The success of a data science project depends upon several factors, the most important of them being a clear framework that guides the actions to be taken for the project and their order.
Here is a basic framework which should determine the handling of a data science project:
Translating the business needs into data science objectives.
It is the first step, and the foundation for employing further efforts. Take ample time to understand the needs of the business and the environment in which the data science model will be used.Then convert the needs into the project’s objectives. This step requires the business executive and the data scientist to come together and chart out a plan.
Describing and verifying data.
Data is messy. In order to make it workable, it has to be defined, explored and refined. The selection, cleaning, construction, and integration of data should take place at this juncture. Also get a thorough understanding of the moving parts and pieces involved. Even though the servers, environment and databases are not everything to a data science project, do this to avoid downtime or other pains later on.
Selecting models and assessing them.
This step involves selecting, fine-tuning, and putting together the best algorithms using techniques like model blending, model fitting, data reduction, feature selection, and so on. After finalizing the appropriate modeling techniques, a test design is generated, the model built and then assessed for effectiveness. Scientists often arrive directly to this step, overlooking the importance of the previous two, generally for reducing the time consumed or creating a quick model test.
Evaluating the deliverables, and making the decision to go on.
This may be the deciding stage of the project. Here, you need to evaluate the deliverables and see whether the objectives of the project are being met. It is imperative to drop the project, if at this stage the goals are not being met.
Planning for the deployment,maintenance and monitoring of the data science model created.
The last phase covers developing techniques for implementing the model, and making plans to monitor and maintain it so that the project enjoys a long tenure. The archiving of new learnings made through the development of the project should also be done at this stage so that knowledge is shared and mistakes are avoided in future projects.
Every step mentioned here has its own list of tasks. These tasks are essentially specific, and are generalized by a data scientist to meet the business needs. It is, however, true that there can always be unexpected hurdles lurking around. The framework of steps outlined here essentially gives an overall view at the surface level, which is necessary to keep in mind in order to ensure an efficient workflow. A set pattern tested by professionals is a better choice than any other alternative, when it comes to investing significant resources.
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