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DA - S14

Data Visualisation

TermDefinition
Heatmaps Best used for: Identifying "hot spots," trends, density, or areas of high/low concentration, regardless of arbitrary borders.
Temporal charts Line charts, area charts, and candlestick graphs. Used to show trends and cycles.
Comparison charts Bar charts (horizontal/vertical), grouped bars, and butterfly charts. Used to show differences between categories.
Colour theory Use colour to higlight intentional versus distracting (unintentional) content. It also helps with preparing material which is colour blindness friendly.
Edward Tufts - Data to Ink ratio Good graphics should include only data-ink. This is to avoid distracting the viewer's attention to irrelevant elements.
Static Reports Show a point in time. They are useful for documentation and where the narrative needs to be controlled
Interactive (Dashboards / BI Tools) Aid understanding through drill downs and providing current data for quickly actionable decisions.
A failed visualisation is one where. A Stakeholder has to ask, "What am I looking at?".
What is the goal of a visualisation? Visualisation helps to move from raw data to information to Insight, providing the opportunity for actionable knowledge.
What are Heatmaps more useful for? If the data is about concentration or volume regardless of borders (e.g., "Where are our customers geographically clustered?"), use a Heatmap
What is a Choropeth map? If the data is tied to a specific jurisdiction or territory (e.g., "What are our sales by state?"), use a Choropleth Map.
The impact of Natural Language Query (NLQ) Instead of dragging and dropping fields, analysts type, "Show me the correlation between regional rainfall and supply chain delays," and the tool generates the optimal chart instantly.
The new impact of ScrollyTelling A web-based technique where visualizations change or animate as the user scrolls down a page, guiding them through a logical narrative sequence.
Adaptive Design Visuals that automatically change their level of detail based on who is viewing them—showing high-level KPIs for an executive but expanding into granular tables for a technical manager.
Dashboards are becoming "living" documents. A document that is continuously updated and revised to reflect current information, processes, or requirements rather than remaining static after creation
Data Provenance The visual "audit trail" showing where the data came from and how it was cleaned.
Cognitive Load The amount of mental effort required to understand a chart (Lower is better).
What does Data Visualisation mean? Means to communicate your analysis with stakeholders
One Benefit of Visualistion Simplifying and displaying complex data in an understandable format to aid stakeholders with key decision making
A second benefit of Visualisation Highlighting key insights quickly so stakeholders who are short for time do not need to review all the data they can see they key information
A third benefit of Visualisation Well designed and displayed visuals will ensure that stakeholders do not misunderstand or misinterpret the data
Some of the software tools available Excel, Power BI, Python
The benefits of Excel Widely used and understood, stakeholder familiarity; good for quick analysis
Limitations of Excel Very limited within visuals; Weak for very large data set handling; Simple visuals can be less engaging.
Key uses of Excel ideal for quick and ad-hoc reporting; Non-technical stakeholders will be familiar and likely more comfortable with it.
The benefits of Power BI Strong interactive dashboards with features such as slives; Real time unpdateable dashboards.
Limitations of Power BI More time consuming to set and maintain than excel; Visuals take a higher skill level to produce key insights.
Key uses of Power BI Creating dashboards; Useful for stakeholders who require high level insights with interactive features.
The benefits of Python Highly flexible and limitless visuals; Strong handling of data sets
Limitations of Python Requires strong coding skills to create visuals; Less accessible for non-technical stakeholders.
Key uses of Python High level analysis and visuals with large data sets.
Some of the approaches to visualisations Scatter plot charts, Pie charts, Data Tables, Bar charts, Line charts
 



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