What types of data presentation projects should you include in your portfolio?

1 Exploratory data analysis

Exploratory data analysis (EDA) is the process of examining and summarizing data sets using descriptive statistics and visualizations. EDA helps you understand the characteristics, distribution, and relationships of the data, as well as identify any outliers, errors, or patterns. EDA is often the first step in any data analysis project, and it can provide valuable insights for further research or modeling. Including EDA projects in your portfolio can show your ability to work with different types of data, apply appropriate methods and tools, and generate relevant questions and hypotheses.

2 Predictive modeling

Predictive modeling is the process of creating and testing models that can predict future outcomes or behaviors based on historical or current data. Predictive modeling can be used for various purposes, such as forecasting, classification, regression, or recommendation. Predictive modeling requires you to use various techniques, such as machine learning, statistics, or optimization, to select, train, and evaluate the best model for your data and problem. Including predictive modeling projects in your portfolio can show your ability to apply data science methods and algorithms, measure and improve model performance, and interpret and explain model results.

3 Data storytelling

Data storytelling is the process of transforming data into a narrative that conveys a clear and compelling message to a specific audience. Data storytelling can be used for various purposes, such as persuasion, education, or entertainment. Data storytelling requires you to use various elements, such as context, structure, visuals, and language, to craft a story that engages and influences your audience. Including data storytelling projects in your portfolio can show your ability to communicate data insights and recommendations, use effective data visualization techniques, and tailor your story to your audience and goal.