As datasets get larger, it becomes more important to explore and visualize this data effectively before plunging into data analysis. Visualization libraries, such as Plotly, can help us process and retain all of this information in the best way.
Data needs to be parsed and intuitively understood before you can use it for modeling and extracting insights. In this course, Building Data Visualizations Using Plotly, you will learn how to use the Plotly Python API to build a wide range of basic, intermediate, and advanced visualizations and animations. You will start off by working with Plotly in the offline as well as the online mode. You will learn to share visualizations using the Plotly Cloud and how to generate these visuals embedded within a Jupyter notebook. Next, you will see how to build basic graphs such as line and bar charts, histograms, pie charts, scatter, and box plots. Finally, you will move on to more advanced visualizations - such as Gantt charts commonly used for project management, Sankey diagrams to monitor network flow, 3D visualizations, and animations. At the end of the course, you will be comfortable using the Plotly Python API to build complex and vivid visualizations using data from the real world.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview Hi, my name is Janani Ravi, and welcome to this course on Building Data Visualizations Using Plotly. A little about myself: I have a master's degree in electrical engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high- quality video content. In this course, you will learn how to use the Plotly Python API to build a wide range of basic, intermediate, and advanced visualizations and animations. We start off by working with Plotly in the offline, as well as the online mode. We learn to share visualizations using the Plotly cloud, and how to generate these visuals embedded within a Jupyter Notebook. We'll build basic graphs, such as line and bar charts, histograms, pie charts, scatter and box plots. We'll then see how we can visualize statistical, as well as financial data with Plotly. We'll work with bubble charts and maps, time series visualizations, candlestick charts, shapes, and funnel charts. We'll then move onto more advanced visualizations, such as Gantt charts, commonly used for project management; Sankey diagrams to monitor network flow, 3D visualizations, and animations. At the end of the course, you will be comfortable using the Plotly Python API to build complex and vivid visualizations using data from the real world.
Building Basic Charts with Plotly Hi, and welcome to this course on Building Data Visualizations Using Plotly. Now, Plotly as a whole refers to a company based in Montreal, Canada. It's a VC funded startup that works on very cool tools for data visualizations, and a suite of analytics products. When we use the term Plotly within this course, we refer to only the Plotly Python package, or the Python API used to build these visualizations. Plotly offerings spans business intelligence, open-source libraries, and even cloud platforms. Plotly offers reporting and charting libraries, integrations with slide decks, and a wide variety of consulting services as well. The term Plotly, also refers to Plotly's websites, plot. ly, and this is where you can set up your charts and visualizations so that you can collaborate on them with other members of your team. Plotly has been built with an emphasis on online collaboration of your visualizations. While you'll be briefly introduced to other offerings from Plotly, the scope of this course is on plotly. y; this is the Python API for visualization. In this first module, we start off with basic plots in Plotly, you'll see how high-quality the visualizations are, how easy it is to share online, and how intuitive it is to use the Python API.
Plotting Statistical and Financial Data with Plotly Welcome to this module where we'll see how we can use Plotly to work with graphs which represent statistical and financial data. Financial data especially is typically represented in the form of a time-series, and that we'll cover here as well. This module will cover a fair number of advanced charting functions and interesting visualizations, such as bubble charts and bubble maps. We'll also see how we can set up heat maps in Plotly. We'll work with a stock price dataset, and see how we can visualize and analyze time-series data. We'll see how we can build up these plots interactively, using sliders to zoom in on specific bits of information. Plotly offers a huge number of built-in visualizations, but it's possible that sometimes you need to draw things yourself. Plotly offers an easy and intuitive way to draw basic shapes; you can also draw complex shapes on your visualization using SVG. In this module, we'll see how we can use candlestick charts to represent stock information, and funnel charts, which we can use to analyze the drop off at different phases in any process.
Building Advanced Plots with Plotly Hi, and welcome to this module where we'll see how we can use Plotly to build advanced plots. Plotly offers built-in functionality that allows you to create very specialized visualizations, such as Gantt charts, which are very widely used in project management, and Sankey diagrams to depict the flow of people or information. In this module, we'll also be introduced to 3D plotting functionality that Plotly offers using scatter plots and surface charts. In addition to interactive visualizations, Plotly also allows you to animate your graphs and charts. In this module, we'll see how you can add animations to your plot in order to make it more eye-catching.