Watch Data Visualization in Python by Examples

Data Visualization in Python by Examples is an insightful course that teaches the fundamentals of creating visually striking graphics using Python. The show, available from Packt Publishing, is aimed at both novice and advanced learners who wish to become proficient in data visualization. The series covers the processes involved in creating dynamic graphs, charts, and maps, including data cleaning, transformation, and presentation.

The course aims to present the most up-to-date best practices in data visualization, which are aligned with current trends in data science. The show covers the principles involved in descriptive and exploratory data analysis, using Python's powerful packages and libraries such as Matplotlib, Pandas, and Seaborn. It also highlights the importance of choosing the most appropriate graphical techniques to effectively communicate the intended message to a diverse audience.

Data Visualization in Python by Examples is delivered through a series of engaging video lessons, which offer a practical, hands-on approach to learning. The show is divided into thirteen chapters and several sections, which are categorized according to specific topics. Each section is structured to provide a step-by-step guide, beginning with the installation of the necessary software and packages, to the final production of a polished visualization.

The course starts with an introduction to data visualization, explaining its advantages, and the basics of data science. The first section dives deep into the fundamentals of the Matplotlib package, which is instrumental in creating high-quality graphs for visualizing data. The instructor walks the students through the process of creating common types of plots such as line, scatter, and bar.

The second section covers the application of Python's data cleaning and transformation techniques to prepare data for visualization. The section covers topics such as data aggregation and filtering, data pivoting, and encoding. The section highlights how data can be transformed and visualized in different dimensions, including time-based and categorical data.

The third section discusses the application of the Pandas package in the creation of visually appealing tables, graphs, and charts. The section provides examples of how to explore, manipulate and clean datasets using Pandas. It also covers the techniques for creating and sharing interactive visualizations and dashboards, using the Plotly Python package.

The fourth section introduces the use of Seaborn, a powerful data visualization package that offers many customization options for creating complex visualizations. The section covers the creation of a range of Seaborn plots such as heatmap, pairplot, violin plot, and swarm plot.

The fifth section covers the creation of time-series plots using Python. The instructor explains how to handle time-based data and the basics of time series analysis. The chapter explores how to create graphs for analyzing time-series data using Matplotlib, Pandas, and Plotly.

The sixth section demonstrates how to create data-driven maps using Python. The section covers the use of packages such as Folium, GeoPandas, and Basemap in creating interactive maps that integrate data visualization techniques such as heat maps and bubble maps.

The seventh section analyzes the application of network visualization techniques in Python. The section covers topics such as graphs, directed graphs, and paths. It also demonstrates how to use networkx, a Python-based package, to create interactive network graphs.

The eighth section highlights the use of Data Science libraries in Python for efficient exploration and visualization of data. Students are walked through the process of creating charts and graphs using popular libraries such as SciPy, NumPy, and Scikit-learn.

The ninth section covers the creation of advanced plots using Python. The section delves deep into the customization of Matplotlib plots, creating subplots, and overlaying graphics. The section explores the creation of 3D plots and the use of animations in data visualization.

The tenth section introduces the use of Python's Machine Learning techniques for data visualization. The section covers topics such as supervised and unsupervised learning algorithms, clustering, dimensionality reduction, and time series analysis.

The eleventh section provides examples of creating web application visualizations. The section covers the use of Flask, a web framework written in Python, to create interactive data visualizations and dashboards.

The twelfth section covers creating visualizations for big data using Python. The section explores how to handle large datasets using distributed storage systems such as Hadoop, and how to create interactive visualizations on a big data scale.

The thirteenth and final section introduces the use of Python for data journalism. The section highlights the process of creating compelling data stories using interactive visualizations that communicate complex data in an accessible manner.

In summary, Data Visualization in Python by Examples is a comprehensive course that covers the A-Z of Python-based data visualization. The course presents the latest industry-relevant techniques and prepares learners to tackle complex data visualization challenges using Python.

Data Visualization in Python by Examples is a series that ran for 1 seasons (24 episodes) between February 27, 2018 and on Packt Publishing