6 Best Matplotlib Online Tutorials For Beginners in 2024
Here are some great python courses for learning matplotlib. If you want to learn this library, then these are among the best matplotlib courses that you can take.
What is Matplotlib in Python?
Matplotlib is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. It allows users to embed Python-generated graphics into other applications using a variety of plug-ins. This library is built on NumPy and is part of the scientific computing stack.
Who created Matplotlib?
John D. Hunter created this Python library in 2002 as a patch to IPython to facilitate interactive MatLab-style plotting through Gnuplot from the IPython command line.
Top Matplotlib Courses List
- Python for Data Science and Machine Learning Bootcamp
- Applied Plotting, Charting & Data Representation in Python
- Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
- Python for Data Science
- Python for Data Visualization Online Class
- Building Data Visualizations Using Matplotlib
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Best Matplotlib Online Training, Tutorials, and Courses
1. Python for Data Science and Machine Learning Bootcamp
Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more.
The Python course will teach you how to analyze data, create beautiful visualizations, and utilize powerful machine learning algorithms using Python. You will use matplotlib and seaborn for data visualizations in conjunction with Plotty for interactive visualizations.
In this course, you will:
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
Designed for beginners and experienced developers alike, this course tackles the basics of Data Science and helps students jump to the next level. Additionally, it will help you learn how to program with Python, create amazing data visualizations, and use Machine Learning with Python.
- Course rating: 4.7 out of 5.0 (117,371 Ratings total)
- Duration: 25 hours
- Certificate: Certificate on completion
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2. Applied Plotting, Charting & Data Representation in Python
Offered by the University of Michigan.
The course introduces learners to basic information visualization concepts, including reporting and charting with matplotlib. Firstly, it will start with a discussion of design and information literacy, covering the components of a good and bad visualization and how statistical measures are translated into visualizations.
In this course, you will learn:
- what makes a good or bad visualization.
- best practices for creating basic charts.
- functions that are best for particular problems.
- how to create a visualization using matplotlib.
Secondly, we will introduce users to best practices for creating basic charts in Python and matplotlib and how to implement design decisions in the framework using the technology to make visualizations.
Thirdly, it will demonstrate basic statistical charts to help learners identify when one method is more suitable for a particular task and provide a tutorial of the functionality in matplotlib. Toward the end of the course, the course will explore other ways of structuring and visualizing data.
- Course rating: 4.5 out of 5.0 (5,998 Ratings total)
- Duration: 21 hours
- Certificate: Certificate on completion
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3. Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence.
Numpy forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. Therefore, first, we will start with a demo where we will prove that using a Numpy vectorized operation is faster than using a Python list.
In this course, you will:
- Understand supervised machine learning with live examples using Scikit-Learn.
- Comprehend and code using the Numpy stack.
- Use Numpy, Scipy, Matplotlib, and Pandas to execute numerical algorithms.
- Understand the pros and cons of various machine learning models like Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, etc.
Second, we will talk about Panda because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually. It makes working with datasets a lot like R if you’re familiar with it.
Having learned how to load data with Pandas, we will now look at the data. We will use Matplotlib to do that. In this, we’ll look at some common plots, namely the line chart, scatter plot, and histogram. Moreover, we will also look at how to show images using Matplotlib.
- Course rating: 4.6 out of 5.0 (20,163 Ratings total)
- Duration: 6 hours
- Certificate: Certificate on completion
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4. Python for Data Science
Learn to use powerful, open-source, Python tools, including Pandas, Git, and Matplotlib, to manipulate, analyze, and visualize complex datasets.
This course will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. You will learn how to use jupyter notebooks, Python, Pandas, Numpy, Matplotlib, Git, etc.
In this course, you will learn:
- Basic process of data science.
- Python and Jupyter notebooks.
- How to manipulate and analyze uncurated datasets.
- Basic statistical analysis and machine learning methods.
- How to effectively visualize results.
As you learn these tools, you can solve compelling data science problems. Upon completion of this course, you will be able to import data, explore it, analyze it, learn from it, visualize it, and generate easily sharable reports based on large datasets.
- Course rating: 310,311 total enrollments
- Duration: 16 hours
- Certificate: Certificate on completion
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5. Python for Data Visualization Online Class
Build accurate, engaging, and easy-to-generate data visualizations using the popular programming language Python.
To communicate their insights to non-technical peers, data scientists need to visualize their data. With Python, you can visualize data using several libraries designed specifically for the language.
Topics in this course include:
- Data Visualization Tools
- pandas
- Matplotlib
- Advanced Plotting
This course will teach you how to build accurate, engaging, and easy-to-generate charts and graphs using Python. Additionally, you will learn to explore the pandas and Matplotlib libraries and then understand how to load and clean data sets and create simple and advanced plots, including heatmaps, histograms, and subplots.
- Course rating: 4.7 out of 5.0 (691 Ratings total)
- Duration: 1 hour 21 minutes
- Certificate: Certificate on completion
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6. Building Data Visualizations Using Matplotlib
Make Matplotlib accessible and understandable to a Data Scientist or Business Analyst.
Matplotlib is one of the most popular visualization libraries used by data analysts and data scientists working in Python, but can often be intimidating to use. This course serves to make working with Matplotlib easy and simple.
Topics in this Matplotlib course include:
- Working with the Matplotlib and Pyplot APIs
- Building Basic, Intermediate, and Advanced Plots with Matplotlib
- Visualizing Statistical Data with Matplotlib
This course will introduce you to the basic components of a plot and show you how to tweak parameters and attributes to customize visualizations. Ultimately, you will learn how to customize the display, colors, and other attributes of these plots which will have multiple axes by learning the basic APIs available in Matplotlib.
- Course rating: 4.8 out of 5.0 (32 Ratings total)
- Duration: 2 hours 8 minutes
- Certificate: Certificate on completion
- View Course
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