Presentations, materials and other resources offered throughout the 2019 Continuous Quality Improvement Statewide Conference for Child Welfare and Probation at UC Davis.
Grade level: graduate students, advanced undergrads, persons with analyzed research results
Course length: 1 semester, 4-6 months
Objective: This course empowers scientists to engage with their own data, each other, and the public through art. Through collective brainstorming, prototyping, and feedback from professional artists, students will create a project that expresses their own research through any artistic medium of their choice. The course typically culminates in a public art exhibition where students interact with a general audience to discuss their research, art, and what it means to be a scientist.
David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. A quiz, thought provoking question, and links for further study are provided to create a lesson around the 18-minute video. Educators may use the platform to easily "Flip" or create their own lesson for use with their students of any age or level.
The aim of this course is to provide fundamental statistical concepts and tools relevant to the practice of summarizing, analyzing, and visualizing data. This course will build your knowledge of the fundamental principles of biostatistical inference. The course will focus on linear regression and generalized linear regression models. We will use a variety of examples and exercises from scientific, medical, and public health research.
Submitted as part of the California Learning Resource Network (CLRN) Phase 3 Digital Textbook Initiative (CA DTI3), CK-12 Advanced Probability and Statistics introduces students to basic topics in statistics and probability but finishes with the rigorous topics an advanced placement course requires. Includes visualizations of data, introduction to probability, discrete probability distribution, normal distribution, planning and conducting a study, sampling distributions, hypothesis testing, regression and correlation, Chi-Square, analysis of variance, and non-parametric statistics.
As if they are environmental engineers, student pairs are challenged to use Google Earth Pro (free) GIS software to view and examine past data on hurricanes and tornados in order to (hypothetically) advise their state government on how to proceed with its next-year budget—to answer the question: should we reduce funding for natural disaster relief? To do this, students learn about maps, geographic information systems (GIS) and the global positioning system (GPS), and how they are used to deepen the way maps are used to examine and analyze data. Then they put their knowledge to work by using the GIS software to explore historical severe storm (tornado, hurricane) data in depth. Student pairs confer with other teams, conduct Internet research on specific storms and conclude by presenting their recommendations to the class. Students gain practice and perspective on making evidence-based decisions. A slide presentation as well as a student worksheet with instructions and questions are provided.
The home of the U.S. Government’s open data. Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more. Topics include Agriculture, Business, Climate, Education, Energy, Ecosystems, Manufacturing and more.
Essentials of Geographic Information Systems integrates key concepts behind the technology with practical concerns and real-world applications. Recognizing that many potential GIS users are nonspecialists or may only need a few maps, this book is designed to be accessible, pragmatic, and concise. Essentials of Geographic Information Systems also illustrates how GIS is used to ask questions, inform choices, and guide policy. From the melting of the polar ice caps to privacy issues associated with mapping, this book provides a gentle, yet substantive, introduction to the use and application of digital maps, mapping, and GIS.
Data needs to be packaged in a beautiful, simple presentation. Presenting data is all about the user experience, and it is your job as an analyst to present your data in a very clear and concise manner. The information in this resource will help you learn how to better present your data to maximize user experience.
This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations. This was offered as a non-credit course 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.
How did scientists figure out the structure of atoms without looking at them? Try out different models by shooting light at the atom. Check how the prediction of the model matches the experimental results.
Help scientists recover worldwide weather observations made by Royal Navy ships around the time of World War I. These transcriptions will contribute to climate model projections and improve a database of weather extremes. Historians will use your work to track past ship movements and the stories of the people on board.
CK-12 Foundation's new and improved Advanced Probability and Statistics-Second Edition FlexBook introduces students to basic topics in statistics and probability, but finishes with the rigorous topics an advanced placement course requires.
CK-12 Foundation's Basic Probability and Statistics - A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.
A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally. Familiarity with MATLABA, Octave, or R will be useful, prior experience with statistics will be helpful but is not essential. This course is intended to be a ground-up sketch of a coherent, alternative perspective to the "null-hypothesis significance testing" method for behavioral research (but don't worry if you don't know what this means).