Are you looking forward to learn powerful data visualization techniques to make your data more presentable and informative? If yes, then this Learning Path is for you. Packts Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Matplotlib is a multi-platform data visualization tool built upon the NumPy and SciPy frameworks. One of the most important features of Matplotlib is its ability to work well with many operating systems and graphics backends. Big data analytics are driving innovations in scientific research, digital marketing, policy-making, and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish. The highlights of this Learning Path are: Construct different types of plot such as lines and scatters, bar plots, and histograms Customize and represent data in 3D Create data visualizations on 2D and 3D charts in the form of bar charts, bubble charts, heat maps, histograms, scatter plots, stacked area charts, swarm plots, and much more Leverage the various aspects of data visualization and plots In this Learning Path, youll hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. Youll understand data dimensionality and set up an environment by beginning with basic plots. Youll enter into the exciting world of data visualization and plotting. You’ll work with line and scatter plots and construct bar plots and histograms. You’ll also explore images and contours in depth. Plot scaffolding is a very interesting topic wherein you’ll be taken through axes and figures to help you design excellent plots. You’ll learn how to control axes and ticks, and change fonts and colors. You’ll work on backend and transformations. Youll then explore the most important companions for Matplotlib, Pandas and Jupyter, used widely for data manipulation, analysis, and visualization. Youll acquire the basic knowledge on how to create and customize plots by Matplotlib. Further, youll learn how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. Youll learn to visualize geographical data on maps and implement interactive charts. Youll learn to create intuitive infographics. Youll explore 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will be also be explored. Finally, you’ll learn how to create interactive plots with the help of Jupyter. By the end of this Learning Path, you’ll be well versed with Matplotlib and construct advanced plots with additional customization techniques to perform advanced data visualization using the Matplotlib library. Meet Your Experts: We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: Benjamin Keller is a postdoctoral researcher in the MUSTANG group at Universitt Heidelberg’s Astronomisches Rechen-Institut. He obtained his PhD at McMaster University and got his BSc in Physics with a minor in Computer Science from the University of Calgary in 2011. His current research involves numerical modeling of the interstellar medium over cosmological timescales. As an undergraduate at the U of C, he worked with Dr. Jeroen Stil on stacking radio polarization to examine faint extragalactic sources. He also worked in the POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. At McMaster, he worked with Dr. James Wadsley in the Physics & Astronomy department. His current research was focused around understanding how the energy released from supernovae explosions regulated the flow of gas through galaxies, and how that gas is converted into stars. Aldrin Kay Yuen Yim is a PhD student in computational and system biology at Washington University School of Medicine. Before joining the university, his research primarily focused on big data analytics and bioinformatics, which led to multiple discoveries, including a novel major allergen class (designated as a Group 24th Major allergen by WHO/IUIS Allergen Nomenclature subcommittee) through a multi-omic approach analysis of dust mites (JACI 2015), as well as the identification of the salt-tolerance gene in soybeans through large-scale genomic analysis (Nat. Comm. 2014). He also loves to explore sci-fi ideas and put them into practice, such as the development of a DNA-based information storage system (iGEM 2010, Frontiers in Bioengineering and Biotechnology 2014). Aldrin’s current research interest focuses
