Introduction to Data Science

Definition of Data Science

Data science is a field which is quite similar with data mining. It is a platform which is used to extract knowledge from available data and information using scientific methods and processes. Winner of Turing award Jim Gray supposed Data Science as a fourth paradigm of science and he asserted that everything is evolving because of positive impact of information technology on modern science. The term “Data Sciene” was initially introduced by Peter Naur in 1960. This concept was firstly published in “Concise Survey of Computer Methods” by Naur in 1974. In 1996, International Federation of Classification Societies (IFCS) allowed to study on this term which was firslty implemented in University of Michigan, United States.

Process of Data Science

Data science is one of the up growing trends in modern age technologies. Data Science consists of following steps

First step is to frame the problem. It is the starting step in this process in which problem is defined. It is necessary to be able to transfer data information in to something actionable. By ending of this stage it is imperative to confirm that you have collected all supporting data and information which is required to solve this problem.
Next step is to collect raw information which is mandatory. In this stage problem statement is optimized and further information is searched which is compulsory to resolve this problem in better way.
Analyzing the data information is also one of the most important steps of process of data science. At this stage data is analyzed. It is prime important to fix the errors and drawbacks of information which is collected. It is also important to change corrupted values and fake entries. Time zone differences are also taken in to the consideration to work with maximum pace.
Afterwards, data is explored. In this step, when your data is clean and to the point then working on data starts with intention to generate particular pattern in order to resolve the problem by inching way closer to solution.
In next step, Deep analysis of data is done. Different sciences technologies which include statistics and mathematics are applied to derive some sort of predictive models. Multiple science tools are also introduced with data.
Last step of this stage is communication of results of data analysis. In this stage irrelevant models and information are discarded. Similarly, with relevant predictive models, proposals are devised in which observation outcomes are discussed. After answering the follow-up questions by reports and compliance proposals solution is extracted.

Conclusion for Data Science

It is invention of modern age. No one can deny from the importance of data science. This is a platform which is very helpful in certain circumstances for instance, If you want to obtain a meaningful and more efficient result from bulk amount of data in less time by increasing the ratio of productivity then Data science is only the proper way to implement in this situation. It is also very advantageous to work in Data Science field because it is one of the most highly paid fields of science. Implication of Data Science in business is a key to boost up your business to the new levels of development and consistency.

Tags: Data Science Introduction, Introduction to Data Science, Process of Data Science