Wolfram Education Group offers courses for a variety of levels of Mathematica expertise. Courses are offered live over the internet, onsite at customer locations, and in computer classrooms and Bring Your Own Laptop (BYOL) locations throughout the world. All courses are presented by a Wolfram Research senior developer or a Wolfram Education Group certified instructor.
M50: An Introduction to Mathematica in the Classroom »
Designed to give high-school and community-college teachers an introduction to Mathematica, this course provides the background needed to use Mathematica to prepare classroom materials, create quizzes and exams, and create student projects.
M100: An Introduction to Mathematica »
This training course introduces the basic features needed to become a proficient user of Mathematica, including programming, visualization and graphics, the notebook interface, symbolic computation, and numerical computation.
M102: Project Session »
Scheduled in conjunction with M101, this session explores selected topics. Participants solve computational problems in their own areas of interest and application.
M205: Visualization and Dynamic Interactivity »
This series of short courses are designed for people who want to take advantage of Mathematica's graphical and visualization tools as well as dynamic elements. Section A of the course is designed to help users master the two- and three-dimensional graphical functions and options in Mathematica. Section B covers the interactive elements in Mathematica, including animations, the Manipulate function, sliders, popup menus, and more.
M215: Applied Statistical Analysis: Descriptive and Mathematical Statistics »
This series of short courses uses real-world and simulated datasets to demonstrate how to import data, extract data based on criteria, analyze the data, and visualize the results. Section A discusses descriptive statistics and visualization for data and distributions, hypothesis testing, and ANOVA. Section B covers linear and nonlinear fitting, regression diagnostics, robust estimation, maximum likelihood estimation, and generalized linear models.
M221: Introduction to Programming in Mathematica »
This course focuses on the programming capabilities of Mathematica, including functional, procedural, and rule-based programming. It includes practical hands-on exercises, and shows how to choose the appropriate programming paradigm for solving problems.
M225: Parallel Computing with Mathematica »
This short course provides an introduction to parallel and distributed programming in Mathematica. It discusses the underlying technology and core functions for developing parallel applications, and provides examples of the parallel development process. The course provides the necessary knowledge and understanding to explore the parallel capabilities of Mathematica, which are applicable both to the multi-core personal computer and the large-scale computer grid.
M235: Mathematica Development using Wolfram Workbench »
This short course covers the major concepts and features of the integrated development environment at the core of Wolfram Workbench. Features such as source code editing, debugging, profiling, and unit testing for advanced development of Mathematica code and projects will be presented and explained.
M255: webMathematica using Wolfram Workbench »
This short course gives an introduction to the core features of webMathematica, along with the development tools provided by Wolfram Workbench. The course is designed primarily for anyone interested in developing webMathematica-powered sites, that are built on Mathematica applications, for clients to access through a web browser.
M310: Digital Image Processing »
This course presents the theory and practice of digital image processing with Mathematica and focuses on the Digital Image Processing package, demonstrating its features and capabilities and including numerous examples and practical hands-on exercises.
M330: Neural Networks »
This course presents the theory and practice of neural networks with Mathematica and the Neural Networks package. It contains relevant theory explaining practical issues when neural networks are used to find relations in data, and includes hands-on exercises illustrating practical solutions to problems using neural networks.
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