The comprehension of the lifecycle of data science is not an easy job. However, it is not very complicated either. An understanding of this lifecycle can turn the fortunes of startups around. There is a high probability that the lifecycle of data science varies with the requirements of a specific project. Notwithstanding this, the crux and core of almost all data science processes follow a unanimous model.
As data scientists tend to understand the sequence of basic data models, they become proficient to handle the intricacies of a project. That said, the entire lifecycle of data science is difficult to comprehend and operate if it is left to the discretion of data scientists alone. Other participants including policymakers and managers need to play their part as well. This is where data science certifications come in handy. Such certifications give the various stakeholders of a data science project a high level of familiarity with the overall lifecycle.
An overview of the life cycle
- The lifecycle of any data science project starts with the investigation of the research problem or research question.
- The second basic step involves the collection of data related to the research problem and its cleansing followed by data processing.
- In the third step, we try to formulate a minimum viable model. The minimum viable model is a preliminary model that needs to be modified and customized according to the project requirements.
- In the fourth step, the model is deployed and its basic functionalities are examined.
- The fifth step is called data science operation and is similar to DevOps. It is in the fifth step that we take the feedback of the deployed system and tend to improve the functioning of the overall model.
The problem statement is the basic step and involves the overall understanding of the problem. It takes into account the concerns of various stakeholders as well as the ethical questions that may be involved in the project. It is at this stage that the problem needs to be stated clearly and its commercial value needs to be underlined. Moreover, this stage also takes into account the various resources that need to be utilized during the course of the project.
Data Collection and cleaning
It is at this stage that data is collected and sent for the process of cleansing. This means that redundant data sets are done away with and the valid data sets are retained. The validity and reliability of data is also checked at this stage. This stage involves the aggregation of different data sets in the form of clusters so that they can be operated further. After this, the data is loaded into the target location and is processed further.
The preliminary model
The preliminary model is formulated so as to take a stock of the activities that we have accomplished. The preliminary model forms the basic framework that eventually culminates into the final model. This model is called preliminary because further additions and modifications are done after we obtain the feedback during the deployment phase.
The deployment phase
As the name signifies, the preliminary model is put into operation at this stage. The basic working of the model is examined. It is ensured that the model meets the requirements of the problem statement that was initially drafted. Various shortcomings of the project are checked at this stage. Finally, the model is set for the Data science Ops stage.
Data science Ops
In simple terms, this is the operational stage of the model in which we collect the feedback and use this information to better the functioning of the model. This is the final stage as it completes the working of the life cycle. This stage is also called the evaluation stage of the model as it allows us to examine if the final requirements of the project have been met or not.
Relevance to modern startups
Modern digital startups are conceived around novel problem statements that are yet to be explored. Different questions and problem statements form the architecture of the functioning of data-based startups. This is where the role of the data science lifecycle comes into play. By understanding the exact requirements of the problem statement and aligning this information with the data science lifecycle, various startups have achieved newfound success. Design thinking, predictive analytics, the development of novel data products, and marketing strategies are the successful outcomes of data-based startups that have successfully decoded the data science lifecycle.
Concluding remarks We may expect a large number of data based startups to blossom in the coming time which would be dominated by data processing, analytics and visualisation.