Format of the training

This course begins by introducing modern distributed file systems and MapReduce, with a focus on what distinguishes effective MapReduce algorithms for handling large datasets. The remainder of the course delves into algorithms for extracting valuable models and insights from these vast datasets. Topics include Google’s PageRank algorithm for assessing web page importance and its various extensions, locality-sensitive hashing for identifying similar items in massive datasets, and efficient dimensionality reduction techniques for large, sparse matrices. The course also explores a range of other large-scale algorithms, as detailed in the syllabus.

The course lasts for 7 weeks. Before taking it, a course in database systems is recommended, as is a basic course on algorithms and data structures. 

Computer Science 101 is a stand-alone course that teaches the basic ideas of computer science to an audience with zero previous experience. Computers may look very complicated, but in reality they only work on a few simple circuits. The course demystifies and brings these patterns to life, which is useful for anyone using computers today.

Course participants play and experiment with short pieces of “computer code” to understand the power and limitations of computers. Everything works within the browser, so there is no additional software to download or install. CS101 also provides general information about computers today: what is a computer, what is hardware, what is software, what is the Internet. Anyone who knows how to use a web browser can be successful with this course. No previous computer science experience is required.