Data science is a branch of science that has acquired a lot of maturity and advancement in a short span of time. The rise of data science can be attributed to application of this science in different types of projects. The iterative process that we use for the completion of a data science project is called the data science lifecycle. Needless to mention, the data science life cycle is different for different projects. However, most of the steps that are used in the data science lifecycle are repeated. To get insight into these steps, it is important to understand the basics of data science in deep detail. For this purpose, data science institutes in Delhi can serve the best purpose. Data science institutes provide hands-on training related to different types of practical applications. In this way, students develop a holistic understanding of the entire life cycle of data science projects. In this article, we aim to understand the different steps that are involved in the lifecycle of an advanced data science project.
Problem statement as the first step
The clear understanding of the problem statement is the first step in the life cycle of the data science project. Identification of the problem statement enables us to plan the course of action that needs to be followed in the time to come. Different types of risk factors are identified at this stage. Ethical considerations are also taken into account at the first stage itself. It is at this stage that different resources are accessed to collect the necessary information. A proper research plan is demarcated and corresponding work is assigned to each and every team member. The potential value of the data science project is communicated to the team members. Furthermore, it is ensured that the project plan is flexible so that it can be altered in the future if the need arises.
Data cleansing as the second step
The second step of the data science lifecycle is the most important step. It is this step that establishes the reliability as well as the validity of data that has already been collected. In this way, this stage establishes the authenticity of the work that has been done in the first stage. The primary aim of this stage is to mine out structured data sets from the unstructured ones so that further analytics can be carried out with a lot of ease. It is at this stage that we arrive at different viewpoints from the data sets and workout on different ideas. After insights have been derived, it is time to present the end results before various types of stakeholders. Finally, with the help of data visualization techniques, the results are communicated in a hassle free manner to various stakeholders.
Product prototype as the third step
The third stage is all about developing the product prototype. This is also called the minimum viable model. It is at this stage that different types of machine learning algorithms are tested so that they can later be applied at a large scale. We can say that this stage provides the necessary documented evidence for the stage of product deployment.
Product deployment as the fourth step
The 4th stage is usually industry-oriented as it involves the deployment of the model that has been developed during the previous stage. If any enhancements need to be carried out in the model, they can be done at this stage. This is the stage of the data science life cycle where the product meets its final destination. This also means that the product can be extended to other applications and use cases depending upon its performance and viability.
Operations and feedback as the final step
It needs to be noted at this point in time that a feedback mechanism is critical in the life cycle of every data science model. The absence of a feedback mechanism can result in the stagnancy of the product itself and render it redundant with the change in demand of the market. In this way, the feedback mechanism serves two important purposes. Firstly, it allows us to make necessary modifications in the final product and ensure it in accordance with the needs of the customers. Secondly, it prevents the product from becoming redundant by allowing constant improvements and upgrades.
The way ahead
The modern data science life cycle models rely on precise data mining. In addition to data mining, the process of knowledge discovery is a very critical addition in the modern data science life cycle models. Such models are bound to witness further updates and improvements as data science becomes more commercialized and research oriented in the times to come.
