In this activity, students will refer to the 1790 and 1800 Censuses to compare the total population with the population of enslaved people, drawing conclusions from the data.

Students will explore census data, including infographics and reports, to better understand rates of poverty in the nation.

In this unit, students examine the question: How does access to a specific diet (nutrition) impact human rights? As students explore biological information on how the human body uses food as a source of energy, they will explore the Universal Declaration of Human Rights (UDHR) document, to examine the crucial question of how access to a proper diet is related to a person’s rights.

Project Assignment for the course "CSC 217: Probability and statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

This document provides learning-by-doing materials for Analytics software skill development using SAS JMP. It integrates Analytics concepts and techniques with real-world scenarios based on the COVID-19 pandemic to illustrate how real-world data can be transformed into actionable insights to offer decision support for COVID-19 related issues. A holistic treatment of the Analytics process from data acquisition and cleansing to data analysis and interpretation is emphasized using five studies:
Characterize COVID-19 mortality demographic risk factors,
Visualize COVID-19 mortality demographics,
Conduct COVID-19 mortality time series forecasting,
Predict COVID-19 mortality, and
Analyze COVID-19 vaccine acceptance, uptake, and experiences.

Each study is structured with guiding questions to engage students to think critically, relate Analytic concepts to the given situation, and arrive at their own answers/solutions for active knowledge exploration and discovery.

After a car and pedestrian accident occurs near the local school, concerned students, parents, and neighbors launch a neighborhood safety project. Students consider potential hazards and then collect traffic and pedestrian data that might shed light on the situation. A survey is conducted to determine how children in the neighborhood travel between home and school, and students challenge their classmates to increase their use of human-powered (foot and pedal) transportation. Students use spreadsheets to enter and represent data, analyze their observations and survey data to determine the most significant problems, and study possible solutions. They develop a proposal for improving traffic safety, create slideshows and brochures, and present their ideas to the local city council.

This unit plan was originally developed by the Intel® Teach program as an exemplary unit plan demonstrating some of the best attributes of teaching with technology.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Scientists seeking to understand the difference between a healthy and unhealthy gut microbiome often turn to sequencing microbial DNA. Two main approaches are currently used: metabarcoding, also known as targeted amplicon sequencing, and shotgun sequencing of random fragments. While metabarcoding has limited resolution, full shotgun sequencing has improved resolution but comes with a higher cost, restricting the number of samples sequenced. As an alternative, researchers evaluated the use of Reduced Metagenome Sequencing (RMS) to estimate microbial community composition. RMS involves double-digested restriction-associated DNA sequencing, meaning that only a small fraction of genomes are sequenced. RMS read sets are different from both amplicon and shotgun data, making it difficult to use analysis pipelines for either method. A new analysis pipeline is proposed that uses fragment clustering and constrained ordinary least square deconvolution, allowing for estimates of relative abundance..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This course was developed and taught by Ben Marwick, Professor of Archaeology at University of Washington. It is a requirement for the UW Master of Science in Data Science, introduces students to the principles and tools for computational reproducibility in data science using R. Topics covered include acquiring, cleaning and manipulating data in a reproducible workflow using the tidyverse. Students will use literate programming tools, and explore best practices for organizing data analyses. Students will learn to write documents using R markdown, compile R markdown documents using knitr and related tools, and publish reproducible documents to various common formats. Students will learn strategies and tools for packaging research compendia, dependency management, and containerising projects to provide computational isolation.

It is expected that Students who take part in this course have completed almost all courses of their MSc and are about to start on their Master Orientation project, their Literature Study or MSc thesis depending on their chosen MSC track.

It is of little value to take this course early, so please plan accordingly!
Course Contents The aim of the course is to be a research-driven preparation for the aerospace engineering MSc thesis in the final year of the MSc. It will help you prepare for the challenges of your thesis work.

The course will consist of 7 lectures and will be taught online using video lectures in periods 1, 2 and 3 and face-to-face using traditional lectures in period 4.

