week 7

When we are creating, we should always remember that we are not only designers but also editors. If we can't get clear information to our users, I think we've failed. Data warehouse more represents a way of data management and use, and it is a complete set of theories, including interpretation of the theme, color selection, picture application. What is now called big data is more of an increase in data magnitude and an update in tools. There is no conflict, on the contrary, but a better combination. The first skill we need to master is to look critically at graphics. Read newspapers, magazines, and textbooks. Visit websites that display infographics and visualizations; And analyze whether they help you understand what's important.

The problem with visualizing data is so hard to criticize the interpretation of the data, which is critical to the meaning of the data. So I went and looked up the TED talk on visual data, and I thought it was conducive.

https://www.youtube.com/watch?v=5Zg-C8AAIGg

I am focusing on the charts on different websites this week because of project 1. I found out about Best Data Visualizations of 2019 the day before yesterday. One of them, called A Year in Graphic Detail, which posted on The Economist, attracted me. I think the color selection and layout can be a template for my project.

week 6

The most important things to make a visible data are intuitive, interactivity, and real-time. So the steps of the iterative process of visualizing the data are divided into data acquisition, then graph selection (presentation), and finally, graph drawing. After reading the article, I think the whole data visualization can be divided into two directions: the first is to focus on display, and the second is to focus on analysis.

The interface of the data visualization program is like a dashboard of a car. You can get the critical information you want at a glance —— from the amount of remaining fuel to the speed of the vehicle to the range of the car. In short, visual data is the ability to display all the critical information in one view. An excellent data visualization tool should have both of the following features. First of all, it looks cool. Second is a bright yet popular color. Too much white is dull, too many colors feel messy, so the interface should be appropriately balanced. For example, in the screenshot below, our first reaction was to notice the map in the upper right corner because of the color.

And here is an excellent website to teach you how to select a color. https://www.webdesignrankings.com/resources/lolcolors/

week 5

The significance of the scatter plot is when X and Y are collected: (x1, y1), (x2, y2),... and (xn, yn). Each pair of Numbers (xi, yi) is marked on the coordinate plane. From the scatter diagram, we can observe the overall pattern and correlation of data distribution.

The correlation between two data, X and Y, is observed from the scatter graph. When the value of one data is higher than the average, the amount of the other data tends to be higher than the average. When the amount of one data is lower than the norm, the value of the other data tends to be smaller than the average, then the data X and Y are said to be positively correlated.

The sample points will be inclined from the bottom left to the top right. If the value of one data is higher than the average, the value of the other data tends to be lower than the average. If the value of one data is lower than the norm, and the value of the other data tends to be higher than the average, the data X and Y are said to be negatively correlated. The sample points will be inclined from the top left to bottom right.

I think the scatter graph shows the direction, pattern, and intensity of the correlation between two data sets. The linear relationship is especially important because it is the purest form, but the light of the eye does not quickly determine the intensity of the correlation. If the dispersion diagram shows a strong linear correlation between two numerical data, a line can be drawn in the dispersion diagram to give an overview of the direct relationship. The least-square method is a way of finding such a line, which is called the optimal line or the regression line.

When we have a lot of scatter graphs, to make it easier to distinguish, we can use different colors to identify each picture. Using color to determine the graph has the advantage of giving a quick idea of the strength of each graph.

week 4

We've been using data visualization for a long time, and images and charts have proven to be an effective way to convey and teach new information. Studies have shown that 80% of people remember what they saw, but only 20% remember what they read! It can even pass on ideas and events to future generations. The development of technology has further improved the opportunities brought by data visualization.

Perhaps the most crucial benefit of using data visualization is that it helps people understand data faster. We can highlight a large amount of data in a chart, and people can quickly spot critical points. Besides, this ability to display large amounts of data is another advantage of data visualization. A graph may highlight several different issues, and people can form different opinions on the data. Chart naturally opens up new avenues for business. One might find something unexpected in the data.

Visual presentation of data improves the ability to interpret information. It's not easy to find connections from vast amounts of data and information, but graphs and charts can provide information in seconds. Easy to see, can offer the required information.

week 3

People are used to starting from the top left corner of a chart or slide. When we are working with tables is to bring the design into context and the data to the core. We can not compete with users' attention with heavy borders and shadows and data. Instead, use narrow limits or remove the edges to highlight the data. It displays all the data in the form of points on a rectangular coordinate system to show the degree of interaction between variables. The value of the variable determines the position of the point.

A scatter plot shows the data correlation. If there is no correlation between the variables, it will appear as discrete points distributed randomly on the scatter chart. If there is some correlation, most of the data points will be relatively dense and presented in a particular trend. 

What I think is scatter plots are more research-oriented charts, allowing us to discover the hidden relationships between variables and make important guidance for our decisions.What struck me most was that we were able to represent Scatter plots in 3D, and this is the 3D Scatter plot video I found on YouTube yesterday.https://www.youtube.com/watch?v=lusJoN63jFY