{"id":55801,"date":"2019-10-19T09:46:10","date_gmt":"2019-10-19T09:46:10","guid":{"rendered":"https:\/\/www.fita.in\/?p=55801"},"modified":"2025-10-11T12:37:24","modified_gmt":"2025-10-11T12:37:24","slug":"data-science-interview-questions-and-answers","status":"publish","type":"post","link":"https:\/\/www.fita.in\/data-science-interview-questions-and-answers\/","title":{"rendered":"Data Science Interview Questions and Answers"},"content":{"rendered":" \r\n

Career in Data Science<\/a> is one of the robust careers at present. Because of the increased importance of Data, the scope and demand for the Data Scientist have been growing tremendously over the years. Based on the report submitted by IBM’s predictions the demand for the Data Scientist role would rise to 28% by 2021. For equipping oneself to a Data Scientist position, one must have an idea about the various questions that are put forth to them in an Interview.<\/p>\r\n

This Data Science Interview Question blog is designed specifically to provide you with the frequently asked and various Data Science Interview Questions that are asked in an Interview. These Questions are useful for the freshers who aspire to begin a career in the Data Science<\/a> field. Also, the experienced candidates could brush up their knowledge in Data Science by referring to this blog before taking an Interview.<\/p>\r\n\r\n

\r\n
    \r\n \t
  1. What is Selection Bias?<\/strong><\/li>\r\n<\/ol>\r\n

    Selection bias is the type of error that occurs at the time when the researcher decides about who is going to be studied. This is generally related to research where the selection of participants is not taking place randomly. It is also known as selection effects. It occurs because of the distortion of the statistical analysis and results from the way of collecting the samples. Understanding and mitigating this bias is a critical skill for any aspiring analyst\u2014something you’ll learn in-depth during a top Data Science course in Trichy<\/a>. If we fail to take the selection bias into account, then the conclusions of the study would not be accurate.<\/p>\r\n\r\n

      \r\n \t
    1. Explain the features of Vectors?<\/strong><\/li>\r\n<\/ol>\r\n

      The feature of a vector is an n-dimensional vector with numerical features that primarily represent some objects. When it comes to Machine learning, the feature vectors are used for representing the symbolic or numeric characteristics, that are called features. The features of an object in mathematical term and that can be easily analyzed.<\/p>\r\n\r\n

        \r\n \t
      1. Elucidate Root Cause Analysis?<\/strong><\/li>\r\n<\/ol>\r\n

        It was initially developed to analyze industrial accidents. Currently, it is used widely in other areas. Root Cause Analysis is the problem-solving technique that is used for isolating the faults or root cause of the problem. A factor is called as a root cause when it is deducted from the problem-fault sequence. This averts the final undesirable event from recurring.<\/p>\r\n\r\n

          \r\n \t
        1. What are the types of Selection Bias?<\/strong><\/li>\r\n<\/ol>\r\n

          There are four types of Selection Bias and they are<\/p>\r\n\r\n