Interview Tips for Data Science

Interview Tips for Data Science

Data Science:

Data science is a multidisciplinary blend of data interface, algorithm development and technology in order to solve the analytical complex problem .The person who work on data science is called Data Scientist .It  is similar to knowledge Discovery in Data base .In this article we will discuss some interview tips for data science.


Various  Key Roles for Data science :

  • The Data Scientist :
  • The Data Analyst
  • Data Architect
  • Statistician
  • Data Base Administrator
  • Business Analyst
  • Data and Analytic Manager.


Key Skills for Data Science to Crack Interview Easily:

 Machine learning:

  Machine learning is the key concept in the data science.Machine learning is part of artificial intelligence.It  focuses on the development of computer programs that can change when exposed to new data.       

Coding languages such as PYTHON and R.

   The main coading language uses in  data science is PYTHON and R.Python is a high-level programming language that bills itself as powerful, fast, friendly, open and easy to learn.R coding language is opposit  to the python.These coding language are mainly used for Data analysis perspective.

Knowledge on Data bases such as MYSQL and POSTGRESQL.

languages which  used to handle  databases  in the data science are MYSQL and POSTGERSQL. MYSQL language  used for accessing , adding and  managing the data in the databases.It is an OPEN SOURCE  RELATIONAL DATA BASE MANAGEMENT. POSTGRESQL language  used for  strong reputation for reliabilty,data intergrity and correctness. It is an OPEN SOURCE object RELATIONAL DATA BASE MANAGEMENT.

Data Visualization and Reporting Technologies

These technologies mainly used for preparing  reports  . Data visualization technologies used to express the huge data with appropriate images and graphes. Reporting technologies used for preparing reports in  understandable way.

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Tips for the interview:

  • Allocate more time in your preparation on Mathematical statistics and Analytical tools.
  • prepare At least Two Business case studies..
  • For more importent questions and answers


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