Data Visualization Module 3 and 4

To make textual information more accessible, understandable, and meaningful.

To determine the emotional tone of a text.

It allows for emphasizing central phrases across multiple texts, grouping content by topic, sentiment, and more.

Our brains are wired to enjoy and make sense of visual data, and we sort through images quicker than written words.

It provides an effective outline of the products, features, and subjects that matter most to customers.

They allow for processing and visualizing text data.

By using text analysis and visualizing insights, it can spot inconsistencies and figure out the leading causes.

To make it more accessible, understandable, and meaningful.

DBMS deals with persistent relations, whereas DSMS deals with transient streams.

DBMS allows for random access, whereas DSMS requires sequential access.

Stream data is often at a fine granularity.

Queries in DSMS are often continuous and evaluated continuously as stream data arrives.

Stream data is often imprecise.

DSMS can handle possibly multi-GB arrival rates, whereas DBMS has a relatively low update rate.

Stream data processing requires main memory computations.

Queries are often complex and require multi-level/multi-dimensional processing and data mining.

Scatter plots are limited to two dimensions, which can make it difficult to visualize and analyze data with more than two variables. This limitation can lead to overplotting, making it challenging to distinguish individual data points.

Parallel coordinate plots differ from line charts in how they translate data into a plot. They are useful for comparing profiles because they enable the visualization of how multiple attributes are distributed across different categories, allowing for the identification of similarities and patterns.

The primary advantage of using scatter plots is that they enable the identification of outliers and patterns in the data. Scatter plots facilitate this advantage by visualizing the relationships between variables, making it easier to recognize clusters and anomalies.

Scatter plots are most suitable for continuous data, as they require a range of values to effectively visualize relationships. They are not suitable for discrete data because it does not provide a range of values, making it difficult to visualize relationships and patterns.

Parallel coordinate plots enable the comparison of different categories by visualizing the profiles of multiple attributes across different categories. This comparison can provide insights into similarities and differences between categories, as well as identify patterns and relationships that may not be immediately apparent.

The primary challenge of using scatter plots with a large number of data points is overplotting, which can make it difficult to distinguish individual data points. This challenge can be addressed by using techniques such as jittering, transparency, or aggregation to reduce the impact of overplotting.

Resource exhaustion

Sensor data (temperature, humidity, etc.)

Detecting fraudulent activities

Responding to security threats in real-time

Providing personalized recommendations

Detecting health anomalies and triggering alerts for immediate medical attention

Real-time processing of video and audio streams

Optimizing routes and addressing supply chain disruptions promptly

The primary goal is to grab attention and make a point in under five seconds, which can be achieved by using traditional graphs, clear labels, and intentional color use.

Using shades of the same color for comparisons, limiting the number of colors, and using colors related to the topic can help convey information effectively and avoid distraction.

Intentionally misrepresenting data, using uneven intervals, inaccurate scales, or inappropriate colors can lead to misinformation and damage credibility.

It can lead to discreditation and dishonesty, and undermine the validity of the data set and one's reputation.

Using more than six colors, crowded charts, and needing multiple text boxes to explain data points are signs of overload.

Using too many colors can be distracting and confusing, making it difficult to differentiate between data points.

Using clear labels, avoiding intentional misrepresentation, and presenting a limited amount of information can help create an effective and honest visualization.

Using colors with cultural associations that may be misleading or confusing for the audience can lead to misinterpretation and should be avoided.