Posted on : 14 Sep, 2021, 11:27:27 AM

Top 20 Data Analyst Interview Questions And Answers in 2021.

Top 20 Data Analyst Interview Questions And Answers in 2021.


Data Analysis is a procedure of transforming data to find useful information to make a decision and deriving a conclusion. The Data Analysis technology is widely used in every sector for multiple purposes. Hence the demand for data analysts remains high worldwide.

To build a strong career in the Data Analysis field, candidates need to crack the interview first in which they ask many Data Analyst interview questions.

We, Wissenhive, compiled a list of frequently asked interview questions with answers for Data Analysts that candidates might encounter during job interviews. It includes basic to advanced interview questions depending on the candidate's experience and various factors.

1. What do you understand by Data Analysis?

Data Analysis refers to a structural process that includes working with huge data by exciting some activities such as cleaning, ingestion, assessing, and transforming it to deliver insights used to drive revenues. Data collected from various sources and at the beginning, data is respected as a raw entity as it has to be processed and cleaned to fill the missing values out by removing the entities that are out of the usage scope.

After preprocessing all the data, it is analyzed with the help of different advanced models, which utilize the data to conduct some analysis. The final step includes ensuring and reporting the data output that is converted into a format that caters to non-technical people alongside the data analysts.

2. What are the popular tools used to implement Data Analysis?

There is a broad spectrum of software and tools that are used in the field of data analysis. Here are some of the top ones include

  • RapidMiner
  • Google Search Operators
  • OpenRefine
  • KNIME
  • Tableau

3. What is the difference between Data Analysis and Data Mining?

 

Data Analysis

Data Mining

Data Analysis is used to organize and order raw data in a meaningful form.

Data Mining is used to recognize the pattern in stored data.

Data Analysis involves data cleaning, which leads to a non-presented form of document format.

Data Mining provides clean and well-documented data.

It is easy to interpret and results extracted from data analysis.

It is not easy to interpret and results extracted from data mining.

 

4. What are the primary responsibilities of a Data Analyst?

The role of a data analyst includes various responsibilities and those includes.

  • Data Analyst provides strong support to coordinate all the data analysts with customers and staff.
  • Identifies new areas or process for improvement opportunity
  • Identify, analyze and interpret patterns or trends in complex data sets.
  • Maintaining data systems or databases and acquiring data from primary and secondary sources.
  • Data Analyst performs an audit on data and resolves business-related issues for customers.
  • It Interprets data and analyzes results by using statistical technology.
  • Work closely with information and management needs and prioritize project needs.
  • Cleans and filters data and reviews computer reports.
  • Determining performance indicators to correct and locate code problems
  • Provides security to the database by developed access systems and determining access at the user level.

5. What is the process of Data Analysis?

Data analysis refers to a process of cleansing, collecting, interpreting, modeling, and transforming data to generate reports and gather insights to gain business profits.

6. Explain the processes of Data Analysis?

There are basically three processes included in data analysis: collecting data, analyzing data, and creating reports.

  • Collecting Data - Data is collected from multiple sources and stored in clear and well-prepared data. In these steps, all the missing outlines and values are removed.
  • Analyzing Data - after preparing the data, the next step is to analyze the collected data. A model repeatedly runs for better improvement then checks the mode validation whether it meets businesses’ needs or requirements.
  • Creating Reports - Then comes the implementation of the model by reporting and passing on stakeholders.

7. What are the various steps included in an analytics project?

The various steps included in analytics projects are

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

8. What is Data Validation?

As the name suggests, data validation is the process that determines the accuracy of provided data and its quality of the source. There are various methods to process the validation of data, but the main ones are data verification and data screening.

  • Data verification - Discovers redundancy that can be evaluated based on various steps and ensures the data item’s presence.
  • Data screening - Use a variety of models to ensure the accuracy of data and track if there were any redundancies presented in the data.

9. How many types of data validation methods are used in Data Analysis?

There are four different types of data validation methods included in data analysis, and those are

  • Field Level Validation
  • Form Level Validation
  • Data Saving Validation
  • Search Criteria Validation

10. Differentiate between Data Mining and Data Profiling?

 

Data Mining

Data Profiling

Data mining refers to identifying patterns and correlations with a huge database.

