What are top 15 Data Analyst Interview Questions and Answers?

What are the top 15 Data Analyst Interview Questions and Answers?

Data analytics has emerged as the latest hotshot for organisations, with tremendous opportunities arising daily in the industry. 

Here are some of the most asked data analyst interview questions one may encounter while sitting for a data analytics job.

  • What are the key skills required for becoming a data analyst?

To become a data analyst, you must possess strong Microsoft Excel skills. 

A typical data analyst's job responsibilities involve gathering and organising the data.

A data analyst must have a thorough understanding of business-related tools, statistics, mathematics, and computer languages like Java, SQL, C++, etc. 

For the profession, one also needs solid analytics training, data mining knowledge, pattern identification skills, and problem-solving aptitude.

  • What does "data cleansing" mean?

Data cleansing refers to the process of detecting and removing any inconsistency or errors from the data to improve its quality. 

  • What are some of the best tools for data analysis?

Some of the most useful tools for data analysis are Google Search Operators, KNIME, Tableau, Solver and RapidMiner.

  • What is the KNN imputation method?

KNN imputation method refers to the attribution of the values of missing attributes by using the attribute values nearest to the missing ones. 

  • Mention some best techniques for data cleansing.

Some of the best techniques for data cleansing are –

  • Sorting of the data, which organises them based on their categories.
  • Focusing attention on the summary statistics for each column 
  • Getting mastery of regular expression
  • Creating a set of utility functions, tools, and scripts 
  • How is data mining different from data profiling?

Data mining focuses on identifying essential records, analysing data collections, discovering sequences, etc. 

Data profiling, on the other hand, is concerned with analysing individual attributes of the data and providing valuable information on those attributes such as data type, length etc.

  • What are data validation methods?

There are two ways to validate data:

  • Data verification – once the data has been gathered, a verification is done to check its accuracy and remove any inconsistency from it.
  • Data screening – inspection or screening of data is done to identify and remove errors from it (if any) before commencing the analysis of the data.
  • Name some common issues associated with a data analyst career.

Some common issues which data analysts face are Missing values, Miss-spelt words, Duplicate values and Illegal values.

  • What is an Outlier?

The term outlier refers to a value which appears far away and diverging from an overall pattern in a sample. 

  • What is logistic regression?

Logistic regression or logit regression is a statistical method of data examination where one or more independent values define an outcome.

  • Mention the various steps in an analytics project.

Various steps in an analytics project –

  • Definition of problem
  • Exploration of data
  • Preparation of data
  • Modelling
  • Validation of data
  • Implementation and tracking
  • What are the missing patterns generally observed in data analysis?

Some of the commonly observed missing patterns are –

  • Missing completely at random
  • Missing at random
  • Missing that depends on the unobserved input value
  • Missing that depends on the missing value itself
  • How can multi-source problems be dealt with?

One can deal with multi-source problems by –

  • Restructuring schemas for attaining schema integration
  • Identifying similar records and merging them together 
  • What are the ways to detect outliers? 

Outliers are detected using two methods. 

Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range).

Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).

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