Posted on : 18 Mar, 2021, 12:57:59 PM

Top 50 Data Analyst Interview Questions And Answers

Top 50 Data Analyst Interview Questions And Answers


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 techniques.
  • 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 collecting 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 are the various types of sampling techniques used in data analysis?

There are majorly five different kinds of sampling techniques and methods used by Data Analysts.

  • Simple random sampling
  • Cluster sampling
  • Systematic sampling
  • Judgmental or purposive sampling
  • Stratified sampling

9. What is Data Cleansing in detail?

Data cleansing and Data wrangling refer to a structural way to find erroneous content in huge data and carefully removing them to assure that the data is of the utmost quality. Here are few ways to clean data systematically, and those are:

  • Removing the entire block of data
  • Without causing redundancies, finding ways to fill blank data
  • Replacing and restoring data with its mean and median value
  • Usage placeholders to fill empty spaces

10. What are the best practices for Data Cleansing?

There are five best practices for data cleansing:

  • Make a data cleansing project by knowing where the standard errors take place and retain communication open.
  • Standardizing data at the entry point and in that way becomes less chaotic and able to ensure that all the details and information are standardized, leading to fewer entry errors.
  • It focuses on data accuracy, providing mandatory constraints, maintaining the data value type, and setting cross-field validation.
  • It recognizes and removes duplicates data before working. It will lead to the procedure of effective data analysis.
  • It creates a set of functions/utility tools/scripts to manage data cleaning tasks.

11. 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.

12. 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

13. 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.

 

14. 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

15. 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

16. 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.

17. 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

18. 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

19. 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.

20. 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

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

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

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

The companies’ or businesses’ data keeps changing daily, 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.

23. What are the requirements to become a Data Analyst?

There are several skills that a Data Analyst needs. Some of them are

  • Strong understanding of programming languages like ETL, XML, and JavaScript frameworks
  • Proficient in databases such as MongoDB, SQL, and more
  • Capability to effectively collect and analyze large data
  • Knowledge of data mining and database designing 
  • Having experience/ability working with large datasets

24. 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.

25. What is Normal Distribution?

Normal Distribution leads to a continuous probability distribution that is symmetric about the mean. In a graphical representation, normal distribution will look like a bell curve.

  • The mean, median, and mode always remain equal.
  • Everything is located in the center of the distribution.
  • 99.7% of the data lies between the mean’s three standard deviations
  • 95% of the data lies between the mean’s two standard deviations
  • 68% of the data falls within the mean’s one standard deviation

26. What do you understand by the KNN imputation method?

The KNN imputation method is used to impute the missing attribute data values, imputed by attribute values that are similar to the missing attribute values. There are three different types of missing values, and those are 

  • Missing Completely at Random (MCAR)
  • Missing at Random (MAR)
  • Missing Not at Random (MNAR)

27. What is Time Series analysis?

Time Series analysis refers to a statistical method that deals with a properly ordered series of values of a variable at equal space-time intervals. The time-series data are collected by adjacent periods, which clears that there is a correlation between the observations. The feature distinguishes cross-sectional data from time-series data.

28. What is the difference between Data Joining and Data Blending in Tableau?

Scope Data Joining Data Blending
Definition Data source Worksheet
Availability Can be published It cannot be published.
Join type Defined Left Join
Data loss Possibly No
Calculation field details Row-level or aggregation Requires aggregation
Published sources as Data sources Can not use published sources Can use published sources
Calculated field Possible Possible
Uses Data duplication or filtering through joins is desirable. Need to combine published data sources and needs per sheet relationships

 

 29. What do you understand by Overfitting?

Overfitting refers to a model that prepares the data well by using the training set, and the performance drops considerably over the test set. Overfitting takes place when the model learns noise in the training dataset and the random fluctuations in detail.

30. What do you understand by Underfitting?

Underfitting refers to a model that neither generalizes to new data nor trains the data, and it performs badly in both the test set and train. Underfitting takes place when there are a lesser amount of data to create an accurate model and when an individual tries to develop or build a linear model using non-linear data.

31. What do you Understand by Hadoop Ecosystem?

Hadoop ecosystem is a framework built by Apache to process large datasets in a distributed computing environment for an application. There are various components included in Hadoop.

  • HDFS
  • Lucene
  • YARN
  • Solr
  • MapReduce
  • Ambari
  • Spark
  • Sqoop
  • PIG
  • Flume
  • HIVE
  • Zookeeper
  • HBase
  • Oozie
  • Apache Drill
  • Mahout
  • Spark MLli

32. What is the difference between COUNT, COUNTIF, COUNTA, and COUNTBLANK in Excel?

  • COUNT - helps in counting the number of cells in a range that contains numbers
  • COUNTA - helps in counting the number of cells in a range that are not empty
  • COUNTBLANK - helped in counting the number of blank cells in a selected range 
  • COUNTIF - helps in counting the number of cells in a range that meets the provided conditions

33. What is the importance of Exploratory Data Analysis?

  • Exploratory data analysis helps in understanding the data better.
  • EDA allows you to obtain confidence in the data to a point where an individual is ready to engage a machine learning algorithm.
  • It authorizes you to refine and improve your selection of feature variables that are used later for model building.
  • Allows discovering hidden insights and trends from the data

