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This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.
After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes (i.e., short sequences of DNA) or two proteins. When we "zoom out" to compare entire genomes, we will employ combinatorial algorithms.
This course will discuss the major ideas used today in the implementation of programming language compilers. You will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines!
CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. The course uses small coding experiments in the browser to play with the nature of computers, understanding their strengths and limitations. Sign up for the "To be announced" session to be notified by email when the class is next run, and sign up for "Self-Study" to start browsing the class materials right away. Self-Study mode makes all the videos and assignments available to be done at your own pace, but without a certificate of completion at the end.
In today’s world, politics and economics are inextricably interconnected, but what is the nature of this connectivity? What are the power relationships that shape the world economy today and create new challenges for international institutions facing globalization? What makes some countries wealthier than others? Do we face cultural diversity or fragmentation? Does the type of governance effect economic development and social change or is it the other way around? How do we measure it and how trustworthy is the data? These issues and many more will be examined in this course along with up-to-date sources and biting criticism.
Learn how to make effective decisions about your future career and how to take control of your professional development by honing your critical thinking and employability skills. Suitable for anyone undertaking some form of study, regardless of academic discipline, interests or employment background.
This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up our algorithmic muscles, we will learn how to apply popular bioinformatics software tools to real experimental datasets.
In this class, we will compare DNA from an individual against a reference human genome to find potentially disease-causing mutations. We will also learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable.
This course teaches the fundamentals of Fog Networking, the network architecture that uses one or a collaborative multitude of end-user clients or near-user edge devices to carry out storage, communication, computation, and control in a network. It also teaches the key results in the design of the Internet of Things, including consumer and industrial applications.
Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium.