This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with GitHub, and reproducible document preparation with R markdown.
This subject offers an interactive introduction to discrete mathematics oriented toward computer science and engineering. The subject coverage divides roughly into thirds: Fundamental concepts of mathematics: Definitions, proofs, sets, functions, relations. Discrete structures: graphs, state machines, modular arithmetic, counting. Discrete probability theory. On completion of 6.042J, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.Interactive site components can be found on the Unit pages in the left-hand navigational bar, starting with Unit 1: Proofs.
M. Aurnhammer, P. Hanappe, и L. Steels. 5th International Semantic Web Conference, ISWC 2006, Athens, GA, USA, том 4273 из Lecture Notes in Computer Science, стр. 58--71. Springer-Verlag, (5-9 11 2006)
A. Park, R. Quadari, и H. Tsang. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), стр. 680-684. (октября 2017)