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Data Collection Interview Questions

  • September 29, 2020
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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Table of Content

  1. What is the 2nd step of CRISP-DM methodology?

    The second step of CRISP-DM methodology is to collect the data for processing a solution for the business problem that is defined in the 1st stage.

    In order to provide an optimized solution, the data needs to be churned using quantifiable techniques. The relevant data for the business problem can be stored in software (Databases)

    or it may be collected directly from the sources by conducting surveys or experiments.

  2. What does Primary Data sources mean?

    The data collected directly from the sources is analyzed for appropriate solutions. Primary Data can be obtained by conducting experiments to simulate the conditions, collect data by surveys, conduct interviews, focus groups, web pages, or social media or IoT devices data.

  3. What are the Secondary Data sources?

    The data generated by various applications, agents, or sources for business are collected and stored using secured software solutions called databases.

    The data which is readily available for Data Mining provides quick access to the data and is inexpensive. Though it also has a disadvantage in terms of having a lot of irrelevant data.

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