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Data analytics has emerged as the latest hotshot for companies in recent times with huge opportunities arising in the industry every day. However, everyone has to undergo a stringent recruitment process in order to get a job with a company which may comprise of aptitude tests, GD sessions and then the final interview.
Here are some of the most asked interview questions one may encounter while sitting for a data analytics job.

What does a typical data analyst responsibilities consist of?

A typical data analyst’s job responsibilities involve gathering and organisation of the data, finding correlations between the analysed data with the data in possession of the company and then applying the knowledge accumulated from it to solve problems creatively.
Also Read : What are the Salary Trends in Data Analytics?

What is some essential requirement to be a data analyst?

A comprehensive knowledge of business-related tools along with the knowledge of statistics, mathematics and computer languages such as Java, SQL, C++, etc. are required in order to be a data analyst. One also needs the knowledge of data mining, pattern recognition and problem solving ability along with a good analytics training for the job.

What does the term “data cleansing” means?

Data cleansing refers to the process of detecting and removing any inconsistency or errors from the data in order to improve its quality. Data can be cleansed in many ways.

Name some best tools which can be used for data analysis.

Some of the most useful tools for data analysis are –

  • Google Search Operators
  • KNIME
  • Tableau
  • Solver
  • RapidMiner
  • Io
  • NodeXL

What is 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. Distance function is utilized to determine the similarity between two attribute values.

Mention some best techniques for data cleansing.

Some of the best techniques for data cleansing are –

  • Sorting of the data which organizes them on the basis of their categories.
  • Focusing attention on the summary statistics for each column that will help identify the most frequent problems.
  • Getting mastery of regular expression
  • Creating a set of utility functions, tools and scripts for handling everyday cleansing tasks.

What is the difference between data mining and data profiling?

Data mining focuses on identifying essential records and analysing data collections along with discovering sequence, etc. while data profiling, on the other hand, is concerned with the analysis of individual attributes of the data and providing valuable information on those attributes such as data type, length, frequency, etc.

What are data validation methods?

Data validation can be done in two manners –

  • 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 data analyst career.

Some common issues which data analysts face are –

  • Missing values
  • Miss-spelt words
  • Duplicate values
  • 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. There are two types of outlier namely univariate and multivariate depending on the number of variables used.

What is logistic regression?

Logistic regression or logit regression refers to a statistical method of data examination where one or more independent values are defining 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
  • Modeling
  • 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 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 containing all relevant attributes without redundancy

What is a hierarchical clustering algorithm?

Hierarchical clustering algorithm merges and divides existing groups creating a hierarchical structure in the process which showcases the order of the merging or division of the group.
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