4.1.1. Visualization Systems

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En provenance du cours de University of Illinois at Urbana-Champaign

Data Visualization

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University of Illinois at Urbana-Champaign

Data Visualization

689 notes

Cours 1 sur 6 dans Specialization Data Mining

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

À partir de la leçon

Week 4: The Visualization Dashboard

In this week's module, you will start to put together everything you've learned by designing your own visualization system for large datasets and dashboards. You'll create and interpret the visualization you created from your data set, and you'll also apply techniques from user-interface design to create an effective visualization system.

4.1.1. Visualization Systems3:20

4.1.2. The Information Visualization Mantra: Part 19:05

4.1.2. The Information Visualization Mantra: Part 29:07

4.1.2. The Information Visualization Mantra: Part 35:49

4.1.3. Database Visualization Part: 112:26

4.1.3. Database Visualization Part: 28:10

4.1.3. Database Visualization Part: 39:46

Rencontrer les enseignants

John C. Hart
Professor of Computer Science
Department of Computer Science

[SOUND] So in this module we're going to

be looking at visualization systems.

So before we do that, we should nail down some concepts.

And so there's going to be several concepts that we'll need to review and

introduce on these visualization systems that'll be helpful.

The first one we've seen before is information visualization.

We've used that as a term to describe the plotting of data

that uses different coordinates than what was provided.

In information visualization as opposed to scientific visualization or

other domain visualization where the data comes to you in a more concrete metaphor,

information visualization the information is much more abstract, and so

the challenge is going to be being able to take that abstract information and

find a concrete metaphor to communicate it to the viewer, so

it can be more easily understood.

The second is large data visualization.

We often hear of big data and there's also big data visualization.

The focus of big data or large data visualization is sometimes just on

the computation mechanics of handling large data sets.

But we're going to focus specifically on the visualization of large data, where

the data is so large that it's visually overwhelming if you display everything,

and so you have to be very selective about what you display so

that you can keep it mentally organized.

And so, we're going to depend on interactivity, the interactive aspect

of these visualization system to be able to, over time,

focus on certain elements of our visualization that we're interested in,

and to be able to ignore other elements so that these data sets,

which would be very large, don't overwhelm our cognitive understandings.

Third is visual analytics, visual analytics is an area that grew out of

business analytics and business intelligence, and

it's specifically on visualization, being able to look at data sets visually and

to be able to analyze them, draw conclusions from them,

and it focuses also on being able to make a decision based on data and

that decision is implied by the visualization, that the visualization

is giving you the insight in order to make a good decision based on your data.

And finally the goal of this week is to make a visualization dashboard.

We want to be able to organize the visualizations when we have multiple

visualizations and we have a lot of data.

We'll want to be able to produce a dashboard view that gives you everything

you need in order to be able to make a good decision based on the data.

So those are some of the terms,

some of the main concepts we use when we talk about visualization system.

We start with information visualization but our goal for

the week is going to be to understand how to make a visualization dashboard,

how to make a visualization system that we can use to make decisions

[MUSIC]

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