Explain Data Science Process with neat diagram

Data Science Process:

Data Science Process is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. It helps improve team collaboration and learning. It contains a distillation of the best practices and structures from Microsoft and others in the industry that facilitates the successful implementation of data science initiatives. The goal is to help companies fully realize the benefits of their analytics program.

We provide a generic description of the process that can be implemented with various tools. A more detailed description of the project tasks and roles involved in the process’s lifecycle is provided in additional linked topics.

7 Steps of Data Science Process:

The Data Science Process may involve 7 clear-cut steps for data analysis.

Step-1: Frame or define the business problem.

Step-2: Collect the raw data needed for your problem and map it to machine learning in case of Big data.

Step-3: Data preparation for processing the data for analysis.

Step-4: Explore the data.

Step-5: Perform in-depth analysis and produce prescriptive Business Insights.

Step-6: Evaluation

Step-7: Visualization and communication of results of the analyze.