Courses tagged with "Web Design" (37)

The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

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.

Learn both theory and application for basic methods that have been invented either for developing new concepts – principal components or clusters, or for finding interesting correlations – regression and classification. This is preceded by a thorough analysis of 1D and 2D data.

本课程讨论人、媒介、信息在社会化媒体环境下的新规律。The course introduces students to regular patterns of interaction among people, media and information under our surrounding social media .

Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialization.

Learn how to program in R and how to use R for effective data analysis. This is the second course in the Johns Hopkins Data Science Specialization.

Learn how to gather, clean, and manage data from a variety of sources. This is the third course in the Johns Hopkins Data Science Specialization.

Learn the essential exploratory techniques for summarizing data. This is the fourth course in the Johns Hopkins Data Science Specialization.

Learn the concepts and tools behind reporting modern data analyses in a reproducible manner. This is the fifth course in the Johns Hopkins Data Science Specialization.

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.

Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization.

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 introduces concepts, algorithms, programming, theory and design of spatial computing technologies such as global positioning systems (GPS), Google Maps, location-based services and geographic information systems. Learn how to collect, analyze, and visualize your own spatial datasets while avoiding common pitfalls and building better location-aware technologies.

Learn how to take scattered data and organize it into groups for use in many applications, such as market analysis and biomedical data analysis, or as a pre-processing step for many data mining tasks.

Learn how to transform information from a format efficient for computation into a format efficient for human perception, cognition, and communication. Explore elements of computer graphics, human-computer interaction, perceptual psychology, and design in addition to data processing and computation.

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.