Data profiling is a process of analyzing data from existing datasets to determine the actual content.

It involves applying computer-based methodologies and mathematical algorithms to extract information hidden in the data.

It involves analyzing the raw data from existing datasets.

The purpose of data mining is to mine the data for actionable information.

The goal is to create a knowledge base of accurate information about the data.

The data mining tasks are classification, clustering, regression, etc.

It employs a set of activities, including discoveries and analytics techniques.

 

11. What are some of the common obstacles faced by Data Analysts during analysis?

The common difficulties faced by data analysts during data analysis includes

  • Handling and managing duplicates
  • Collecting the accurate data at the right time
  • Handling data storage and purging problems
  • Cleaning with compliance issues
  • Making the data secure

12. What do you mean by a data collection plan?

A data collection plan refers to the procedure used to collect all the important data in a system, which covers

  • The data type that needs to be gathered or collected
  • Various data sources for investigating a data set

13. What are the criteria to say whether a developed data model is perfect or not?

The answer to this question varies from analyst to analyst, but there are a few criteria that are considered to decide whether the developed model of data is perfect or not.

  • A designed model for the dataset should have outstanding predictable performance, required to predict the future.
  • A developed model is considered good when it is easy to adapt to changes according to the company’s requirements.
  • If data changed, then the model should be capable of scaling with the data.
  • The designed model should be easy and flexible to consume by the customers for profitable and actionable results.

14. What are the technical tools that are used for analysis and presentation purposes?

Data analysts are expected to understand the tools for analysis and presentation purposes. Some of the demanded and popular tools are:

  • MS SQL Server
  • MySQL
  • MS Excel
  • Tableau
  • Google Search Operators
  • Google Fusion Tables
  • R
  • Python
  • SPSS
  • RapidMiner
  • MS PowerPoint
  • OpenRefine

15. What are the benefits of version control?

The primary advantages of using version control are

  • It helps in identifying differences, enables comparing files and merging the changes.
  • It allows you to keep on track of building applications by identifying the underdevelopment, production, and QA version.
  • It helps in improving the culture of collaborative work.
  • It keeps different variants and versions of secured code files.
  • Allows to check and see changes made in the file’s content
  • Records a complete history of the case project file in central server breakdown

16. Explain what you do with missing or suspicious or missing?

When there is any missing or suspicious data, then.

  • Create a validation report to present information on the missing or suspected data.
  • Have trained personnel look at it so that it can determine its acceptability.
  • Update Invalid data with a validation code.
  • Utilize the best analysis approach to work on the suspicious or missing data such as deletion method, simple imputation, or case wise imputation.

17. How can you manage missing values in a dataset?

There are four different technique to handle and manage missing value in the dataset, and those are

  • Listwise Deletion
  • Average Imputation
  • Multiple Imputations
  • Regression Substitution

18. What are some Python libraries used by Data Analysts for Analysis?

  • Bokeh
  • Keras
  • Matplotlib
  • TensorFlow
  • NumPy
  • Seaborn
  • SciPy
  • Pandas
  • SciKit

19. When do you think Data Analyst should retrain a model?

The companies’ or businesses’ data keeps changing on a daily basis, but the format remains the same. When an operational business process enters a new market, seeing a sudden rise of opposition or seeing its position failing or rising, it is suggested to retrain the model. So, as and when the business dynamics shift, it is recommended to retrain the model with customers’ changing behaviors.

20. What is the true positive rate and recall?

The true positive rate, also referred to as sensitivity or recall, is used to estimate and measure the actual percentage of original positives, which are correctly classified and identified.

We, Wissenhive, hope you found this top 20 Data Analyst interview questions and answers article useful. The questions covered in this article are the most sought-after interview questions for a data analyst that will help candidates in acing your next interview!

If you are searching forward to learning and mastering all of the Data Science and Data Analytics concepts and earning a certification in the same, do take a look at Wissenhive’s latest and advanced Data Science related certification offerings.

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