Top Data Analyst Interview Questions & Answers

1. What do data analysts do?

This question is basic but serves an essential function. It weeds out the candidates who lack a rudimentary understanding of data analysis. It also lets you compare how well various candidates understand data analysis.

What to look for in an answer:

• Coverage of each step
• Mention of soft skills, such as communication
• Discussion of how data analysts benefit a company

2. What are the various steps involved in any analytics project?

The various steps involved in any common analytics projects are as follows:

Understanding the Problem

Understand the business problem, define the organizational goals, and plan for a lucrative solution.

Collecting Data

Gather the right data from various sources and other information based on your priorities.

Cleaning Data

Clean the data to remove unwanted, redundant, and missing values, and make it ready for analysis.

Exploring and Analyzing Data

Use data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze data.

Interpreting the Results

Interpret the results to find out hidden patterns, future trends, and gain insights.

3. What are the best practices for data cleaning?

Ans. There are 5 basic best practices for data cleaning:

• Make a data cleaning plan by understanding where the common errors take place and keep communications open.
• Standardize the data at the point of entry. This way it is less chaotic and you will be able to ensure that all information is standardized, leading to fewer errors on entry.
• Focus on the accuracy of the data. Maintain the value types of data, provide mandatory constraints, and set cross-field validation.
• Identify and remove duplicates before working with the data. This will lead to an effective data analysis process.
• Create a set of utility tools/functions/scripts to handle common data cleaning tasks.

4. Mention what are the various steps in an analytics project?

Various steps in an analytics project include

• Problem definition
• Data exploration
• Data preparation
• Modeling
• Validation of data
• Implementation and tracking

5. What is Data Validation?

Data validation, as the name suggests, is the process that involves determining the accuracy of data and the quality of the source as well. There are many processes in data validation but the main ones are data screening and data verification.

• Data screening: Making use of a variety of models to ensure that the data is accurate and no redundancies are present.
• Data verification: If there is a redundancy, it is evaluated based on multiple steps and then a call is taken to ensure the presence of the data item.

6. What are the challenges that are faced as a data analyst?

There are various ways you can answer the question. It might be very badly formatted data when the data isn’t enough to work with, clients provide data they have supposedly cleaned it but it has been made worse, not getting updated data or there might be factual/data entry errors.

7. What is your process when you start a new project?

This question lets you measure candidates’ organizational skills and how well they anticipate. It also gives you an opportunity to see if candidates’ leadership or work styles are compatible with your company culture.

What to look for in an answer:

• Clear steps
• Deliberate process
• Consideration of deadline

8. What is Data Analysis, in brief?

Data analysis is a structured procedure that involves working with data by performing activities such as ingestion, cleaning, transforming, and assessing it to provide insights, which can be used to drive revenue. 

Data is collected, to begin with, from varied sources. Since the data is a raw entity, it has to be cleaned and processed to fill out missing values and to remove any entity that is out of the scope of usage.

After pre-processing the data, it can be analyzed with the help of models, which use the data to perform some analysis on it.

The last step involves reporting and ensuring that the data output is converted to a format that can also cater to a non-technical audience, alongside the analysts.

9. Mention what is the difference between data mining and data profiling?

The difference between data mining and data profiling is that

Data profiling: It targets on the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value and their frequency, occurrence of null values, data type, length, etc.

Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.

10. Mention the name of the framework developed by Apache for processing large data set for an application in a distributed computing environment?

Hadoop and MapReduce is the programming framework developed by Apache for processing large data set for an application in a distributed computing environment.