Courses tagged with "Udacity" (197)
This class is offered as CS6440 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. This is a survey course designed to provide a broad, forward-facing overview of contemporary health informatics, a specialized field of computing that seeks to improve the quality and efficiency of healthcare delivery. To understand health informatics (HIT) you also need to have at least a basic understanding of the complex and highly regulated US healthcare industry. The course is designed for students from diverse backgrounds and who have not been previously exposed to HIT. It is divided into three sections: The US healthcare delivery and the key role of the federal government in promoting HIT adoption The core technologies that drive all contemporary HIT systems and tools The real world applications of HIT from electronic medical and personal health records to exploiting digital data aggregated from them for research and other purposes
This class is offered as CS6290 at Georgia Tech where it is a part of the [Online Masters Degree (OMS)](http://www.omscs.gatech.edu/). Taking this course here will not earn credit towards the OMS degree. The course begins with a lesson on performance measurement, which leads to a discussion on the necessity of performance improvement. Pipelining, the first level of performance refinement, is reviewed. The weaknesses of pipelining will be exposed and explored, and various solutions to these issues will be studied. The student will learn hardware, software, and compiler based solutions to these issues.
The goal of this course is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. This course focuses on theoretical underpinnings. To give a practical feeling for how algorithms map to and behave on real systems, we will supplement algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems. This course is a graduate-level introduction to scalable parallel algorithms. “Scale” really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so. The techniques you’ll encounter covers the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include Cilk Plus, OpenMP, and MPI, and possibly others.)
In an introduction to the basics of the famous Customer Development Process, Steve Blank provides insight into the key steps needed to build a successful startup. The main idea in this course is learning how to rapidly develop and test ideas by gathering massive amounts of customer and marketplace feedback. Many startups fail by not validating their ideas early on with real-life customers. In order to mitigate that, students will learn how to get out of the building and search for the real pain points and unmet needs of customers. Only with these can the entrepreneur find a proper solution and establish a suitable business model. Building a startup is not simply building an execution plan for a business model that the entrepreneur thinks will work, but rather, a search for the actual business model itself.
Here is one important question Android developers ask while making apps: "How can I do ________ in Android?" The following are versions of this question that we came across recently: - How can I add radio buttons to my app? - How can I play a sound? - How can I navigate between multiple screens? This course is a collection of such questions and their answers. By the end of this course you will have mastered the ability to implement new Android features by reading a blog or article — this is a critical skill possessed by professional Android developers. As a result, you will also be able to use several User Interface components — like Toggle Buttons, Menus, Grid View and many more — that are central to making functional and delightful Android apps.
'2D Game Development with libGDX' explored the basics of the libGDX game framework, from simple drawing to user input. This course will use those skills to create a much more sophisticated platforming game called GigaGal. In creating GigaGal, you'll learn about sprite drawing, keeping track of complex game state, controlling interactions between game entities, and also how to load and manage level data.
Effective use of version control is an important and useful skill for any developer working on long-lived (or even medium-lived) projects, especially if more than one developer is involved. This course, *built with input from GitHub*, will introduce the basics of using version control by focusing on a particular version control system called Git and a collaboration platform called GitHub. This course is part of the Front End and Full Stack Nanodegrees..
This course is intended for budding full-stack web developers to master the basics of HTTP, the protocol that underlies all web technology. In this course, you'll explore HTTP directly, talking with web servers and browsers by hand. You'll write and deploy low-level web applications in Python. And you'll learn more about how HTTP connects with other web technologies.
This course provides a brief review of introductory algebra topics. Topics to be covered include integer operations, order of operations, perimeter and area, fractions and decimals, scientific notation, ratios and rates, conversions, percents, algebraic expressions, linear equations, the Pythagorean theorem, and graphing.
Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. ***Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!***
In this introduction to computer programming course, you’ll learn and practice key computer science concepts by building your own versions of popular web applications. You’ll learn Python, a powerful, easy-to-learn, and widely used programming language, and you’ll explore computer science basics, as you build your own search engine and social network.
This course will introduce you to the world of data analysis. You'll learn how to go through the entire data analysis process, which includes: * Posing a question * Wrangling your data into a format you can use and fixing any problems with it * Exploring the data, finding patterns in it, and building your intuition about it * Drawing conclusions and/or making predictions * Communicating your findings You'll also learn how to use the Python libraries NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and runs faster. This course is part of the [Data Analyst Nanodegree](https://www.udacity.com/course/data-analyst-nanodegree--nd002).
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