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Intro to Data Visualisation with Python : Create Comprehensive Plots with Matplotlib and Numpy free skillshare course

Know a bit of python ??.. This class is all you need to visualise data with Python. This class aims to teach the essentials required to visualise data with Matplotlib, a library for plotting in python. The class also covers NumPy in a brief manner, a scientific computing library that goes hand-in-hand with the visualisation capabilities provided by the Matplotlib. We'll also work with CSV files with pandas. You'll also be getting the essential coding practices required to create these types of programs.
To make the most out of this class, it is beneficial to have some prior programming experience ( preferably python ) but however, it's not necessary. An up-to-date system running Windows, MacOs or Linux with an installed version of python is recommended ( the instructor will provide the resources if python is not installed ).
 Intro to Data Visualisation with Python : Create Comprehensive Plots with Matplotlib and Numpy

By the end of this class, you'll be able to:-
1. Craft Line Plots, Coordinate Plotting ( 1st Programming Lecture )
2. Create a plot containing 2 subplots ( 2nd Programming Lecture )
3. Work with Bar Graphs ( 3rd Programming Lecture )
4. Build Pie Charts ( 4th Programming Lecture )
5. Program a Scatter Plot ( 5th Programming Lecture )
6. Read data from a CSV File ( 6th Programming Lecture )
7. Create a Gaussian Histogram ( 7th Programming Lecture )
8. Import and Process Images in a Plot ( 8th Programming Lecture )
9. Code a Polar Plot ( 9th Programming Lecture )
10. Add styles to the graph ( 10th Programming Lecture )
11. Set a 3D Plot Environment ( 11h Programming Lecture )
12. Create a 3D Scatter Plot and 3D Line Plot ( 12th Programming Lecture )
13. Plot Bars in 3D ( 13th Programming Lecture )
14. Program a 3D Wireframe and Surface Plot ( 14th Programming Lecture )
For any additional help you can refer the programming lectures and the lecture slides ( provided ), for further help you can shoot me an email at [email protected] or visit my blog at rohanbhasin.blogspot.com. You can also surface your queries in the community section.
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