This course focuses on cyberspace and its implications for private and public, sub-national, national, and international actors and entities.
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.
This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.
The book offers a blend of theory and practice in guiding readers to apply design thinking principles to solving some of our world’s biggest problems. At the same time, readers are encouraged to become aware of new and emerging technologies that make prototyping and applying solutions a reality.
Le but de ce manuel est de vous donner les connaissances de base nécessaires pour commencer à rechercher des informations en utilisant le catalogue et les bases de données de la bibliothèque de l’Université d’Alberta. Le manuel contient des instructions étape par étape, des vidéos et des exemples en sciences humaines et sociales. Pour atteindre les objectifs prévus, il vous faudra passer au moins une heure et demie à lire les instructions du didacticiel, répondre aux questions des exercices et revoir les exemples chaque fois que cela sera nécessaire.
Differentiating open access and open educational resource can be a challenge in some contexts. Excellent resources such as "How Open Is It?: A Guide for Evaluating the Openness of Journals" (CC BY) https://sparcopen.org/our-work/howopenisit created by SPARC, PLOS, and OASPA greatly aid us in understanding the relative openness of journals. However, visual resources to conceptually differentiate open educational resources (OER) from resources disseminated using an open access approach do not currently exist. Until now.
This one page introductory guide differentiates OER and OA materials on the basis of purpose (teaching vs. research), method of access (analog and digital), and in terms of the relative freedoms offered by different levels of Creative Commons licenses, the most common open license. Many other open licenses, including open software licenses also exist.
An instructional text that seeks to untangle the social complexities and ethical dilemmas of online data and information. DIGITAL CITIZENSHIP will educate readers on the economics of the Internet and the means by which political bad actors exploit its platforms to pervert the public discourse.
Hello and welcome to the Digital Citizenship Toolkit. Have you ever wondered if your phone is listening to you? Do you ever look to the Internet for the answer to a question, and hours later, find that you are more confused than before? Have you argued with a friend or relative about a meme? Have you been tempted to share your own thoughts and feelings online, but resisted for fear of trolls? This book delves into these issues and more.
A modules-based approach to learning research skills that emphasizes the reflective nature of information discovery, the contextual basis for evaluating that information, and a recognition that information has value.
The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.
This resource is used to provide library instruction for introductory undergraduate composition courses.
This resource is used for library instruction in undergraduate second semester composition courses
This is a collection of all materials used in Health Information Technology by Dr. Chi Zhang at Kennesaw State University, including lecture slides, assignments, and assessments, including a question bank.
Topics covered include:
Clinical Financial Records
Patient Bedside Systems
Health Information Networks
HIPAA Privacy and Security
This course will provide a gentle, yet intense, introduction to programming using Python for highly motivated students with little or no prior experience in programming. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. The course is designed to help prepare students for 6.01 Introduction to EECS. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. 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.
This course is a collaborative offering of Sana, Partners in Health, and the Institute for Healthcare Improvement (IHI). The goal of this course is the development of innovations in information systems for developing countries that will (1) translate into improvement in health outcomes, (2) strengthen the existing organizational infrastructure, and (3) create a collaborative ecosystem to maximize the value of these innovations. The course will be taught by guest speakers who are internationally recognized experts in the field and who, with their operational experiences, will outline the challenges they faced and detail how these were addressed.This OCW site combines resources from the initial Spring 2011 offering of the course (numbered HST.184) and the Spring 2012 offering (numbered HST.S14).
This freshman course explores the scientific publication cycle, primary vs. secondary sources, and online and in-print bibliographic databases; how to search, find, evaluate, and cite information; indexing and abstracting; using special resources (e.g. patents) and "grey literature" (e.g. technical reports and conference proceedings); conducting Web searches; and constructing literature reviews.
To be information literate, a person must be able to recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information. By the end of this unit you will be able to Define Information Literacy, Define the four domains that fall under Metaliterate Learners, Identify a lack of knowledge in a subject area, Identify a search topic/question and define it using simple terminology, Articulate current knowledge on a topic, Recognize a need for information and data to achieve a specific end and define limits to the information need, and Manage time effectively to complete a search.
During your studies you will frequently be asked to write a paper. For such a paper you will need information, but how do you get it? What exactly do you need? Where can you find it? How do you go about it? Almost anyone can use Google, of course, but more is expected of a TU Delft student!
We challenge you to go beyond using the popular search engines. This instruction will help you discover what there is to learn about information skills.
This instruction follows on from the online instruction Information Literacy 1, in which you learned how to find, evaluate and use information. Today’s instruction is intended for advanced users.