
Use examples like something interested you in a data science course, challenges you faced while working on actual projects, or how you solved a problem in a previous job role.
- Stay updated with the field
Big data and data analysis are both very pertinent in the world right now and interesting in the things they can achieve.
It is important to stay updated on how data is being used on all levels, by what sort of organizations, new tools being used, new trends being discovered, etc.
If you mention such details in your answers, your interviewers will be able to see true passion and curiosity in you.
- Prepare for general questions
Apart from preparing for technical questions about your field, it is also important that you read through general interview questions to prepare better for your interview.
Read common questions asked in interviews and practice their answers before sitting in the interview.
Also Read: 60 Basic Interview Questions And Answers
- Dress well
Dressing sense is crucial to interviews and boosts confidence.
Dress appropriately and look your best.
You can read more about interview dress code at Interview Dress Code For Males and Females
- Demonstrate a keen business sense
Companies want to hire data analysts for the precious insights they provide during the processes of strategizing and product-building.
Having an aptitude or knowledge of how to conduct business or market research is an added advantage in an interview.
Show your interviewers that you know about using data to solve problems creatively and help the growth of an organization using examples from new developments data analysis as well as actual work you may have done previously.
Good luck!
