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Lifecycle of Data Science

  • Introduction to the Data Science Lifecycle

    The basic idea of the data science lifecycle is the use of machine learning and other analytical techniques to generate insights and predictions from data in order to meet business objectives. The complete procedure entails a number of processes, including data preparation, model building, model evaluation, etc. It is a protracted procedure that might take many months to finish. Therefore, it is crucial to establish a broad framework to adhere to for any challenge at hand.

     

    The lifecycle of data science

     

    1. Business Understanding

    The business aim is the focal point of the entire cycle. Without a specific issue to fix, what would you do? The business aim must be well understood because it will be the analysis's end result. After thorough comprehension, we are the only ones who can determine the precise purpose of the analysis in relation to the company's target. You need to understand whether the client wants to decrease credit loss, forecast the price of a commodity, etc.

     

    2.Data Understanding

     

    Data understanding comes next after business understanding. The gathering of all the accessible information is required for this. If you work closely with the business team, you may learn more about the data that is available, what data can be utilized to solve this business problem, and other details. The data must be described, together with their kind, relevancy, and or organization Visual charts are used to investigate the data. Basically, you can obtain information about the data by looking through it.

     

    3. Data Preparation

    The step of data preparation follows. This involves choosing the pertinent data, integrating it by joining the data sets, cleaning it, handling outliers, handling missing values by either deleting them or imputing them, and handling incorrect data by eliminating it. Creating new data and deriving new features from old. Format the data as needed, removing any unnecessary columns and features. The most time-consuming but possibly most significant stage of the whole life cycle is data preparation. As good as your data will be, so will your model.

     

    4. Exploratory Data Analysis

    Before creating the real model, this phase entails acquiring a general understanding of the answer and the variables impacting it. Bar-graphs are used to study the distribution of data within various feature variables, while scatter plots and heat maps are used to visualize the relationships between various characteristics. Several alternative data visualization approaches are frequently used to examine each component alone and in combination with other features.



    5. Data Modeling

     

    The core of data analysis is data modeling. The required output is produced by a model using the provided data as input. This phase entails selecting the right model type, depending on whether the issue is one of classification, regression, or clustering. Following the selection of the model family, we must carefully select and implement the algorithms to be used inside that family from among the many available options. To get each model to function as we want, we must adjust its hyperparameters. Additionally, we must make sure that performance and generalizability are properly balanced. The model shouldn't learn the data and then perform poorly with new data.

     

    6. Model Evaluation

    Here, the model is assessed to see if it is prepared for deployment. The model is tested on hypothetical data and assessed using a set of well considered assessment measures. Additionally, we must ensure that the model reflects reality. If the evaluation does not yield a satisfying outcome, we must repeat the modelingprocedure in its entirety until the necessary level of metrics is attained. Any data science solution or machine learning model should be able to adapt to new assessment metrics and change over time, much like a person. For a given phenomena, we can create several models, but many of them can be flawed. Phenomenon of models enables us to select and create the ideal model.

     

    7. Model Deployment

    The model is finally deployed in the desired format and channel following a thorough evaluation. The data science life cycle ends at this stage. The many phases of the data science life cycle should be carefully considered. The entire effort is wasted if any step is carried out incorrectly since it will have an impact on the following phase. For instance, improper data collection will result in information loss and a model that is not ideal. The model won't function effectively if the data is not adequately cleansed. The model will fall short in the actual world if it is not adequately examined. Each phase, from business comprehension through model deployment, should get the appropriate consideration, time, and effort.