T005x Course Info. I ranked every Intro to Data Science course on the internet, based on thousands of data points. A year ago, I dropped out of one of the best computer science programs in Canada.
I started creating my own data science master’s program using online resources. I realized that I could learn everything I needed through edX, Coursera, and Udacity instead. And I could learn it faster, more efficiently, and for a fraction of the cost. I’m almost finished now. Calling Bullshit. Teaching — Enrico Bertini. I have taught Information Visualization at NYU Tandon every year since 2012.
The course focuses on how to design, develop and evaluate interactive data visualization solutions for complex data analysis problems. This page links to material I developed for the course. Feel free to use it in your course or to study visualization on your own. Lecture Slides. StatGraphCourse < Main < Vanderbilt Biostatistics Wiki. SFU Statistics - Stat 201. Stat-201 - Statistics for the Life Sciences NOTE: Because of an "upgrade" to our webserver, all access controls were modified.
Unfortunately, SFU has decided not provide any resources to fix this problem. Consequently, these pages are no longer available to SFU students. Welcome to Stat-201. The goals of this course are to: Appreciate the ubiquitous role of variability in real life. Visual Storytelling.
CSE 5334:002 Data Mining - Home. CSE4334/5334 Data Mining. Harvard CS 109 - Data Science. Dataquest. Visualization - Courses · Stanford HCI Group. Updates as the year goes along.
HCI Program Sheets note Project courses often require applications. Factor that into your plans. (e.g., don't create a degree plan that only works if you get into a limited-enrollment course. Related courses outside cs The below courses are likely of interest to HCI students. Cs448b-fa16-wiki. Main Page - CS 294-10 Visualization Fa13. Chap3. CSE 591 - Visual Analytics. Visual Analytics is the science of analytical reasoning facilitated by interactive visual interfaces.
People use visual analytics tools and techniques to synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data, provide timely, defensible, and understandable assesments; and communicate assesment effectively for action. The overall goal is to detect the expected and discover the unexpected. Visual analytics is a multidisciplinary field that includes the following focus areas: Working knowledge of C/C++ In the final project you may choose among several topics related to the course content. Klaus Mueller's homepage. Medical imaging.
Here we use programmable commodity graphics hardware boards (GPUs) to accelerate a wide variety of 3D computer tomographic (CT) reconstruction algorithms. So far we have achieved speed-ups of 1-2 orders of magnitude, without significant loss in reconstruction quality. Evergreen Data Academy. Wannabe Data Scientist! Here are 8 free online courses to start… – Medium.
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Erik Demaine TAs: Tom Morgan, Justin Zhang [Home] [Lectures] [Assignments] [Project] [Problem Session] Data structures play a central role in modern computer science. Google Computer Science Education. Linkedin. LinkedIn Learning: Online Courses for Creative, Technology, Business Skills. Google Computer Science Education. Seminars · Stanford HCI Group - CS547 Spring 2016. Metis.
Data Science Courses. Information Design and Visualization. Overview “Understanding precedes action.” – Richard Saul Wurman, Distinguished Professor of the Practice in Information Design.
Data Visualization for Business. Accenture Labs: Data Insights. Dear Data: Business School Edition. Big Data for Smart Cities. Cities run on a stream of data.
In the smart city, the innovative use of data helps provide better and more inventive services to improve people’s lives and make the entire city run more smoothly. But the data our cities collect nowadays is more massive and varied, and is accessed at higher speeds than ever before. This is Big Data. New technologies are constantly being developed to better manage Big Data. This computer science course, from the IEEE Smart Cities initiative and the University of Trento, helps students understand and use these new technologies to help improve a city. Learn Data Analysis - Free Curriculum. Introduction The Data Analysis learning path provides a short but intensive introduction to the field of data analysis. The path is divided into three parts. In part 1, we learn general programming practices (software design, version control) and tools (python, sql, unix, and Git).
Springboard - Learn Data Science & UX Design online. Data Visualization with Python. TDWI Online Learning. Visual Literacy: An E-Learning Tutorial on Visualization for Communication, Engineering and Business. Error Bars Considered Harmful. About to teach Statistical Graphics and Visualization course at CMU. I’m pretty excited for tomorrow: I’ll begin teaching the Fall 2015 offering of 36-721, Statistical Graphics and Visualization. This is a half-semester course designed primarily for students in our MSP program (Masters in Statistical Practice). A large part of the focus will be on useful principles and frameworks: human visual perception, the Grammar of Graphics, graphic design and interaction design, and more current dataviz research.
As for tools, besides base R and ggplot2, I’ll introduce a bit of Tableau, D3.js, and Inkscape/Illustrator. For assessments, I’m trying a variant of “specs grading”, with a heavy use of rubrics, hoping to make my expectations clear and my TA’s grading easier. Classifier diagnostics from Cook & Swayne’s book My initial course materials are up on my department webpage. Statistical Graphics and Visualization course materials. I’ve just finished teaching the Fall 2015 session of 36-721, Statistical Graphics and Visualization.
Again, it is a half-semester course designed primarily for students in the MSP program (Masters of Statistical Practice) in the CMU statistics department. I’m pleased that we also had a large number of students from other departments taking this as an elective. For software we used mostly R (base graphics, ggplot2, and Shiny). But we also spent some time on Tableau, Inkscape, D3, and GGobi. We covered a LOT of ground. Tapestry 2016 materials: LOs and Rubrics for teaching Statistical Graphics and Visualization. Here are the poster and handout I’ll be presenting tomorrow at the 2016 Tapestry Conference. My poster covers the Learning Objectives that I used to design my dataviz course last fall, along with the grading approach and rubric categories that I used for assessment. The Learning Objectives were a bit unusual for a Statistics department course, emphasizing some topics we teach too rarely (like graphic design).
The “specs grading” approach seemed to be a success, both for student motivation and for the quality of their final projects. The handout is a two-sided single page summary of my detailed rubrics for each assignment. Information Visualization (Online MPS) Journalism in the Age of Data: A Video Report on Data Visualization by Geoff McGhee.