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Data Science Life Cycle

Every step in the lifecycle of a data science project depends on various data scientist skills and data science tools. The typical lifecycle of a data science project involves jumping back and forth among various interdependent data science tasks using a variety of tools, techniques<br>https://nareshit.com/data-science-online-training/

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Data Science Life Cycle

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  1. Data Science Life Cycle https://nareshit.com/data-science-online-training/

  2. Understanding Topic • Acquisition of Data • Preparation of Data • Exploring Data • Predictive modeling and Evaluation • Interpretation and Deployment https://nareshit.com/data-science-online-training/

  3. Understanding Topic Firstly, Data Scientist identifies the problem and analyzes the problem for solution. This is a decisive phase in which they also find if such a case happened in the past. • Acquisition Of Data • Data Acquisition is also called data discovery or data collection. In this acquisition, data is readily available for working or you will be collecting data required to a deal with the acquisition of data depends on its quality and processing. https://nareshit.com/data-science-online-training/

  4. Preparation Of Data Data Preparation is the most important step in this life cycle. It does not matter how you collected The data you must clean data and make it ready for analysis. During this stage data will be wobbling, so we will sometimes need to go back and collect the data required. Many data scientists say this preparation and cleaning of data consume 80% of time • Exploring Data • Data Exploration is also called Data Mining. This is a step where you start analyzing and understanding the patterns of the data prepared. You May Need to do additional cleaning of data while analyzing it. https://nareshit.com/data-science-online-training/

  5. Predictive Modeling and Evaluation In this, you try different combinations with your data to evaluate the outcomes. You will be noticing new things as you analyze your data set. Using separate validation sets of data to know how your model is performing. •  Interpretation and Deployment Once your prediction model is confirmed you outcome can interpret the data and results, finally, your model is deployed and can be used in real-time. https://nareshit.com/data-science-online-training/

  6. Thank You For More Information Click On 👉 👉 Data Science Online Training https://nareshit.com/data-science-online-training/

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