Reference no: EM133230113
1. Data science and big data are two distinct areas that impact each other a great deal. Big data involves large amounts of information that is collected and maintained. Big data can be used to extract the most important information to use for trending of data sets. All kinds of businesses use big data from social media to healthcare. Data science on the other hand, is a scientific discipline, not just a technique. Data science involves collecting and analyzing data for use in various applications. Data science is a broad focus area. The activities from big data impact data science, as the information acquired through big data can then be used in areas of data science. The relationship between the groups will only expand into the future. Companies such as google, and Facebook collect huge volumes of big data to then use in advertising algorithms and other such ways to connect consumers to products. Healthcare has become interconnected in a way that allows for the acquisition of data to then be used to trend healthcare topics and provide continuity of care between healthcare companies (Wickramasinghe, 2021).
2. The importance of data science will exponentially grow as big data will grow indefinitely. The more Big Data evolves the more IT and data science positions will be necessary. It will continue to grow rapidly, and data is being collected and stored more and more every day. From our readings we learned that it's growing at 40% per year, that is more rapid than any other industry. Data is already so overwhelming we need more technology to process and analyze the data, which means more data scientists will need to be able to create the storage and applicational software to keep up with how fast big data is expanding.
3. I have an illustrative example of how the concept of big data and data science is used every day (on a nanoscale). We use software, apps, and programs at work, school, and home. I was thinking (metaphorically) that the protein, DNA, ligand, resin, and cells used in R&D are the big data. They continuously spit new data to collect and analyze. Then we put them in a machine (is to Google, etc.) to collect data, although some software and devices are innovative. For instance, in a microLiter of sample (big data) machine, the HPLC machine will read it and send it to the software (Google), which categorizes the data and provides graphs and pictures. Some software or programs try to analyze the data, but now everyone is looking for that specific generalized raw data that the software or program provided. So then that is when data science comes in, which is us (humans); we collect the information and then clean it (remove unwanted data). Then put the target data on a graph or table, analyze, translate the data to understand the purpose, and create deliverable. Although even after the developed method is accepted, data science continues the follow-ups and reports to maintain stability and consistency of the technique or for further development. I am not sure if that makes sense to anyone.
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