Week 3: Visualization of Non-Numerical Data

Understand the general concepts of data mining, and basic methods and applications. Next, I will examine the data mining subdomain, pattern discovery. Learn the detailed concepts, methods, and applications of pattern detection in data mining. We also introduce some pattern-based classification methods and some interesting applications of pattern discovery. In this course, you will practice scalable pattern discovery methods for large transaction data, master skills and content to participate, discuss pattern evaluation measurements, and minify various patterns, sequential patterns, and subgraph patterns Provide opportunities to explore ways.

In this week's module, you will learn how to visualize graphs that show relationships between data items. It is also possible to draw data using coordinates not specifically provided by the data set.

PS: Currently, I am making a video for DATA based on this article. Video production takes about three weeks. Our core designers who graduated from two professional video producers and visual arts schools (SVA) focused on visualizing the data story and providing clear and reliable videos to our investors and audiences I will.

Data story focuses on data visualization rather than data science, but there are often interesting duplicates between topics. Biweekly, Enrico Bertini and Moritz Stefaner will talk to guests about various data topics. Recent episodes on data ethics and data viewing from the universe are particularly interesting.

LiveEdu Data Science topics include data reasoning, algorithm development, and techniques to solve complex problems. In this topic, there are subcategories of data visualization, data mining, data analysis, text processing, Wolfram, data warehouse, and big data.

I have worked on data visualization projects, published papers on data visualization, and developed data visualization software. But to be honest, I am done. Subjectivity and uncertainty are too high - the visualization of data itself is unreliable, not scientific and ultimately insightful insight. It works sometimes, but not as good as other methods (see below). Some may argue that the information in the text is from a wider range of sources than visualization, or that the text may be displayed at various points in the visualization itself. It is fair. But the truth still remains - in the Quartz article text is getting better in changing information into human brains.

In this huge dataset, we processed a total of nine dimensions. For people with less than average average intelligence like me, it is difficult to visualize the data in 9 dimensions. Hell, 3D is enough for me. With PCA you can reduce the dimensions needed to visualize the data. For those who have never received a linear algebra or statistics course, this may be something silly for you. That is, PCA can exclude large data sets and convert them to 3D graphics based on what is called covariance matrix. This allows you to visualize the data and see if any patterns are occurring.