This article covers every phase of the data science project lifecycle from beginning to conclusion

When discussing data science initiatives, it might not be easy to understand how the entire process works, from data collection to data analysis and results.

1. Obtaining Data: The initial stage in every data science effort is relatively straightforward: collect and get the necessary data. You will be unable to process anything if you do not have any data at all. You will need to query databases at this phase, requiring technical knowledge such as MySQL to handle the data. You may obtain data in simple forms such as Microsoft Excel and then go to more complex formats later.

2. Scrubbing Data: The primary purpose of the scrubbing data process is to clean and filter the data. Remember the trash in, garbage out principle, which asserts that the analytic results are useless if the data used is unfiltered and irrelevant.

3. Exploring Data: You must study your data once it is ready before diving into AI and Machine Learning. In a corporate or commercial setting, your employers usually feed you data and expect you to understand it. Thus, it will be up to you to assist them in determining the business question and transforming it into a data science inquiry.

4. Modeling Data: One of the first steps in data modeling is to lower the dimensionality of your data source. Not all of your attributes or values are required for your model to forecast. As a result, you must select the ones that will contribute to predicting the desired results.

5. Interpreting Data: We have arrived at the data science project's final and most essential stage: data interpretation. Data interpretation refers to the presentation of your data, providing the results so that they can answer the business questions you posed when you initially started the project, together with the actionable insights discovered through data science.