The rapid development of the Internet is accompanied by the generation of massive information, and behind the massive information is massive data. How to obtain valuable information from these massive data for people to study and work requires the use of big data mining and analysis technology. As a core part of big data technology, the importance of data visualization analysis is self-evident.
MATLAB, as a widely used scientific computing programming language, is an indispensable data analysis, mining and modeling tool for scientific researchers. Relying on MATLAB development tools, it aims to help students master basic MATLAB drawing and advanced drawing skills, 1D/2D/3D and high-dimensional data visualization methods, the use of Gramm drawing toolbox and the export of pictures that meet the requirements for publishing scientific papers.
Yu Lei (associate professor) is specially invited to use a combination of "theoretical explanation + case practice + hands-on practice + discussion and interaction" to shed light on the experience and skills needed to analyze MATLAB data visualization through a large number of specific cases.
expert
Yu Lei (associate professor) is mainly engaged in MATLAB programming, machine learning and data mining, data visualization and software development, physiological system modeling and simulation, and biomedical signal processing. He has rich practical application experience and is the editor-in-chief of "30 Cases of MATLAB Intelligent Algorithms" "Analysis" and "MATLAB Neural Network 43 Case Analysis" related works. Has published many high-level international academic research papers.
Target:
1. Master MATLAB basic drawing and advanced drawing methods and techniques
2. Master MATLAB one-dimensional data visualization methods and techniques (pie charts, stem-and-leaf charts, ladder charts, box plots, microline charts, stacked line charts, calendar heat maps, statistical histograms, envelope charts, etc.)
3. Master MATLAB two-dimensional data visualization methods and techniques (2D scatter plots, contour plots, regional distribution maps, maps, etc.)
4. Master MATLAB three-dimensional and high-dimensional data visualization methods and techniques (3D scatter plots, slice plots, parallel coordinate plots, tree plots, Andrew curve plots, etc.)
5. Master the usage methods and techniques of Gramm drawing toolbox (download and installation, case demonstration and explanation, etc.)
6. Master MATLAB image saving and export methods and techniques
7. Master various programming skills through practical training
8. Solve difficult problems faced by students in their actual work
Python scientific research data visualization
Over the past 20 years, as society has produced a massive increase in data, the need for data understanding, interpretation, and decision-making has also increased. The only constant is humanity, so our brains must learn to make sense of this ever-increasing amount of data. As the saying goes, “A picture is worth a thousand words”, as the quantity, scale and complexity of data continue to increase, excellent data visualization has become more and more important.
In recent years, the Python programming language has been favored by more and more scientific researchers and continues to win the championship in many programming language rankings. Help scientific researchers learn data visualization methods in the Python environment more systematically. Relying on Python development tools, it aims to help students master the basic knowledge of Python programming, as well as Matplotlib, Seaborn, Bokeh, Pyecharts, Plotly, Altair, NetworkX, Basemap, Geoplotlib, etc. Basic drawing and advanced drawing techniques of commonly used visualization libraries.
Using a combination of "theoretical explanation + case practice + hands-on practice + discussion and interaction", through a large number of specific cases, we will shed light on the experience and skills needed to analyze Python data visualization in a simple and easy-to-understand way.
Target:
1. Master the basic knowledge of Python programming (environment construction, basic syntax, process control, Numpy&Pandas and other commonly used module libraries, etc.)
2. Master Matplotlib’s basic graphics (line charts, bar charts, pie charts, bubble charts, histograms, box plots, scatter plots, etc.) and advanced graphics (3D charts, contour charts, cotton swab charts, dumbbell charts, Funnel charts, tree charts, waffle charts, etc.) drawing methods and techniques (beautification of graphic styles, graphic layout, etc.)
3. Master Seaborn graphics drawing methods and techniques (downloading and installation, basic graphics drawing, style and color management, multi-picture drawing, etc.)
4. Master Bokeh graphics drawing methods and techniques (downloading and installation, basic graphics drawing, data types and conversions, view properties, etc.)
5. Master Pyecharts graphics drawing methods and techniques (download and installation, basic knowledge of Pyecharts, drawing of commonly used graphics, drawing of combined graphics, etc.)
6. Master Plotly graphics drawing methods and techniques (download and installation, basic syntax, basic graphics drawing, etc.)
7. Master the usage methods and techniques of other visualization module libraries (interactive visualization library Altair, complex network visualization library NetworkX, map visualization library Basemap, geospatial data visualization library Geoplotlib, etc.)
8. Master various programming skills through practical training
9. Solve difficult problems faced by students in their actual work
