Thematic analysis is a type of qualitative data analysis method that takes large bodies of data and groups them according to their relative similarities, which can be termed as themes. Thematic analysis can be applied to various sorts of data set from secondary sources, like media to records of focus group meetings or interviews. Also, it tends to be used...
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Nowadays, data analysis becomes more and more popular in the world. Due to the development of modern technology, data analysis can be basically a comprehensive analysis of the company’s data, and it could achieve with no deviation. The future of data analysis and the reasons why we should learn data analysis become the most interesting questions in the society. Zimmermann...
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The keyword in data analysis is analysis, not the data. The mere volume of data does not make it significant. But the value we can extract and the questions that we can answer from the data is what makes the data significant. Suppose we have 300 MB of data but the answer we require can be answered from just few...
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2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on the Earth. The numbers are huge and such data cannot be examined without the analytical and visualization power. Why Data Analysis and Visualization Are Important...
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Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. Big data decodes previously untouched data to derive new insight that gets integrated into business operations. However, as the amounts of data increase exponential, the current techniques are becoming obsolete....
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In today's data-driven world, organizations across various industries heavily rely on accurate and reliable data to make informed decisions. However, ensuring data quality can be a complex process, involving numerous factors and considerations. The American Health Information Management Association (AHIMA) has developed a comprehensive Data Quality Management Model to guide organizations in effectively managing and improving data quality. In this...
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In a world increasingly driven by data, a simple spreadsheet first sparked my fascination. As a teenager, I remember being captivated by how a collection of numbers could tell a compelling story, influencing decisions in business, science, and even daily life. This early encounter laid the foundation for my aspiration to become a data scientist. This field, a blend of...
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This work is a reflection on my own experiences of collaborative data science work, using the DEBRIEF framework. It is adapted from my report initially submitted in March 2022. Describe events 'Six students, including me, worked together on the Group Project in the Data Science module. Most of us had never met before and we had not previously worked together....
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The insurance industry in general has been on an expansion mode with various players cropping up and transformations in regulatory framework and evolving technology pursuits. With the rise of big data and analytics, there has been immense cross-churning of information in both the life and non-life insurance sector. This has led to immense potential of positive opportunities in the insurance...
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Nowadays, in every movement, data is increasing in a high manner. The data is exponentially growing in the market. The data is not only growing but it has been generated, stored, analyzed and acquired also. The reason behind huge data is the use of the Internet. The Internet is not only used on the web, but it is exponentially growing...
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Data is a material gathering and storage agent, to evaluate the activities of the present and the future of an event or situation. Data is used by organizations or individuals, to comprehend and observe the activities of consumers. It is about gathering facts according to certain needs or characteristics by the individual or organization carrying out the information gathering. Data...
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