34. What are the frameworks developed by Apache for processing massive datasets in a distributed computing environment?

  • HDFS
  • MapReduce 
  • Zookeeper 
  • YARN 
  • HBase
  • Spark 
  • PIG, HIVE
  • Flume, Sqoop
  • Solr & Lucene
  • Mahout, Spark MLlib 
  • Ambari 
  • Apache Drill
  • Oozie

35. What are the steps to highlight cells with negative values in Excel?

You can highlight and detail cells by using conditional formatting in Excel. It includes four easy steps, and those are

  • Choose the cells that you want to highlight with the negative values.
  • Go to the option named Home tab and click on the Conditional Formatting option.
  • Go to the option named Highlight Cell Rules and then click on the Less Than option.
  • In the end, In the dialog box of Less Than, specify the value as 0.

36. What are the steps to handle slow Excel workbooks?

There are multiple ways to handle or manage slow excel workbooks, but some strategies can be very popular in handling workbooks.

  • Use manual calculation mode.
  • Prepare all the mentioned data in a single sheet.
  • Use tables in excel and named ranges.
  • Avoid using entire columns and rows in reference.
  • Use helper columns and avoid array formulas.
  • Convert all the untouched formulas to value

 37. What do you understand by Pivot Table?

A pivot table refers to a simple featured statistical table in Microsoft Excel that summarizes the huge data of an extensive table which includes a business intelligence program, spreadsheet, database, etc. Pivot Table is easy to use as it requires dropping and dragging columns/rows headers to create the report. The summary might incorporate averages, statistics, or sums, which helps pivot table groups to bind data together in a meaningful way.

38. What are the different areas included in Pivot Table?

A Pivot table is made up of four different divisions, and those are

  • Values Area
  • Rows Area
  • Column Area
  • Filter Area

39. What do you understand about the Print Area?

A print area in excel refers to a range of cells that individuals are designated to print whenever they print the worksheet. For example - if an individual wants to print the first 45 rows from the whole worksheet, then they can set the first 45 rows as the print area.

40. How can you set a Print Area in Excel?

To set the Print Area, an individual has to follow the four basic steps that include

  • Selecting the cells to set the print area
  • Click on the Page Layout option. 
  • Click on the print area option.
  • Select set print area option

41. What are the most common questions Data Analysts should ask a client before creating a dashboard?

The answer varies on a case-to-case basis, but some common questions that Data Analyst should ask before creating an Excel dashboard are

  • Purpose of the Excel Dashboards
  • Various data sources
  • Usage of the Excel Dashboard
  • The frequency for dashboard updations
  • Client office excel version

42. What is the common syntax style of code writing in SAS?

  • WriteDATA statement and name the database
  • Write INPUT statement to name variables dataset. 
  • Statements always end with a semicolon.
  • Proper spacing between statement and words

43. Explain how PROC SQL works?

PROC SQL is a simultaneous processing method for all the observations. Here are some of the steps to execute PROC SQL

  • SAS scans every statement in SQL processing and checks the syntax errors if any.
  • The SQL optimizer scans the problem inside statements to decide how SQL query should be executed to minimize the runtime.
  • If a table is available in the FROM statements, it is then loaded into the data engine to be accessed in the memory.
  • Calculation and codes are executed.
  • The final table is created in the memory.

44. What do you understand by the term DBMS?

A full form of DBMS is Database Management System that refers to a software application used to interact with the applicants, users, and database to analyze and capture data. The database’s stored data can be easily modified, deleted, and retrieved and can be of any type such as images, strings, numbers, etc.

45. What are different types of DBMS?

There are mainly four different types of DBMS, and those are 

  • Hierarchical DBMS
  • Relational DBMS 
  • Network DBMS
  • Object-oriented DBMS

46. What is the ACID property in a database?

ACID property refers to property that is used in the database to check whether the transactions of data are reliably processed in the system or not. To define each term, understanding ACID is a must. ACID is an acronym for 

  • Atomicity
  • Consistency
  • Isolation
  • Durability.

47. What do you mean by Normalization?

Normalization is the method of organizing and preparing data to avoid redundancy and duplication. There are numerous successive levels of normalization that are also called normal forms. Every standard form depends on the previous form; the first three forms are usually adequate.

48. What is the role of different normal forms in normalization?

There are four different types of normal forms available in normalization, and those are known as 

  • First Normal Form
  • Second Normal Form
  • Third Normal Form 
  • Boyce-Codd Normal Form

49. What are the advantages of Normalization?

  • Better database organization
  • More compact database
  • Greater flexibility for queries
  • Allows easy modification
  • Assure consistent data after modification
  • Easier to implement security
  • Efficient data access
  • Quickly find the information.
  • Reduction of redundant and duplicate data
  • More tables with smaller rows

50. What are the differences between Power BI and Tableau?

Parameters Power BI Tableau
Cost $100 for a yearly subscription $1000 for a yearly subscription
Ease of use Easy to implement Offers variety when it comes to consulting and implementation services.
Application Dashboard AD-Hoc Analysis
Support Level Low High
Visualization Easier to upload data sets Scales better to larger datasets
Users Technical / Non-Technical People Analysts
Infrastructure Software as a Service Flexible

 

 

We, Wissenhive, hope you found this top 50 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 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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