You can teach all levels of learners, simultaneously. Teach techniques of interpreting and managing data through graphing and probability studies. The use of surveys, board games, and games of chance will help to reinforce basic skills in addition, subtraction, multiplication, and division. 96 pages.

Why research? -- Developing research questions -- Data -- Principles of data management -- Finding and using secondary data -- Primary and administrative data -- Working with missing data -- Principles of data presentation -- Designing tables for data presentations -- Designing graphics for data presentations

This teacher resource offers a detailed introduction to the Hands-On Mathematics program (guiding principles, implementation guidelines, an overview of the processes that grade 2 students use and develop during mathematics inquiry), and a classroom assessment plan complete with record-keeping templates and connections to the Achievement Levels outlined in the Ontario Mathematics Curriculum. It also provides strategies and visual resources for developing students' mental math skills. Each unit is divided into lessons that focus on specific curricular expectations. Each lesson has materials lists, activity descriptions, questioning techniques, problem-solving examples, activity centre and extension ideas, assessment suggestions, activity sheets and visuals.--Portage & Main Press.

Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.

Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsA unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate thro

Documents relating to "NIH guidelines for research involving recombinant DNA molecules".

This book constitutes the refereed post-proceedings of the 13th International Conference on AI, Simulation, and Planning in High Autonomy Systems, AIS 2004, held in Jeju Island, Korea in October 2004. The 74 revised full papers presented together with 2 invited keynote papers were carefully reviewed and selected from 170 submissions; after the conference, the papers went through another round of revision. The papers are organized in topical sections on modeling and simulation methodologies, intelligent control, computer and network security, HLA and simulator interoperation, manufacturing, agent-based modeling, DEVS modeling and simulation, parallel and distributed modeling and simulation, mobile computer networks, Web-based simulation and natural systems, modeling and simulation environments, AI and simulation, component-based modeling, watermarking and semantics, graphics, visualization and animation, and business modeling.