5 Constitutional Stages To explain Data Science Life Cycle

These days industries are highly dependent on vast amounts of raw and unstructured data available in their data repository for valuable insights. Data science is the emerging technology that provides powerful and efficient algorithms for handling massive amounts of data to stream meaningful insights. These insights help in steering decisions for improving the organisation’s revenue generation. Companies practice following a methodical approach towards solving data-based real-time problems. Several procedural steps together constitute the process of the Data Science Life Cycle.

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How many stages are there in the Data Science life cycle?

In order to explain the Data Science life cycle, let us look at its five constitutional stages:

1. ? Capture: The first step of the data science life cycle is gathering the raw structured and unstructured data. MySQL is a very useful tool for querying and reading databases. R and Python have special packages to read data from specific sources into data science methods.

2. ? Maintain: In this stage, the data is processed, filtered and converted into a usable format. Data cleansing, data staging, and data processing constitute the tasks of this stage.

3. ? Process: This stage of the data science cycle involves various tasks like data mining, data classification, data modelling, data summarisation etc. The cleaned and filtered data is processed to examine the patterns, ranges and biases to decide the usefulness of data in predictive analysis.

4. ? Analyse: Here the data is analysed through methods like predictive analysis, regression, qualitative analysis, etc. The methodical analysis uncovers the insights from the data.

5. ? Communicate: The data analysts perform the task of transforming the analysis into structured and readable forms such as charts, graphs and reports. Data reporting, data visualisation, and business intelligence are a few of the tasks in this stage.

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The life cycle of a Data scientist: Different roles of? a data scientist

A data scientist has different responsibilities and needs relevant skills for every stage of the Data Science life cycle.

1. ? Problem Definition:

As the first step in the data science life cycle, a data scientist needs to work along with the business team to recognize the problem well and define the scope of the project.

?2. ? Data collection? and preparation:

In this phase, the data scientist performs the tasks of gathering the data, cleansing it and assembling it for analysis. Duplicates, erroneous values and missing data are identified and converted into an appropriate format for analysis.

3. ? Exploratory data analysis:

In this stage, the data scientist explores the data to pin down the right patterns which would be fed into the model for the required predictions. Additional parameters are also identified which can help improve the accuracy of the model.

4. ? Model selection and training:

This forms the core of the data science life cycle. The data scientist identifies a suitable model for the problem which is trained using the prepared data. The model’s performance is assessed based on the results. It is iteratively optimized for better accuracy.

5. ? Model deployment:

The Data Scientist will deploy the trained model into the real-time environment and observe its performance. The model is re-trained to accommodate the evolving patterns.?

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Conclusion

The data science life cycle is an exhaustive step-wise process that churns the raw data into refined predictions. Each step of the life cycle needs to be performed with detailed precision. Following the procedures rightly produces reports which play a significant role in decision-making for any organization. Organisations can hugely benefit with considerable growth, with a well-structured data science process in place to follow.