Bespoke Storytelling and Visualization Certificate for Hassan Hamdi
Day 1
Data Visualisation Techniques
● Discrete and continuous data
● Measures and categorical data
● Sources of data
● Combining datasets
● Chart types
● Plots
● Visualising time based data
● Visualising geographical data
● Word clouds
● Histograms
● Combining data features in groups and sets
● User filters
● Drilling through data
● Using colours, shapes and size
● Platforms for sharing dashboards and stories
Storytelling with Data
● The data foundation
● Understanding the audience
● The psychological power of storytelling
● Building a narrative
● Selecting visual representation
● Communicating the need for action
Day 2
Visualisation Problems and Pitfalls
● Bad data
● Poor design
● Using the wrong chart
● Using colour, shapes and size inappropriately
● Visual clutter Immersive & Interactive Visualisation
● Immersive visualisation use cases
● Differences between working in 2D and 3D
● Pros and cons of using 3D analytics
● Exploiting human senses
● Transitions between 3D environments
● Coping with 3D analytics interaction complexity
● Developing and evaluating immersive analytics scenarios
● Use of augmented and virtual reality with immersive analytics Workplace New Realities
● Interacting with data using natural language
● Interfacing with data using chatbots and virtual assistants
● Platforms for building natural language analytics applications
Day 3 Use UX on Dashboard and reports
● Understanding user needs
● Selecting types of dashboard
● Eliminating noise from user understanding
● Providing context
● Aesthetics
● Timely publication of dashboards and stories
● Latency, networks and hardware How to help decision maker to make their decision
● Understanding decision makers and their priorities
● Communication styles
● Gaining the confidence of decision makers
● Which visualisations to include and exclude
● Guiding users through dashboards and stories
● Enabling teams to view and discuss dashboards and stories
● Tying decisions outcomes to data Storytelling without deception
● Knowing your data sources
● How filters are applied
● Identifying manipulated data
● Prioritising features to communicate
● Selecting when and how to aggregate data
● Generalising non-heterogeneous population data
● Avoiding bias
● False causality
● Removing ambiguity