Data Science

Data science can be understood even to those who are new to it, since it does not require programming, math or even past experience to understand how it is done.  The taxonomy of data starts with getting more data, which involves things that can be measured such as numbers, temperatures, names and types. They are things that if they are changed in any way, they can lead to different results. Data are of different types such as names that can be turned into numbers and that appear in a defined order. The process of organizing and transforming data into a definite and useable form is referred to as data engineering (Rohrer, 2016).

After obtaining new data, it is important to ask a sharp question which must be answered using a number or name. To get answers that are in quantity form such as expected sales volume, the market share and products can only be answered through data analysis. The data that is used to explore the relationships between various factors such as how market share relates to sales is referred to as target data.  It is possible to have a question that one does not have target data for and in this case it is necessary to get more data that addresses that specific question.  After getting the data and asking a sharp question, the data has to be put in a table. The data in a table has to be aggregated, distributed, computed and then measured. Estimation can then be done to get a reasonable guess of a specific expected outcome such as stock price, market share and total users of a product (Rohrer, 2016). This process allows for easy analysis of the entire target data collected.

Reference

Rohrer, B., (2016).Data Science for Absolutely Everybody. Retrieved from: https://channel9.msdn.com/Events/Machine-Learning-and-Data-Sciences-Conference/Data-Science-Summit-2016/MSDSS02

Beneficial and Threatening Aspects of Computing As It Progresses Into the Future 

Automation that has been created and advanced by computing improvements has presented many opportunities for discovery. The human performance is associated with gradual growth while that of computers is growing rather rapidly. Computers are now being utilized in solving issues that would take more human effort and time to resolve. The advancement has presented undeniable benefits such as its utilization in the medical and learning field. It is through the development that humans have acquired more life’s superiority and stability given that they contribute to economic development while making diagnostics. It is apparent that computers have not achieved this growth on their own given that they are collaborating with humans but they are bound to outweigh human abilities in the future.

Given that computers capability is rapidly increasing it is clear that in the future the servicing industries will rely on them more as compared to human labor which will result in the loss of jobs. In that computers today can speak, hear, write as well as integrate knowledge which means that will the progress will reply the human efforts. Computers will and are already affecting interactions personally as well as professionally. In that most professions depends on the capabilities of computers in solving issues as well as creating ease. The general contribution of these computers is essential given that it has resulted in increased efficiency, speed, and quality. However, the fact that their abilities might outweigh those of humans is worrying on the ground that humans will no longer be required in accomplishing effort something that might affect individuals socio-economically. The advancing progress is essential but there must be a balance amid the two in order to save humans from destruction.

Reference

Howard, J. (2014). How Advanced Machine Learning Can Improve Our Lives. Retrieved from https://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-1173393

NoSQL database systems

NoSQL is a database system that is largely distributed and enables an organization to rapidly analyze and perform ad-hoc organization of data that is disparate and of high volume. The name came about as a way of showing that, depending on the needs of an organization; there are a variety of storage mechanisms that could be used when designing a product or software solution. Among the many reasons as to why an organization should consider NoSQL is the fact that JSON which is its key component is everywhere. When it comes to representing data in any domain, JSON is considered to be the de facto standard. This makes NoSQL a necessity for an organization as it is better that the traditional method of transforming data (Sadalage, 2014).

            Another reason is that NoSQL supports and enhances the flexibility of data. One can easily represent data sets that are complex and diverse without necessarily making them uniform. Even if documents of data differ, it will not cause any problem because JSON allows for the storage of events that differ without necessarily having to fix them or making them uniform (Sadalage, 2014). Rather than having to normalize data before using it, NoSQL allows for the use of data immediately it is created without the need of cleaning it first. Another reason why the organization ought to consider using the NoSQL database is that it makes the process of accessing and analyzing data easier and faster. While most organization had been previously focusing on the creating of large amounts of data, they have realized that this is not always wise because the data may at times prove to be of little, if any value. Choosing a NoSQL database system is thus advisable as it allows an organization to put the data to use quickly as opposed to storing it (Sadalage, 2014).

Reference

Sadalage P, (2014) “NoSQL databases: An overview” Data Science and Engineering