Online courses directory (147)

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

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.

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.

In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures (heaps, hash tables, search trees), randomized algorithms, and more.

In this course you will learn several fundamental principles of advanced algorithm design: greedy algorithms and applications; dynamic programming and applications; NP-completeness and what it means for the algorithm designer; the design and analysis of heuristics; and more.

This course is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.

In this course we’ll explore complex analysis, complex dynamics, and some applications of these topics.

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. Part I covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

This Capstone MOOC gives Signature Track students who passed all previous MOOCs in the MoCCA Specialization “with Distinction” an opportunity to integrate and demonstrate the knowledge they've acquired across the three earlier content area MOOCs.

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.

This course covers finite automata, context-free grammars, Turing machines, undecidable problems, and intractable problems (NP-completeness).

Start learning how to program video games using the C# programming language. Plenty of practice opportunities are included!

Education is increasingly occurring online or in educational software, resulting in an explosion of data that can be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data.

Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data from high content molecular and phenotype profiling of human cells.

Use of available (mainly web-based) programs for analyzing biological data. This is an introductory course with a strong emphasis on hands-on methods. Some theory is introduced, but the main focus is on using extant bioinformatics tools to analyze data and generate biological hypotheses.

Use of available (mainly web-based) programs for analyzing biological data. This is Part 2 of an introductory course with a strong emphasis on hands-on methods. Some theory is introduced, but the main focus is on using extant bioinformatics tools to analyze data and generate biological hypotheses.

This course was the first in a two-part series covering some of the algorithms underlying bioinformatics. It has now been split into three smaller courses.

This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.

This course teaches the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in life sciences. The lectures are taught in both Mandarin Chinese and English with slides in English. 生物信息学是一门新兴的生命科学与计算科学的前沿交叉学科。本课程讲授生物信息学主要概念和方法，以及如何应用生物信息学手段解决生命科学问题。本课程同时提供中文普通话授课和英文授课两个版本，以及英文幻灯片。