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Mila Dymnikova Let me guess, your code is awesome but no one else gets it? You need a data visualisation! Complex concepts can easily be explained through visual information. There are four types to choose from storytelling, status, analytical and exploratory. I’ll help you find the perfect one. Mila is a data geek that loves data visualisations. Especially if the data visualisation is related to cats. Ruby is still her favourite language because of the community and how expressive it is. When she's not coding, you’ll find Mila cruising on an electric skateboard. #ruby #rubyconf #rubyconfau #programming
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The video is a presentation by Mila Dymnikova at RubyConf AU 2019, focusing on data visualization strategies. Mila discusses the importance of visual communication in programming and data science, emphasizing that complex concepts can be simplified through effective data visualization. She categorizes visual communication into four types: analytical, status, storytelling, and exploratory, each serving unique purposes in conveying information. Key points include: - **Importance of Data Visualization**: It enhances understanding of complex ideas for both technical and non-technical audiences, making it crucial for sharing work and gaining appreciation. - **Four Types of Visualizations**: - **Analytical Visualizations**: These include techniques like flow diagrams and UML to visually explain code processes, helping audiences understand system functionalities without overcomplicating diagrams. - **Status Visualizations**: Used for monitoring system health, these visualizations provide normality thresholds and alerts for anomalies, often through dashboards displaying multiple graphs, further simplifying data interpretation. - **Storytelling Visualizations**: These are infographics that present project journeys, emphasizing successes and challenges, making lengthy documents more engaging and accessible, and tools such as Canva are recommended. - **Exploratory Visualizations**: Designed for users to interact with the data, these allow flexible exploration of relationships within complex datasets, similar to a choose-your-own-adventure setup. D3.js is recommended for creating appealing exploratory visualizations while noting its complexity for simpler graphs. Important examples include using flow charts and dashboards to communicate status and analytical information efficiently. Mila stresses the significance of creating clear visuals and dashboards, ensuring that they maintain focus and communicate effective narratives without overwhelming the viewer. Concluding remarks highlight that each visualization type has overlapping aspects and encourage the use of data visualizations as a strategic communication tool to share achievements and insights effectively. Mila concludes by offering links to resources on her Twitter, emphasizing the need for effective visual storytelling in programming and data science.
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