What are the stages in the life cycle in data science?

Meaning of Data Science Life cycle

Data science, an interdisciplinary approach that seeks to replace the traditional methods of processing a plethora of collected and stored data for analysis to produce actionable insights have transformed businesses in recent years. It is interrelated to data mining and big data and is a part of artificial intelligence. Data science life cycle means all the stages of data until it is processed for analysis. It uses machine learning and several other analytical techniques to give insights from the stored data for the different purposes of an enterprise. 

There are seven different stages to the data science life cycle  Stages in the Data science Life cycle- 

Business Understanding

The first step in the data science life cycle is getting a clear understanding of the issue at hand is crucial for the success of a data science project. To do so documentation of the requirement of the enterprise and identifying the problem by exploring current situations is essential. 

Data Understanding

The available data will be analyzed under this head by identifying the data sources. Therefore, a data scientist with good experience in Relational Database Systems and Non-Relational databases and understanding Cloud-based systems and Cloud-based file storage is necessary. Identifying the sources of data to extract data in both quality and quantity, defining data governance and tracking data lineage are other important aspects under this head.

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Data Preparation

After data collection, the integration, annotation, preparation and processing of data are essential so that it can be used for learning and generalization. It should be started with a small statistically valid sample and iteratively improved by data preparation strategies and at the same time maintaining data integrity. 

Exploratory Data Analysis(EDA)-  It is a crucial part of the data science life cycle. It involves finding out relationships and correlations between variables using visual and statistical methods. To understand data it is important to identify patterns which can be done by using the right tools. The relationship between variables is shown using graphs and chats. 

Data Modeling 

It is referred to as the heart of the data science life cycle which uses organized data as input and gives the relevant output. We must carefully select the algorithms to put into effect and enforce after deciding on the model family and the number of algorithms within that family. To achieve the best results, we must fine-tune the hyperparameters of each model.

Model Evaluation

It is the second last step in the stages of the data science life cycle. After the model is trained it needs to be evaluated to determine the performance and accuracy to achieve the business goals.

Model Development 

It is the core of the Data Science life cycle. Algorithms, testing out various strategies and tools and techniques is the basis of model development. A machine learning algorithm is selected that is relevant to the identified problem and then trains the ML Model.

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