Programme Content
Introduction to Visualization: Need to visualize data and how visualization can be an essential tool for exploring and communicating complicated information. Seven stages of data visualization and various types of charts like comparison, distribution, composition, and relationship. Exploratory and Explanatory analysis. Visual best practices: Edward Tufte’s visual encoding. Conversion of data into visualizations to draw valuable insights. Visualization of Numerical Data: Choosing right chart for the data on hand. Data analysis as a dashboard to provide narrative and communicate the results. Visualization of Text data: Visualization and the challenges of handling text data. How we use chart types such as word clouds, scatterplots, histograms, line charts etc. to visualize a document. Topic models, word embedding, and creating visualizations with bubble charts, bar charts, and t-SNE clusters. Visual Storytelling: – Why story telling matters and the science of storytelling. Various types of visual storytelling techniques and the pitfalls of traditional presentation methods. We will also learn about how businesses are adapting various presentation techniques like Pecha-Kucha, Presentation Zen to improve their communication among professionals and how these techniques allow them to weave a story around their presentation to make them more thoughtful, engaging, and interesting to the audience. Story telling framework: Types of narratives: author-driven narratives and reader-driven narratives. Seven different types of story types and how to create a narrative around a data science problem through visualization. Misleading with charts: how bad visualization can be misleading in decision making.
Programme Content Introduction to Visualization: Need to visualize data and how visualization can be an essential tool for exploring and communicating complicated information. Seven stages of data visualization and various types of charts like comparison, distribution, composition, and relationship. Exploratory and Explanatory analysis. Visual best practices: Edward Tufte’s visual encoding. Conversion of data into visualizations to draw valuable insights. Visualization of Numerical Data: Choosing right chart for the data on hand. Data analysis as a dashboard to provide narrative and communicate the results. Visualization of Text data: Visualization and the challenges of handling text data. How we use chart types such as word clouds, scatterplots, histograms, line charts etc. to visualize a document. Topic models, word embedding, and creating visualizations with bubble charts, bar charts, and t-SNE clusters. Visual Storytelling: – Why story telling matters and the science of storytelling. Various types of visual storytelling techniques and the pitfalls of traditional presentation methods. We will also learn about how businesses are adapting various presentation techniques like Pecha-Kucha, Presentation Zen to improve their communication among professionals and how these techniques allow them to weave a story around their presentation to make them more thoughtful, engaging, and interesting to the audience. Story telling framework: Types of narratives: author-driven narratives and reader-driven narratives. Seven different types of story types and how to create a narrative around a data science problem through visualization. Misleading with charts: how bad visualization can be misleading in decision making.
