Les infos clés
Learn the fundamentals of data visualization and practice communicating with data. This course covers how to apply design principles, human perception, color theory, and effective storytelling to data visualization. If you present data to others, aspire to be an analyst or data scientist, or if you’d like to become more technical with visualization tools, then you can grow your skills with this course. The course does not cover exploratory approaches to discover insights about data. Instead, the course focuses on how to visually encode and present data to an audience once an insight has been found. This course is part of the Data Analyst Nanodegree.
Lesson 1a Visualization Fundamentals (2 hours)Learn about the elements of great data visualization. In this lesson, you will meet data visualization experts, learn about data visualization in the context of data science, and learn how to represent data values in visual form.
Lesson 1b D3 Building Blocks (4 hours)Learn how to use the open standards of the web to create graphical elements. You’ll learn how to select elements on the page, add SVG elements, and how to style SVG elements. Make use of all the Instructor Notes throughout this lesson if you have little to no experience with HTML and CSS.
Mini-Project 1: RAW Visualization (2 hours)Create a data visualization using a software of your choice. We will provide recommendations for visualization software as well as data sets. We want you to get right into making data visualization so here’s your first chance!
Lesson 2a Design Principles (2 hours)Which chart type should I use for my data? Which colors should I avoid when making graphics? How do I know if my graphic is effective? Investigate these questions, and learn about the World Cup data set which will be use throughout the rest of the course.
Mini-Project 2: Take Two (2-5 hours)Find an existing data visualization, critique it for what it does well and what it doesn’t do well, and finally, recreate the graphic using a software tool of your choice. We recommend using Dimple.js, which is covered in Lesson 2b, but we don’t want you to feel constrained by the choice of tools. Use any tool that works for you.
Special NoteAt this point in the course, you can start the final project. The remaining content of the course covers narrative structures, types of bias, and maps. All of the code in Lesson 3 and Lesson 4 pertains to d3.js. If you'd like to learn d3.js and complete the final project using d3.js, then please continue. If you prefer to stop, you can complete the final project using dimple.js.
Lesson 3 Narratives (5 hours)Learn how to incorporate different narrative structures into your visualizations and code along with Jonathan as you create a graphic for the World Cup data set. You’ll learn about different types of bias in the data visualization process and learn how to add context to your data visualizations. By the end of this lesson, you’ll have a solid foundation in D3.js.
Lesson 4 Animation and Interaction (5 hours)Static graphics are great, but interactive graphics can be even better. Learn how to leverage animation and interaction to bring more data insights to your audience. Code along with Jonathan once again as you learn how to create a bubble map for the World Cup data set.
Final Project: Making an Effective Data Visualization (2 hours or more)You will create a data visualization that conveys a clear message about a data set. You will use dimple.js or d3.js and collect feedback along the way to arrive at a polished product. ####NOTE: As a free student, you are welcome to complete the project to showcase your learning; however, only students enrolled in the Data Analyst Nanodegree are able to submit the final project for review and certificate. Interested in enrolling? Find out more!
- Jonathan Dinu - Jonathan is redefining data science education as the co-founder and CTO of Zipfian Academy -- a 12-week full time immersive data science program. He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley. In a former life, he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. At Zipfian Academy, he gets to combine his two favorite things: humans and code. When he is not working with students you can find him blogging about data, visualization, and education at hopelessoptimism.com
- Ryan Orban - Ryan is currently the CEO and Co-founder of Zipfian Academy, the leading provider of immersive training programs focused on data science and data engineering. Before Zipfian Academy, Ryan was a Sr. Systems Engineer at Nutanix, creating scale-out distributed computing solutions for virtualized environments. Ryan holds a bachelor’s degree in Molecular Cell Biology from UC Berkeley, where he plumbed the depths of plant immunity at the Plant Gene Expression Center, building big data applications targeting next-generation sequencing technologies. When not trying to pull beauty out of data, you can find Ryan hacking on 3D printers, DIY Bio, and open-source hardware.
Udacity est une entreprise fondé par Sebastian Thrun, David Stavens, et Mike Sokolsky offrant cours en ligne ouvert et massif.
Selon Thrun, l'origine du nom Udacity vient de la volonté de l'entreprise d'être "audacieux pour vous, l'étudiant ". Bien que Udacity se concentrait à l'origine sur une offre de cours universitaires, la plateforme se concentre désormais plus sur de formations destinés aux professionnels.