Top 10 Data Analyst Interview Questions

Congratulations on getting an interview call for a data analyst job! While there might be a couple of days left for the interview, you must utilize the time to prepare for data analyst interview questions. Whether this is the first time you will be interviewed or have already attended a few, grabbing the data analyst job means exploring growth and opportunities. The data industry needs people with exceptional math, computer, communication, logical, critical thinking, and analytical skills.

Data Analyst Salary

The Job prospects and remuneration in the data industry are attractive. As per a survey conducted by a reputed job portal in the first quarter of 2019, it is learned that the Data Analyst Salary worldwide range in between $67,500 to $83,850.

Data Analyst Job Trends

The survey has also pointed toward Data Analyst Job Trends according to which the total number of jobs in five states of the United States: New York, California, Illinois, District of Columbia, and Massachusetts is 14436.

Nailing Data Analyst Interview

To nail the interview, it's good to be prepared. However, while answering the questions, you shouldn't sound as rehearsed. The way way to respond to questions is by being natural and speak the way you do with your friends. Of course, respecting the interviewers and being a professional during the interview is necessary. Accept it that there will be questions that can unsettle you and be an acid test for you. So preparing for different types of questions related to the the job is essential. As there is no way to guess the questions of the interviewer, at least be prepared for these to 10 data analyst interview questions.

1. What key skills are required for a data analyst job?

Most probably, you will be asked this question during your data analyst interview. The reason to ask it is to check whether you have the knowledge about the required skill set for the data analyst job, or just trying to make hay of the Data Analyst Job Trends.

To work as a data analyst, you need to have the following skill set:

Whether you have these skills or not, work or re-work on these skills.

2. What are the duties and responsibilities of a Data Analyst?

Since what you are doing as a data analyst would depend on your employer's needs, it's possible that you may not be aware of the entire duties and responsibilities of a data analyst. So being aware of what the job entails is necessary.

The duties and responsibilities could be varied. However, you must be able to define the role of a data analyst clearly.

3. What are the statistical methods used for analyzing data?

The statistical methods used for analyzing data will differ according to the types of tools used in the job. As such, the answer could be different for this question. The methods used will also depend on the type of data being analyzed. However, you can give a few examples such as:

4. What is the difference between data analysis and data mining?

To work as a data analyst, you must know the difference between data analysis and data mining. So explain with a few examples that data analysis starts by creating a theory, premise, or assumption. This means you need to construct a hypothesis first to begin with a research. Data analysis involved cleansing and filtering of data and interpreting the results to be shared with the stakeholders. On the other had, data mining requires clean and complete data. It doesn't require any hypothesis since it is constructed automatically by algorithms based on the equations created based on hypothesis.

5. What are the typical data analysis processes?

Data analysis aims at gathering, evaluating, inspecting, converting, and modeling for deriving valuable insights to support for better decision making.

Here are the steps involved in data analysis:

6. What are the two steps involved in the data validation process?

Demonstrating your knowledge about the steps involved during the data validation process will build the confidence of the interviewer in you. The two steps are:

Data Screening - Screening or filtering the data for errors and removing them before performing data analysis.

Data Verification - Checking for accuracy and completeness of data and removing any inconsistencies once data migration is conducted and completed.

7. How Data Mining Differs from Data Profiling?

Data Mining helps in uncovering the hidden patterns and relationships in data in order to make predictions for the future. It aims at identifying sequence discovery, unusual records, bulk analysis, different types of attributes, etc.

Data Profiling is a process carried out to analyze data values for consistency, frequency, length, uniqueness, and logic in a given dataset. It cannot detect an inaccuracy or incorrectness in data, but can identify irregularities and violations in business operations.

8. What is Data Cleansing?

When you're given a dataset for analysis, it's not necessary everything in that dataset will be worth using. As a data analyst, you must have the skills to sort out what information is important for analysis process. Therefore, you need to inspect and clean data to identify any irregularities, repetitive in data, and remove incorrect information. However, no information from the dataset should be deleted for analysis. Rather, the information needs to be enhanced to be used for analysis.

9. What are the best tools for data analysis?

There are open source tools as well as paid versions that are both user friendly and performance-oriented. The company you would be interviewed with may use either open source or paid version. So having knowledge of the most popular, useful, and common tools is necessary, which are:

10. Define "Outlier"

All data analysts must know this term and be able to work with it since understanding it is important in analyzing data. Basically, outliers are the data records that differ from others. An outlier could possibly cause a difference in the results derived through analytical systems and algorithms. Univariate and Multivariate are the two kinds of outliers in the data analysis field.

Don't memorize these questions and sound like a parrot. Instead, try to get into the root of the answer, and respond effectively.

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