Introduction To The Life Cycle Of Data Science

The Data Science Lifecycle is focused on the application of machine learning and various analytical methods to extract insights from data to achieve a company goal. The complete procedure includes several activities, such as data cleansing, preparation, modeling, and model evaluation. It is a time-consuming process that could take months to complete


The lifecycle of the data science process:


  1. Business Understanding 


The overall cycle revolves around the company's objectives. Considering that the study's ultimate goal is to fully understand the business objective, this is essential. For instance, You must determine if the consumer wishes to estimate the rate of a commodity or if he wants to minimize savings loss.

  1. Data comprehension

The following step is to obtain a better grasp of the data after gaining a better understanding of the company. Classifying the data, its structure, its significance, and the types of information it contains are all part of this process. Data can be explored via graphical charts. Basically, you can extract any facts about the information by simply viewing the data.


  1. Data Preparation

In this system, relevant data is selected, integrated by merging data sets, cleaned, handled by removing or imputing missing values, treated by removing incorrect data, and tested for outliers with box plots and dealt with. Constantly making data and obtaining new elements from old data.


  1. Exploratory Data Analysis 

This process involves getting a rough notion of the behavior and the factors that influence it before creating the true model. Then, the correlations between various features are represented using graphical representations such as scatter plots and warmth maps. Data distribution within various character variables is graphically explored using bar graphs.


  1. Modelling of Data

This stage is all about selecting the right model, whether the task is classification, regression, or clustering problem. Algorithms must be carefully chosen after deciding on the number of algorithms in a model family and on the model's family structure.

 

  1. Evaluation of the Model

The model was examined using a meticulously developed set of evaluation criteria and tested utilizing previously unreported data. Furthermore, we must ensure that the model is correct. If the evaluation does not give a satisfying result, the entire modeling method must be repeated until the necessary level of metrics is achieved.


  1. Model Deployment

After a thorough evaluation, the model is finally implemented in the structure and channel of your choice. The data science life cycle comes to an end with this step.

Each phase of the data science life cycle mentioned above must be carefully considered. If one phase is done incorrectly, it will influence the next stage, resulting in a loss of time and effort. If data isn't collected properly, you'll lose records and won't be able to create an ideal model. If the data are not sufficiently cleaned, the model will stop functioning. Finally, the model will fail in the real world if it is not accurately evaluated. From business understanding to model deployment, each stage requires careful consideration, time, and effort.


Are you curious about where you can acquire these data science skills? Check out the data science course in Hyderabad offered by Learnbay. Our IBM-certified data science courses will set you ahead of others! 

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