Business understanding

When we work with a data science project, this project usually has several phases. Each data science project begins with the business problem, or identifying the question. There are key tasks addressed in this first stage of the project:

• Define the goal or objective: identify the required business variable that needs to be predicted by the analysis
• Identify the data sources: find the required data that you will use for the data science project

When you work with stakeholders to identify the key questions and project goals, you should use sharp questions that will help you identify the required data for your analysis.

Based on the questions that you'll be trying to answer, you can also foresee the data science methods that you'll use to address such a question. Typical questions for data science projects would include the following:

• How much, or how many?
• Which category?
• Which group?
• Is this weird?
• Which option should be taken?

During the first phase of the project, you're also usually building the TDSP, which will consist of various personnel members, each specializing in a particular subject, which will be essential to the success of the project:

• A data scientist: A highly educated and skilled person who can solve complex data problems by employing deep expertise in scientific disciplines (mathematics, statistics, or computer science).
• A data professional: A skilled person who creates or maintains data systems, data solutions, or implements predictive modelling. These people will work in professions such as database administration, database development, or BI Development.
• A software developer: A person who designs and develops programming logic, and who can apply machine learning to integrate predictive functionality into applications.
• A project leader: A project leader manages the daily activities of individual data scientists and other project contributors on a specific data science project.