![]() Phase five of the life cycle checks the results of the project to find whether it is a success or failure. The kind of environment needed for the execution of the model is decided and prepared so that if a more robust environment is required, it is accordingly applied. Also, the execution of the model, based on the planning made in the previous phase, is carried out. The next phase of the lifecycle is model building in which the team works on developing datasets for training and testing as well as for production purposes. The data prepared in the previous phase is further explored to understand the various features and their relationships and also perform feature selection for applying it to the model. At this stage, the various division of work among the team is decided to clearly define the workload among the team members. The third phase of the lifecycle is model planning, where the data analytics team makes proper planning of the methods to be adapted and the various workflow to be followed during the next phase of model building. The stakeholders involved during this phase are mostly involved in the preprocessing of data for preliminary results by using a standard sandbox platform. ![]() It includes huge CPUs, high capacity storage, and high I/O capacity.The IBM Netezza 1000 is one such data sandbox platform used by the IBM Company for handling data marts. A sandbox is a scalable platform commonly used by data scientists for data preprocessing. In the second phase after the data discovery phase, data is prepared by transforming it from a legacy system into a data analytics form by using the sandbox platform. Once all these assessments and evaluations are completed, the stakeholders start formulating the initial hypothesis for resolving all business challenges in terms of the current market scenario. The entire team makes an assessment of the in-house resources, the in-house infrastructure, total time involved, and technology requirements. In this first phase of data analytics, the stakeholders regularly perform the following tasks - examine the business trends, make case studies of similar data analytics, and study the domain of the business industry. Let us now briefly discuss all the six phases of the data analytics lifecycle followed in any data science projects: ![]() It is interesting to note that these six phases of data analytics can follow both forward and backward movement between each phase and are iterative. The six phases of the data analytics lifecycle that is followed one phase after another to complete one cycle. ![]() Hence to understand data science thoroughly, let us first try to understand the various phases in the data analytics lifecycle.ĭata analytics involves mainly six important phases that are carried out in a cycle - Data discovery, Data preparation, Planning of data models, the building of data models, communication of results, and operationalization. Data science is an umbrella term that comprises a large variety of fields compared to data analytics which is more focused and can be considered to be a subset of data science. While the terms data science and data analytics are often used interchangeably, the two terms are quite different based on the difference in the scope of their performances.
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