Common / Useful Data Transformations - Part 1

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來自 New York University Tandon School of Engineering 的課程

Information Visualization: Foundations

34 個評分

New York University Tandon School of Engineering

Information Visualization: Foundations

34 個評分

課程 1（共 4 門，Specialization Information Visualization ）

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on information visualization and to design and develop advanced applications for visual data analysis. This course aims at introducing fundamental knowledge for information visualization. The main goal is to provide the students with the necessary “vocabulary” to describe visualizations in a way that helps them reason about what designs are appropriate for a given problem. This module also gives a broad overview of the field of visualization, introducing its goals, methods and applications. A learner with some or no previous knowledge in Information Visualization will get a sense of what visualization is, what it is for and in how many different situations it can be applied; will practice to describe data in a way that is useful for visualization design; will familiarize with fundamental charts to talk about the concept of visual encoding and decoding.

從本節課中

Fundamental Graphs and Data Transformation

Overview: Fundamental Graphs and Data Transformation1:48

How to Visualize?3:23

Fundamental Graphs9:10

Alternate Representations Part 111:34

Alternate Representations Part 26:05

Going Beyond Two Attributes6:41

Scatter Plots + Faceting4:51

Data Transformation6:34

Common / Useful Data Transformations - Part 14:20

Common / Useful Data Transformations - Part 25:46

與講師見面

Enrico Bertini
Associate Professor
Computer Science and Engineering
Cristian Felix
PhD. Candidate
Computer Science and Engineering

Even though aggregation is one of the main types of transformations that

happen literally all the time in a project,

there are many other common other common and useful transformations.

Here I am going to show you some of the most common and useful ones.

The first one is transformations related to

attributes that encode information on time and date.

Very often in a project, you are confronted with the problem of creating

a visual representation of a dataset that contains some time-oriented information.

So time and dates or time and dates at the same time.

So in this case, it's very common to have aggregation at different levels

according to at what level of details you want to conduct your analysis.

So times and

dates have these interesting properties that they have a hierarchical structure.

So you can go from seconds to minutes to hours, days,

weeks, months, years, and so on.

You can imagine this as being different resolutions,

you can conduct your analysis at different levels of resolution.

And you can imagine that this also has an impact on the way

this information is visualized.

Let me give you an example, here once again, I'm using a bar chart

to show a trend over time, something that changes over time.

But again, according to the different resolution you choose,

you can spot different trends.

So you can go for months to weeks to days and as you can see, there are different

patterns that emerge according to the resolution that you choose.

There's never one single resolution that is optimal.

It depends on the specific question that you have and

on the specific project you’re working on.

So it’s very important to keep in mind when you have time and

deeds to look at what is the impact of using different resolutions

which ultimately translating to different aggregation levels.

The same thing happens when you are analyzing and

visualizing spacial data or geographical data.

For instance, you can go from analysis at the zip code level,

in a city, to a county, to the state, and so on.

And in general, every time you have spacial data,

you have the option of conducting your analysis and

creating visual presentations at different levels of resolution.

Here is an example, I have a map where the quantities are mapped

at the level of state, all the states in the United States.

But I can represent exactly the same information at the county or

zip code level.

So going from here to here what changes is the resolution.

Another very common type of transformation that happens when you have spatial data or

more precisely geographical data,

is going from the name of a location to the geographical coordinates.

Or the other way around,

going from the geographical coordinates to the name of this location.

This is very, very common and is called typically geo-coding and

decoding, very common.

For instance here, I have a dataset with different cities in the United States.

And for every city, I have one pair of values.

One for the latitude and one for the longitude.

And as you can imagine, this is very useful when you have to

create a map in every single location you want to visualize

some quantities or attributes coming from your dataset.

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