Visualizing data is one of the most valuable skills every Data Scientist and Analyst should have. There are a lot of data visualizations to learn in data science. So if you are a beginner in data science and want to learn and practice data visualization using Python, this article is for you. In this article, I will take you through a list of tutorials on all the data visualizations in data science using Python.
All Data Visualizations in Data Science using Python
The matplotlib and plotly library in Python are the most used data visualization libraries by Data Science professionals. You will probably learn the most about matplotlib and plotly while learning about all the data visualizations mentioned below. The only prerequisites to get started with these data visualizations are the fundamentals of statistics and Python programming language.
If you want to learn the basics of statistics before learning data visualization using Python, I will recommend Statistics 101, which is one of the best books for learning statistics for data science. And if you want to learn the basics of Python, you can learn from here.
Below are all the data visualizations in data science with their implementation using Python:
- Bar plot
- Histogram and density plots
- Scatter plot
- Pie Chart
- Donut plot
- Box plot
- Time Series Graph
- Violin plot
- Plotting Annotations
- Word Cloud
- Visualizing a neural network architecture
- Visualizing a machine learning algorithm
- Candlestick chart
- Animated scatter plot
- Sunburst plot
- Choropleth map
- Visualizing a decision tree
- AUC and ROC Curve
- Most important data visualizations in data science
There are many more data visualizations that you can learn to visualize your data. I will keep updating the above list of visualizations regularly with more tutorials.
So this was a list of tutorials on all the data visualizations using Python that you should know. The above list will be updated with more tutorials regularly. I hope you liked this article on a list of tutorials on all data visualizations using Python. Feel free to ask valuable questions in the comments section below.