How do you generate insights and recommendations for a data science project?

1 Define the problem and the objective

Before you dive into the data, you need to have a clear understanding of the problem you are trying to solve and the objective you are trying to achieve. This will help you frame your analysis, select the relevant data sources, and define the metrics and criteria that will measure your success. You should also consider the stakeholders and users of your insights and recommendations, and how they will use them to make decisions or take actions.

2 Explore and clean the data

Once you have defined the problem and the objective, you need to explore and clean the data. This involves checking the quality, completeness, and consistency of the data, and dealing with any missing values, outliers, or errors. You should also perform some descriptive and exploratory analysis to get a sense of the distribution, trends, and patterns of the data, and identify any potential relationships or correlations between variables. You can use visualizations, summary statistics, and data profiling tools to help you with this step.

4 Interpret and communicate the results

The final step is to interpret and communicate the results of your analysis and modeling. This involves translating the technical and numerical outputs into meaningful and actionable insights and recommendations that address the problem and the objective. You should also provide the context, assumptions, limitations, and implications of your results, and explain how they can help the stakeholders and users to make decisions or take actions. You can use visualizations, narratives, dashboards, or reports to help you with this step.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?