Life Cycles of Data Science

Phase 1

The first phase of the life cycle of data science is obviously the discovery of the principles of data science. Before beginning a project you must know the name and the requirements and also the correct and the appropriate budget to execute the project.  Effectively at this stage you need to formulate the first hypothesis of the project that you are going to work on.

Phase 2

The second phase of the life cycle of the data science includes the preparation of the data which includes gathering the right data and organizing it in the correct way. So you actually need to have something called analytical sandbox where you can do analytics during the project. Then you will move forward to what is called ETLT. It is an abbreviation which expands as extract, transform, load and transform. 

Phase 3

The Phase 3 of the life cycle of the data science is model planning. Here you have to carefully analyze the data and establish relationships between various variables. When you have the relationship between various variables you can easily form the base of your algorithm. This is what you will need for the next phase of the life cycle of the data science. There are various model planning tools which you must be familiar with before starting this phase. R is a language that has a full set of various modelling capabilities and using which we can build various interpretive models. Also, there are other tools to it like SQL Analysis Services using which you can use various data mining techniques to gather data and bring various disciplines of data science together and work on the project appropriately. Although there is no dearth of tools for data modelling, but the R language is the most commonly used one as it is easy to learn and not complex to use. 

Phase 4

As after the third phase you have planned the model, now it is the time to execute that model. So this phase is all about the building of a model by feeding the system with the databases and testing the techniques on that amount of data. It will help us know whether the current techniques are sufficient or we need a faster method to execute the same process. 

Phase 5

This phase is all about operationalizing. In this phase you actually deliver all the technical documents and codes and other reports are finalized. 

Phase 6

This is the most important phase of the life cycle of the data science. In this phase you actually check if you have been able to achieve your goal or your project is working up to the mark. It is all about communicating final results. All the goals that you had set in the first phase of this life cycle of data science are being achieved at the end of the fifth phase, then it means that you have successfully completed your project. 

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