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Google Computer Science Education

Google Computer Science Education

https://www.google.com/edu/cs/index.html

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Advanced Data Structures (6.851) Prof. Erik Demaine TAs: Tom Morgan, Justin Zhang [Home] [Lectures] [Assignments] [Project] [Problem Session] Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). Online Learning Online Courses on Business Intelligence and Analytics from TDWI Build your business intelligence and analytic skills with in-depth and interactive online courses taught by industry experts and in-the-trenches practitioners. Each course offers an engaging on-demand learning experience featuring video, audio, quizzes, exams, and more. 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. Our RapidCT system enables fast, accelerated 3D reconstruction in diagnostic imaging, radio-therapy applications, surgery planning, electron microscopy, and others, at a fraction of the cost of proprietary devices. Related projects are the iterative 3D reconstruction from data acquired with X-ray CT scanners at low radiation doses (known as low-dose CT), with transmission ultrasound for breast mammography, data obtained from MV-CT and proton-CT scanners for the treatment of cancer, projections obtained via mobile X-ray source/detector pairs, as well as functional imaging applications, including MRI, functional MRI, SPECT, and PET.

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.

Courses · Stanford HCI Group Updates as the year goes along. HCI Program Sheets note CSE4334/5334 Data Mining Course Description: This is an introductory course on data mining. Data Mining refers to the process of automatic discovery of patterns and knowledge from large data repositories, including databases, data warehouses, Web, document collections, and data streams. We will study the basic topics of data mining, including data preprocessing, data warehousing and OLAP, data cube, frequent pattern and association rule mining, correlation analysis, classification and prediction, and clustering, as well as advanced topics covering the techniques and applications of data mining in Web, text, big data, social networks, and computational journalism. Student Learning Outcomes: A solid understanding of the basic concepts, principles, and techniques in data mining; an ability to analyze real-world applications, to model data mining problems, and to assess different solutions; an ability to design, implement, and evaluate data mining software.

Information Design and Visualization Overview “Understanding precedes action.” – Richard Saul Wurman, Distinguished Professor of the Practice in Information Design 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.

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. 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.

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.

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