This section covers commonly asked and expert level Data Analyst Interview questions and answers. The types of questions covered are general, conceptual, behavioral, situational and experience based. You can also find interesting examples and sample answers with each question.
Who are these Data Analyst Interview Questions useful for?
These interview questions will be very useful to all the candidates interviewing for the role of Senior or Junior level Data Analysts.
Both entry level freshers and experienced candidates will be benefited by these questions and answers.
1. What do you know about the responsibilities of a Data Analyst?
The main responsibilities of a data analyst are:
i.) Obtaining data from primary and secondary sources and maintaining databases.
ii.) Filter and clean existing data, monitor and audit data quality.
iii.) Interpreting data and analyzing results using statistical techniques
iv.) Use data in order to identify trends in customer base.
v.) Develop data collection systems and other strategies that optimize statistical efficiency and quality.
vi.) Identify patterns in complex data sets.
vii.) Prepare reports for internal and external audiences using business analytics reporting tools.
viii.) Coordinate with external and internal clients, to understand data content.
ix.) Identify business needs and prioritize business data requirements with the management.
x.) Identify opportunities to improve the process and define the new process.
Video : Data Analyst Interview Questions and Answers
2. What are the skills required to be a successful Data Analyst?
In order to be successful as a Data Analyst following are some of the required skills:
i.) Attention to detail: T be able to analyze large amount of data.
ii.) Problem Solving Skills: Ability to identify problems and find a solution.
iii.) Creative Thinking: Brainstorm and identify new approaches to data analysis.
iv.) Industry Knowledge: Understand how data contributes to the success of an organization.
v.) Effective Communication Skills: To present data and reports in a clear and effective manner.
vi.) Analytical skills: Look at the data differently and analyze the relevant information required.
vii.) Technical Skills: Ability to learn different software which help with the job.
3. What is data cleansing?
As the name suggests, cleansing of data is a process to find if the existing data has any repetitive data or incorrect or irrelevant information. This is one of the important steps in data analysis which helps to sort the data that is modified or deleted.
Data cleansing is also known as Data Scrubbing.
4. What are the advantages of data cleansing?
Data cleansing helps to lower the operational cost that helps in increasing the profits.
Some of the main advantages of data cleansing are as follows:
i.) Helps to target the right audience: The information of the customer keeps changing due to relocation, etc. This needs the data to be updated regularly. The correct and clean data can help to carry out a successful marketing campaign to effectively reach out to the right audience. A clean list with the right prospects can ensure the highest return on the email campaign.
ii.) Helps in effective decision making: A clean data can help in the better analysis which helps the organization to take business decisions
iii.) Helps in launching a new product or service: A clean data provides various insights about the customer's likes and dislikes. Clean data with right analytics can help the enterprise to develop and launch a new product or service in the market.
iv.) Helps to increase productivity: Clean data improves the employee's efficiency and productivity.The employees do not waste time in sending emails to the wrong audience.
v.) Helps to increase revenue: Accurate data can drastically improve the response rate which eventually results in increased revenue.
5. What best practices would you recommend for data cleaning?
Some of the best practices for data cleaning are:
i.) Use various attributes to sort the data.
ii.)If the dataset is large, break it down into smaller sets.
iii.) For large datasets, take a stepwise approach. Keep improving the data with each iteration until you achieve a good quality data.
iv.) For common cleansing, create some utilities. These may be function, tools or scripts.
v.) To improve the data cleanliness, arrange the data by frequency and handle the problem that occur most frequently.
vi.) Analyze the summary statistics for each column. Study the standard deviation, mean, number of missing values etc.
vi.) It is very important to keep a track of all the cleaning operations, datewise This way, you can alter the changes or undo any operation that do not feel right.
6. What are the common problems faced by data analysts?
Thedata analysts need to sort and analyze the data so that it becomes useful. The analyst, however, faces a lot of problem due to the following:\
i.) Inadequate or faulty data or inconsistent data with missing values.
ii.) Data with repetitive and duplicate entries.
iii.) Data with different value representations.
iv.) Wrong spelling of a word is one of the common problems faced by the data analyst.
v.) Data with poor formatting makes the work of the analyst difficult.
7. How would you handle suspected or missing data?
Following things should be done to handle suspected data:
i.) A validation report carrying the details of all suspected data should generated. The report should carry details like the failed validation criteria, date and time of occurrence etc.
ii.) If you are unsure, get the data examined by a senior to see if it is acceptable.
iii.) Assign a validation code to invalid data and replace it.
iv.) For missing data, use the most suitable out of deletion method, model based methods or imputation methods etc.
8. What impact does data analytics have on customers?
With this question, the interviewer is trying to understand if you are aware of the importance of data analytics. He/she would like to know if you know how data can impact the customers buying behavior. They would like to know if you are aware of its advantages.
"To answer this question you can say something like: Data analytics is very important for any business firm. The data of a company is an asset which helps to improve the efficiency of the business."
Analytics helps to predict the customer's demand. You will be able to know what the customers like, what their requirement is and what channels they use to buy a particular kind of product. All this information will help you to channelize your marketing campaigns which also help to improve the productivity of the employees. It improves customer experience and also helps to improve the product from the feedback of the customers.