How do you simplify your data science project explanations?

2 Use a clear structure

A good way to simplify your data science project explanation is to use a clear and logical structure that guides your audience through your process and results. A common structure is the CRISP-DM framework, which stands for Cross-Industry Standard Process for Data Mining. It consists of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. You can use this framework to explain your project goals, data sources, data cleaning and transformation, model selection and tuning, model performance and validation, and model implementation and outcomes.

3 Use visuals and stories

Another tip to simplify your data science project explanation is to use visuals and stories to illustrate your points and engage your audience. Visuals, such as charts, graphs, tables, and diagrams, can help you show your data, model, and results in a more intuitive and appealing way. Stories, such as anecdotes, scenarios, or examples, can help you connect your data and model to real-world situations and problems. You can use visuals and stories to highlight the key insights, trends, patterns, and implications of your data science project.

4 Avoid jargon and acronyms

One of the biggest barriers to simplifying your data science project explanation is the use of jargon and acronyms that may confuse or alienate your audience. Jargon and acronyms are terms that are specific to a field or domain, such as machine learning, statistics, or programming. While they may be useful for communicating with other data scientists, they may not be understood or appreciated by non-technical audiences or potential employers. You should avoid using jargon and acronyms, or explain them in simple and relatable terms, when explaining your data science project.

5 Use analogies and metaphors

A final tip to simplify your data science project explanation is to use analogies and metaphors to make your data and model more accessible and memorable. Analogies and metaphors are comparisons that use familiar concepts or objects to explain unfamiliar or complex ones. For example, you can use an analogy of a recipe to explain a data pipeline, or a metaphor of a teacher and a student to explain a supervised learning model. You can use analogies and metaphors to help your audience grasp the main idea, logic, and function of your data and model.

6 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?