With so much data being continuously generated, developers with a knowledge of data analytics and data visualization are always in demand. Data Visualization with Python, shows you how to use Python with NumPy, Pandas, Matplotlib, and Seaborn to create impactful data visualizations with real world, public data. You'll begin the course with an introduction to data visualization and its importance. Then, you’ll learn about statistics by computing mean, median, and variance for the some numbers, and observing the difference in their values. You'll also learn about Numpy and Pandas, such as indexing, slicing, iterating, filtering, and grouping. Next, you’ll study different types of visualizations, compare them, and find out how to select a particular type of visualization using this comparison. You'll explore different plots, such as relation plots, distribution plots, and geo plots. Then, you'll move on to create custom plots with a dataset by choosing an appropriate library. After you get a hang of the various visualization libraries, you'll learn to work with Matplotlib and Seaborn to simplify the process of creating visualizations. You'll also be introduced to advanced visualization techniques, such as geoplots and interactive plots. You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful and insightful visualizations.You'll study how to plot geospatial data on a map using Choropleth plot, and study the basics of Bokeh, extend plots by adding widgets, and animate the information and the plot. The course will complete with one last activity in which you will be given a new dataset, and you’ll apply everything you’ve learned to create insightful visualizations
- Get an overview of various plots and their best use cases
- Work with different plotting libraries and get to know their strengths and weaknesses
- Learn how to create insightful visualizations
- Understand what makes a good visualization
- Improve your Python data wrangling skills
- Work with real world data
- Learn the industry standard tools
- Develop your general understanding of data formats and representations
For an optimal student experience, we recommend the following hardware configuration:
- Processor: Dual Core or better
- Memory: 4GB RAM
- Storage: 10GB available space
You’ll also need the following software installed in advance:
- OS: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later
- Browser: Google Chrome, Latest Version
- Conda
- Jupyterlab