Most Asked Data Analyst Interview Questions and Answers

Read Top Data Analyst Interview Questions and Answers Experienced, Freshers for Job Interview Quiz with Solutions.

What is the responsibility of a Data analyst?

Responsibility of a Data analyst include,

  • Provide support to all data analysis and coordinate with customers and staffs

  • Resolve business associated issues for clients and performing audit on data

  • Analyze results and interpret data using statistical techniques and provide ongoing reports

  • Prioritize business needs and work closely with management and information needs

  • Identify new process or areas for improvement opportunities

  • Analyze, identify and interpret trends or patterns in complex data sets

  • Acquire data from primary or secondary data sources and maintain databases/data systems

  • Filter and “clean” data, and review computer reports

  • Determine performance indicators to locate and correct code problems

  • Securing database by developing access system by determining user level of access.

What is required to become a data analyst?

To become a data analyst,

  • Robust knowledge on reporting packages (Business Objects), programming language (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, etc.)

  • Strong skills with the ability to analyze, organize, collect and disseminate big data with accuracy

  • Technical knowledge in database design, data models, data mining and segmentation techniques

  • Strong knowledge on statistical packages for analyzing large datasets (SAS, Excel, SPSS, etc.)

What are the various steps in an analytics project?

Various steps in an analytics project include

  • Problem definition

  • Data exploration

  • Data preparation

  • Modelling

  • Validation of data

  • Implementation and tracking

What is data cleansing?

Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.

List out some of the best practices for data cleaning?

Some of the best practices for data cleaning includes,

  • Sort data by different attributes

  • For large datasets cleanse it stepwise and improve the data with each step until you achieve a good data quality

  • For large datasets, break them into small data. Working with less data will increase your iteration speed

  • To handle common cleansing task create a set of utility functions/tools/scripts. It might include, remapping values based on a CSV file or SQL database or, regex search-and-replace, blanking out all values that don’t match a regex

  • If you have an issue with data cleanliness, arrange them by estimated frequency and attack the most common problems

  • Analyze the summary statistics for each column ( standard deviation, mean, number of missing values,)

  • Keep track of every date cleaning operation, so you can alter changes or remove operations if required

What is logistic regression?

Logistic regression is a statistical method for examining a dataset in which there are one or more independent variables that defines an outcome.

List of some best tools that can be useful for data-analysis?

  • Tableau

  • RapidMiner

  • OpenRefine


  • Google Search Operators

  • Solver

  • NodeXL

  • io

  • Wolfram Alpha’s

  • Google Fusion tables

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.

List out some common problems faced by data analyst?

Some of the common problems faced by data analyst are

  • Common misspelling

  • Duplicate entries

  • Missing values

  • Illegal values

  • Varying value representations

  • Identifying overlapping data

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

Hive is the programming framework developed by Google for processing large data set for an application in a distributed computing environment.


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