Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package.
Today the mining industry is under increasing pressure to supply more minerals to meet the needs and expectations of a rapidly rising world population. This often requires extracting from greater depths, which in turn can create challenges when equipment needs servicing or repair. "Identifying maintenance requirements before something breaks down enables us to make major direct savings in costs and time," says Patrick Murphy, President of the Rock Drills & Technologies division at Sandvik. That is why Sandvik is working with IBM to introduce advanced analytical cognitive data processing and modeling based on data generated by sensors on loaders and trucks. "OptiMine Analytics, partnered by IBM Watson IoT [Internet of Things] solutions, offers our customers a more complete view of their operations for smarter, safer and more productive working," Murphy says.
Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them. The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining). The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.
Note: You should complete all the other courses in this Specialization before beginning this course. This six-week long Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, you will work on a restaurant review data set from Yelp and use all the knowledge and skills you've learned from the previous courses to mine this data set to discover interesting and useful knowledge. The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas creatively. The goal of the Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining.
CHEYENNE, WYOMING – Federal officials withdrew a proposed requirement for companies to clean up groundwater at uranium mines across the U.S. and will reconsider a rule that congressional Republicans criticized as too harsh on industry. The plan that the U.S. Environmental Protection Agency put on hold Wednesday involves in-situ mining, in which water containing chemicals is used to dissolve uranium out of underground sandstone deposits. Water laden with uranium, a toxic element used for nuclear power and weapons, is then pumped to the surface. No digging or tunneling takes place. The metal occurs in the rock naturally but the process contaminates groundwater with uranium in concentrations much higher than natural levels.