Every Data Analytics Student must watch this video...π₯ | Part 1 | @c4yourselfyt
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#data #dataanalytics #sources #nature
β³οΈ About the video
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In this video we will learn about Sources and nature of data for Data Analytics.
Sources and Nature of Data for Data Analysis
Data analysis is the process of examining, cleaning, transforming, and interpreting data to discover useful information, draw conclusions, and support decision-making. The efficacy of any data analysis largely hinges on the quality and relevance of the data being analyzed. Understanding the sources and nature of this data is crucial for both the robustness of the analysis and the validity of the conclusions.
Sources of Data
Data can be sourced from multiple avenues:
Primary Sources: Data collected directly from its source. Examples include surveys, interviews, observations, and experimental research.
Secondary Sources: Data that has already been collected and processed by someone else. Examples are books, journals, reports, and databases.
Tertiary Sources: Resources that index or compile primary and secondary sources, like bibliographies, directories, and databases like PubMed.
Internal Data: Information generated within an organization, including transaction records, customer databases, and internal reports.
External Data: Information from outside the organization, which can be governmental data, data purchased from third parties, social media data, and more.
Big Data: Typically sourced from the digital traces we leave on the internet, including web logs, social media posts, and other online activities.
Nature of Data Collection
Observational Data: Data captured without interfering or controlling the source. It's often used in studies where it's ethically or practically challenging to control variables.
Experimental Data: Data obtained from controlled experiments, where certain variables are manipulated to observe the effect on others.
In conclusion, understanding the sources and nature of data is foundational for data analysis. It dictates the choice of analytical tools, influences the robustness of the analytical process, and impacts the credibility of the results. Properly sourced and understood data can significantly improve the quality and reliability of insights derived from data analysis.
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#data #dataanalytics #sources #nature #datacollection #bigdata #observationaldata #experimentaldata #variables #analyzed #internaldata #externaldata #bigdata
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