Which of the Following Choices Is a Way That Big Data Science Can Be Applied?
There are several different ways to think about what options are available when you are trying to understand the relationships between big data science. It seems to me at least that one of these methods of analysis must be a better choice than the other. I am speaking from experience as an entrepreneur and data analyst who have worked in both sectors in many of their different forms. In fact, the only time that I have ever seen a Data Analyst goes completely off on a tangent and becomes an applied mathematician, it was due to the fact that he had chosen to focus on the option of statistics as his analytical methodology. I was surprised by this and yet it made sense since most Data Analysts were statistical scientists.
What should the analytical method be? Many people are very good at logical and problem solving techniques and computer programming. If you are looking at Data Science as an opportunity to apply scientific methodologies and language, then I would say this is a great place to start. The two programming languages that are typically required to perform the analysis of big data is R and Python. Of course, when you are talking about the big picture, you will need to use Sci-ometrics or some other type of metric as your measurement device.
The reason that I advocate going with the option of metrics over statistics is because the two languages make a very easy transition from using plain English text files to using scientific equations. The equation is much more meaningful and lends itself better to being able to differentiate between terms. In other words, you have less problems with interpretation and more issues with differentiation. Metrics on the other hand makes very easy sense out of big data Science.
So which of the following choices is a way that big data Science can be applied? There are three options. You can do traditional text mining. This entails combing through all of the publicly available text files, looking for keywords and phrases, then finding relevant articles in the scientific journals that pertain to the selected topic. Article databases are enormous and it is not difficult to find relevant articles covering your selected topic.
You can also go the route of big data analytics. With analytic tools, you can obtain a large amount of aggregated data sets in an extremely short period of time. You can then utilize the aggregated big data sets to perform several types of analytics, including trend detection, quality assessment, and product prediction. You have the potential to obtain highly granular data sets and there is really no end to what you can do with them.
If you prefer more hands-on capabilities and want to train and analyze your own predictive models, then you can consider building predictive big data sets from scratch. There are some excellent software packages out there that make this possible. Some of the software programs require no prior programming experience, while others are incredibly user friendly and simple to use. The software makes it possible to rapidly and easily generate predictive big data sets by taking the previous decisions about data sources, selection, and aggregation and running them through a recurrent loop. In many cases you can just customize the initial parameters and let the software take care of the rest.
Finally, some people have turned to running predictive big data sets on their own hardware. You can do this by purchasing or building hardware that is capable of storing and running large numbers of randomly chosen data sets. Such hardware is called a large memory computer. The benefit of using such a computer is that it allows you to run predictive big data sets quickly and without any human intervention. Of course, in much the same way as you would use a laptop for tasks other than writing to a database, you also need a laptop for running predictive big data sets. This will allow you to collaborate with co-workers and set up work groups to share and grow the data sets.
In short, the most compelling case for using big data sets in your predictive analytics is the ability to rapidly and easily obtain and manage large amounts of data. However, before you choose your hardware or choose a vendor for your hardware, you should make sure that you fully understand the potential usefulness of the information and the limitations of the data. Also, you must make sure that you have the appropriate information infrastructure in place before proceeding. Otherwise you may find yourself wishing you had chosen a different approach. Only when you have considered all these aspects should you begin to explore the possibilities of using big data sets in your predictive analytics. Hopefully you will be able to use this brief guide to help you determine which of the following choices is a way that big data science can be applied.