The lecture set up is as follows:
1. Research Design in MSc - Introduction to research, research framework
2. Research Methods - Stages of a project, Research objective, research questions, research strategy, research methods
3. Data Analysis - Quantitative & Qualitative methods
4. Validation & Verification - How to validate & verify your work?
5. Project Management & Peer review of draft Project plan - How to manage your project and your thesis progress. Project plan peer review
6. Planning - How to plan, expectations, Gannt Charts
7. Literature Review - How to carry out a scientific literature review? Differences between review and research

Please be advised that all lectures are also available via Blackboard for those following the online version. It is possible to do this course by distant learning, attendance in the 4th period, though highly appreciated, is not mandatory!
Study Goals At the end of the course the student will:
- be aware of the expectations of an MSc student
- be able to formulate a research question and research aim
- be able to set up a research plan for their MOP/Literature Study/MSc thesis
- be able to write a literature review based on the research plan with a view to select appropriate methodologies for their MOP/MSc thesis

Education Method (Online) Lectures, Assignments and voluntary Peer review of each others research plans and literature studies

Assignment for political science course that allows students to analyze data, run a frequency distribution on a dependent variable, and identify and code independent and control variables.

Add different salts to water, then watch them dissolve and achieve a dynamic equilibrium with solid precipitate. Compare the number of ions in solution for highly soluble NaCl to other slightly soluble salts. Relate the charges on ions to the number of ions in the formula of a salt. Calculate Ksp values.

Students will participate in an online scavenger hunt based on a story that a geographer named Gina, who loves to travel, has escaped to an undisclosed location. It is their mission to bring her back to the school. Students must follow a series of clues about the location including landmarks, weather, and population—and use a U.S. Census Bureau data tool called State Facts for Students to answer questions that lead them one step closer to finding Gina.

This activity asks students to review the demographic and lifestyle statistics available at ERsys.com, and determine which city or location would provide the best consumer market opportunity for the given problem.

This advanced course in anthropology engages closely with discussions and debates about ethnographic research, ethics, and representation.

Students are introduced to several types of common medical sensor devices, such as ear and forehead thermometers, glucometers and wrist blood pressure monitors; they use the latter to measure their blood pressure and pulse rates. Students also measure their heights and weights in order to calculate their BMIs (body mass index). Then they use the collected data to create and analyze scatterplots of the different variables to determine if any relationships exist between the measured variables. Discussions about the trends observed and possible health concerns conclude the activity.

Students modify a provided App Inventor code to design their own diseases. This serves as the evolution step in the software/systems design process. The activity is essentially a mini design cycle in which students are challenged to design a solution to the modification, implement and test it using different population patterns The result of this process is an evolution of the original app.

Students are introduced to the technology of flexible circuits, some applications and the photolithography fabrication process. They are challenged to determine if the fabrication process results in a change in the circuit dimensions since, as circuits get smaller and smaller (nano-circuits), this could become very problematic. The lesson prepares students to conduct the associated activity in which they perform statistical analysis (using Excel® and GeoGebra) to determine if the circuit dimension sizes before and after fabrication are in fact statistically different. A PowerPoint® presentation and post-quiz are provided. This lesson and its associated activity are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note for details.

Students apply pre-requisite statistics knowledge and concepts learned in an associated lesson to a real-world state-of-the-art research problem that asks them to quantitatively analyze the effectiveness of different cracked steel repair methods. As if they are civil engineers, students statistically analyze and compare 12 sets of experimental data from seven research centers around the world using measurements of central tendency, five-number summaries, box-and-whisker plots and bar graphs. The data consists of the results from carbon-fiber-reinforced polymer patched and unpatched cracked steel specimens tested under the same stress conditions. Based on their findings, students determine the most effective cracked steel repair method, create a report, and present their results, conclusions and recommended methods to the class as if they were presenting to the mayor and city council. This activity and its associated lesson are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note for details.

Working as if they are engineers aiming to analyze and then improve data collection devices for precision agriculture, students determine how accurate temperature sensors are by comparing them to each other. Teams record soil temperature data during a class period while making changes to the samples to mimic real-world crop conditions—such as the addition of water and heat and the removal of the heat. Groups analyze their collected data by finding the mean, median, mode, and standard deviation. Then, the class combines all the team data points in order to compare data collected from numerous devices and analyze the accuracy of their recording devices by finding the standard deviation of temperature readings at each minute. By averaging the standard deviations of each minute’s temperature reading, students determine the accuracy of their temperature sensors. Students present their findings and conclusions, including making recommendations for temperature sensor improvements.

This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.