In EARTH 801, you will develop skills in a programming language designed for visual arts and visualization while exploring Earth science topics. Specifically, you'll learn and practice digital graphics capabilities in order to render Earth science concepts that are otherwise difficult to visualize due to complicated space and time scales. Here, you will interact with large, open, freely-available data sets by collecting, plotting, and analyzing them using a variety of computational methods. You'll be ready to teach secondary school students a range of Next Generation Science Standard skills involving data collecting, manipulation, analysis, and plotting. You'll also read and discuss current research regarding the teaching, learning, and evaluation of visualization skills, as well as multiple external representations of science concepts.

Geospatial System Analysis and Design surveys the process of GIS design through critical reading/writing and collaborative discussion. Key topics in the course outline the broad range of current GIS systems, how they are designed and evaluated, and how emerging technologies may impact their design and implementation in the near future. In particular, students will develop a term-long project where they propose a realistic problem scenario that requires the skills and understanding required to effectively complete a geospatial system specification, design, and implementation. Students completing this course are able to develop a comprehensive system design plan that takes into account current technologies as well as emerging technology trends.

Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.

Information Technology I helps students understand technical concepts underlying current and future developments in information technology. There will be a special emphasis on networks and distributed computing. Students will also gain some hands-on exposure to powerful, high-level tools for making computers do amazing things, without the need for conventional programming languages. Since 15.564 is an introductory course, no knowledge of how computers work or are programmed is assumed.

Ever hang your head in shame after your Python program wasn’t as fast as your friend’s C program? Ever wish you could use objects without having to use Java? Join us for this fun introduction to C and C++! We will take you through a tour that will start with writing simple C programs, go deep into the caves of C memory manipulation, resurface with an introduction to using C++ classes, dive deeper into advanced C++ class use and the C++ Standard Template Libraries. We’ll wrap up by teaching you some tricks of the trade that you may need for tech interviews.
We see this as a “C/C++ empowerment” course: we want you to come away understanding

why you would want to use C over another language (control over memory, probably for performance reasons),
why you would want to use C++ rather than C (objects), and
how to be useful in C and C++.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Capítulo de apresentação da Linguaagem Python e instalação do Ambiente de Desenvovimento.Está direcionado para utilizar o Recurso Anaconda Python e o Jupter Notebook em ambiente operacional Windows 10 ou superior.

Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts.

OpenDSA is infrastructure and materials to support courses in a wide variety of Computer Science-related topics such as Data Structures and Algorithms (DSA), Formal Languages, Finite Automata, and Programming Languages.

OpenDSA materials include many visualizations and interactive exercises. Our philosophy is that students learn best when they engage the material and then practice it until they have demonstrated their proficiency. OpenDSA provides a place for students to practice skills and develop knowledge with a variety of exercises including small code writing problems, proficiency exercises where students demonstrate knowledge of an algorithm by interacting with a data structure, and questions about knowledge.

You do not have to be a student in a course to use OpenDSA. OpenDSA provides comprehensive support for teachers who want to use our materials for their textbook or homeworks. But we also support independent learners who are just practicing on their own. See our sample books list , where you can practice any exercises or view any materials that you wish.