DASHlink is a virtual laboratory for scientists and engineers to disseminate results and collaborate on research problems in health management technologies for aeronautics systems. Managed by the Integrated Vehicle Health Management project within NASA's Aviation Safety program, the Web site is designed to be a resource for anyone interested in data mining, IVHM, aeronautics and NASA.
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.
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 focuses on basic and advanced techniques on data warehousing and data mining, the study of the processing, modelling, querying, organizing, classifying documents, identifying association rules and categorization.
Our final database video. This one looks at some odds and ends. We examine: Data Warehouse, Data Mining, Big Data. I also talk about the ethics of data mining from the NSA and CDC, and how they are different.
We also give out top picks for the lesson.
Links from Video:
•What is Database & SQL by Guru99 http://youtu.be/FR4QIeZaPeM
•What is a database http://youtu.be/t8jgX1f8kc4
•MySQL Database For Beginners https://www.udemy.com/mysql-database-for-beginners2/
Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems?
Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities.
This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.
Students learn basic data analysis tools and techniques in AP Statistics, but often dont work with large sets of real-world data. This project gives students exposure to how data is analyzed in many of Americas top corporations, universities and banks. By using multiple input variables, students learn how to develop realistic prediction models for the demand for goods and services.
- Statistics and Probability
- Material Type:
- Lesson Plan
- North Carolina State University
- Provider Set:
- Kenan Fellows Program for Curriculum and Leadership Development
- Celia Rowland
- Date Added:
This interactive lesson helps students understand how companies use algorithms to sort job applicants. It also encourages students to reflect on how digital data mining also can contribute to the hiring process. Students examine resumes and digital data to consider the ways in which our data may open or close opportunities in an increasingly digitized hiring market.
Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.