How do we draw a surface of water using only particles? In this video we'll introduce a powerful analogy using heat to help us out.
This book provides an updated look at issues that comprise the online learning experience creation process. As online learning evolves, the lines and distinctions between various classifications of courses has blurred and often vanished. Classic elements of instructional design remain relevant at the same time that newer concepts of learning experience are growing in importance. However, problematic issues new and old still have to be addressed. This handbook explores many of these topics for new and experienced designers alike, whether creating traditional online courses, open learning experiences, or anything in between.
How to use SQL to create a table and insert data into a database.
An overview of Creating elements from scratch
Learn how to use jQuery to create elements from scratch and then insert them at different places in your webpage.
Overview: This survey instrument is designed to understand how creators use (or decide not to use) Creative Commons licenses. The target survey participants are people who create materials for documentation, maintenance, instruction, learning, and/or revitalization of Indigenous, minority, endangered, and/or low-resourced languages. Part 1 of this survey is designed to learn about the digital creation formats and sharing practices in this specific community of practice. Parts 2 through 4 of this survey contain general questions intended to elicit information about research participants’ knowledge and awareness of Creative Commons (CC) licenses; understanding of how to apply CC licenses to their digital creations; interest in and experience with applying CC licenses to their digital creations; motivations for and barriers to applying CC licenses to their digital creations; and understanding of how to adapt or reuse digital creations licensed with CC licenses.
This survey can be easily adapted for use in other communities of practice by editing some of the questions and multiple-choice responses.
If you wish to read this survey and background information about it, please start with the file ReadMe-CCLuseSurvey.pdf.
The file Creative_Commons_License_Use_Survey.qsf is a Qualtrics Survey File that can be used to recreate this survey in your own instance of the Qualtrics software.
The file Creative_Commons_License_Use_Survey.pdf is a PDF of the survey. This version includes Creative Commons graphics that are used in the survey questions. The file ReadMe-CCLuseSurvey.pdf does not include the graphics.
This course features a rigorous introduction to modern cryptography, with an emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements.
A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.
This course examines computers anthropologically, as artifacts revealing the social orders and cultural practices that create them. Students read classic texts in computer science along with cultural analyses of computing history and contemporary configurations. It explores the history of automata, automation and capitalist manufacturing; cybernetics and WWII operations research; artificial intelligence and gendered subjectivity; robots, cyborgs, and artificial life; creation and commoditization of the personal computer; the growth of the Internet as a military, academic, and commercial project; hackers and gamers; technobodies and virtual sociality. Emphasis is placed on how ideas about gender and other social differences shape labor practices, models of cognition, hacking culture, and social media.
This economics course provides an introduction to the field of cybersecurity through the lens of economic principles. Delivered by four leading research teams, it will provide you with the economic concepts, measurement approaches and data analytics to make better security and IT decisions, as well as understand the forces that shape the security decisions of other actors in the ecosystem of information goods and services.
Systems often fail because the organizations that defend them do not bear the full costs of failure. In order to solve the problems of growing vulnerability to computer hackers and increasing crime, solutions must coherently allocate responsibilities and liabilities so that the parties in a position to fix problems have an incentive to do so. This requires a technical comprehension of security threats combined with an economic perspective to uncover the strategies employed by cyber hackers, attackers and defenders.
This course focuses on cyberspace and its implications for private and public, sub-national, national, and international actors and entities.
Google Security Princess Parisa Tabriz and Jenny Martin from Symantec introduce the most common types of cybercrime, including viruses, malware, DDOS attacks and phishing scams.
The DOM (Document Object Model)
Now we add a damper to our spring.
Modeling of the control processes in conventional and high-speed data communication networks. Develops and utilizes elementary concepts from queueing theory, algorithms, linear and nonlinear programming to study the problems of line and network protocols, distributed algorithms, quasi-static and dynamic routing, congestion control, deadlock prevention. Treats local and wide-area networks, and high-speed electronic and optical networks. Focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.
This website aims to develop and implement a DIL curriculum in conjunction with university faculty to address these needs.
This guide provides resources on the topic of Data Literacy, as a supplement to the OERTX Digital Information Literacy Hub.
This session was part of an undergraduate, critical thinking and global perspectives course. The course is offered by various disciplines on campus. This instance focused on global challenges (The Seven Revolutions developed by csis.org). For the session the students applied two frameworks to data: authority is constructed and contextual and scholarship as conversation. Students learned about a data life cycle concept with emphasis on evaluation.
The MIT Libraries Data Management Group hosts a set of workshops during IAP and throughout the year to assist MIT faculty and researchers with data set control, maintenance, and sharing. This resource contains a selection of presentations from those workshops. Topics include an introduction to data management, details on data sharing and storage, data management using the DMPTool, file organization, version control, and an overview of the open data requirements of various funding sources.
Introduces students to a class of methods known as data mining that assists managers in recognizing patterns and making intelligent use of massive amounts of electronic data collected via the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Topics covered: subset selection in regression, collaborative filtering, tree-structured classification and regression, cluster analysis, and neural network methods. Examples of successful applications in areas such as credit ratings, fraud detection, database marketing, customer relationship management, and investments and logistics are covered. Hands-on experimentation with data-mining software is used. Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.