Python: Data Visualization with Python: 2-in-1
- 4.5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Learn the best ways to visualize data on the most interesting data sets to create your own plots
- Explore appropriate charts to describe your data and get to know the matplotlib’s techniques
- Use the most popular data visualization Python libraries to make 3D visualizations mainly using mplot3d
- Add impact to data analysis by visualizing the interpretation
- Manipulate and interact directly with data
- Manipulate and interact directly with data
Introduce ggplot and setup your computer for creating visualizing with it.
In scientific computing, images are often represented as NumPy array data structures. We can import images using various techniques. In this video, we will take a look at using image processing in Python, mainly related to scientific processing and less on the artistic side of image manipulation.
There are different plots used for representing data differently. In this video, we'll compare them and understand advanced concepts in data visualization. We would also plot sine and cosine plots and customize them.
There are different kinds of audiences to whom the data is presented. Having lines set up distinct enough for target audiences (for example, vivid colors for young audience) leaves a great impact on the viewer. This video shows how we can change various line properties such as styles, colors, or width.
If you have two variables and want to spot the correlation between those, a scatter plot may be the solution to spot patterns. This type of plot is also very useful as a start for more advanced visualizations of multidimensional data. Let's see how to create a scatter plot.
- Prior experience in Python programming is assumed
- Familiarity with the basics of data visualization will be useful
Effective visualization helps you get better insights from your data, make better and more informed business decisions. Python is a favorite tool for programmers and data scientists because it’s easy to learn, and the extensive list of built-in features and importable libraries contribute to increased productivity. To do this, we will focus on the following very popular libraries in Python: matplotlib, ggplot, seaborn, and plotly.
This comprehensive 2-in-1 course is a practical tutorial to help you determine different approaches to data visualization, and how to choose the most appropriate one for your needs. It will help you use data visualization as your preferred business reporting tool. Adds impact to your data by representing information in the form of a chart, diagram, pictures, and so on. This will also help you deploy plots and charts using various data visualization tools in Python.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Data Visualization in Python by Examples, covers Data visualization with matplotlib, ggplot, and seaborn in Python. In this course, you will walk through some of the fundamentals of data visualization, sharing many examples of how to handle different types of data and how best to present your insights. Finally, you’ll use Plotly to plot comparative graphs of Apple iPhone version releases and compare the performance of gaming consoles such as Xbox and PlayStation.
The second course, Python Data Visualization Solutions, covers creation of attractive visualizations using Python’s most popular libraries. This video starts by showing you how to set up matplotlib and other Python libraries that are required for most parts of the course, before moving on to discuss various widely used diagrams and charts such as Gantt Charts. As you’ll go through the course, you’ll get to know about various 3D diagrams and animations. As maps are irreplaceable to display geo-spatial data, this course will show you how to build them. In the last section, you’ll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python.
By the end of this training program you’ll be able to create effective visualizations for your data sets using tools: matplotlib, ggplot, seaborn and plotly in Python.About the Authors
- Harish Garg is a data scientist and a lead software developer with 17 years' software Industry experience. He worked for McAfee\Intel for 11+ years before starting his own software consultancy. He is an expert in creating Data visualizations using R, Python, and Web based visualization libraries.
- Dimitry is a data scientist with a background in applied mathematics and theoretical physics. After completing his physics undergraduate studies in ENS Lyon (France), he studied fluid mechanics at École Polytechnique in Paris where he obtained first Class class Master’s degree. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data-scientist for a smart-energy start-up in Cambridge, in close collaboration with the university.
- Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualisation in Python on his blog.
- Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
- Python users who wish to enter the field of data visualization or enhance their data visualization skills to become more effective visual communicators. The target audience includes business analysts; data analysts; web developers; product managers; program managers; decision makers
- Analyst or a budding data scientist who wants to know how to use Python to visualize your data to get effective insights from it, then this book is for you.