Communicate, using a mix of visualizations, a coherent, well-sequenced summary of a data analysis to inform an audience of the key findings of that analysis. • Interpret and write a summary of the noteworthy patterns, trends and exceptions identified in a set of data. • Create simple line, bar and column charts to visually display data. • Select which chart types are relevant to each type of analytical inquiry. • Use data visualizations to compare and contrast aggregate measures between subsets of data, or over time, in order to identify patterns, trends and exceptions present in data. • Create a Jupyter Notebook project, and manage the files associated with that project. • Write Python code that loads data from storage, processing and transforming as necessary, to output summary tables and charts that meet a defined specification. • Write Python code to create estimated forecasts of future events. • Test written Python code to ensure it handles anomalies present in the data, such that the desired results are not distorted, skewed or otherwise contain confusing or misrepresented aspects.