Students investigate different balls' abilities to bounce and represent the data they collect graphically.

Artist R. Luke DuBois makes unique portraits of presidents, cities, himself and even Britney Spears using data and personality. R. Luke DuBois weaves information from a multitude of sources into art and music exploring the tensions between algorithms, portraiture and temporal space. About the speakerR. Luke DuBois · Artist, composer, engineer R. Luke DuBois weaves information from a multitude of sources into art and music exploring the tensions between algorithms, portraiture and temporal space. In this talk, he shares nine projects — from maps of the country built using information taken from millions of dating profiles to a gun that fires a blank every time a shooting is reported in New Orleans. His point: the way we use technology reflects on us and our culture, and we reduce others to data points at our own peril.

Introduces students to the basic tools in using data to make informed management decisions. Covers introductory probability, decision analysis, basic statistics, regression, simulation, and linear and nonlinear optimization. Computer spreadsheet exercises and examples drawn from marketing, finance, operations management, and other management functions. Restricted to Sloan Fellows.

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Using the same method for measuring friction that was used in the previous lesson (Discovering Friction), students design and conduct an experiment to determine if weight added incrementally to an object affects the amount of friction encountered when it slides across a flat surface. After graphing the data from their experiments, students can calculate the coefficients of friction between the object and the surface it moved upon, for both static and kinetic friction.

Epidemiological investigation requires a good understanding of different data types, as this will strongly influence data analysis and interpretation. Data can broadly be classified as qualitative and quantitative, and within each of these groups, data can be further categorised as shown below. Although different grouping systems are available, it is important to consider the type of data being dealt with prior to any analysis. If desired, data can often be changed into different types through manipulation (for example, the quantitative variable weight can be converted to qualitative variables such as low/medium/high or low/not low).

All epidemiological investigations require some form of data description. A number of methods are available for describing data, and the most appropriate one will depend upon both the type of data available and the aims of the investigation. If these issues are not considered, useful information may be lost, or more seriously, a misleading estimate may be made.

This unit will help you to understand the forms of data that are handled by software and look at the various processes that can be applied to the data. These ideas are demonstrated through the use of a supermarket till and illustrate how simple data sets can be manipulated.

This course introduces the basic techniques of demographic analysis. Students will become familiar with the sources of data available for demographic research. Population composition and change measures will be presented. Measures of mortality, fertility, marriage and migration levels and patterns will be defined. Life table, standardization and population projection techniques will also be explored.

Students are introduced to the "Walk the Line" challenge question. They write journal responses to the question and brainstorm what information they need to answer the question. Ideas are shared with the class (or in pairs and then to the class, if class size is large). Then students read an interview with an engineer to gain a professional perspective on linear data sets and best-fit lines. Students brainstorm for additional ideas and add them to the list. With the teacher's guidance, students organize the ideas into logical categories of needed knowledge.

Use this activity to explore forces acting on objects, practice graphing experimental data, and introduce the algebra concepts of slope and intercept of a line. A wooden 2 x 4 beam is set on top of two scales. Students learn how to conduct an experiment by applying loads at different locations along the beam, recording the exact position of the applied load and the reaction forces measured by the scales at each end of the beam. In addition, students analyze the experiment data with the use of a chart and a table, and model/graph linear equations to describe relationships between independent and dependent variables.

***LOGIN REQUIRED*** Students will create an inquiry based mini lab given materials in the area of plant science. Students will work together to design protocols, collect data, and analyze results. Lesson 2 out of 4

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Students use their senses to describe what the weather is doing and predict what it might do next. After gaining a basic understanding of weather patterns, students act as state park engineers and design/build "backyard weather stations" to gather data to make actual weather forecasts.

MIT grad student Joy Buolamwini was working with facial analysis software when she noticed a problem: the software didn't detect her face — because the people who coded the algorithm hadn't taught it to identify a broad range of skin tones and facial structures. Now she's on a mission to fight bias in machine learning, a phenomenon she calls the "coded gaze." It's an eye-opening talk about the need for accountability in coding ... as algorithms take over more and more aspects of our lives

We're on the edge of a new frontier in art and creativity — and it's not human. Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning. In this captivating demo, he shows how neural nets trained to recognize images can be run in reverse, to generate them. The results: spectacular, hallucinatory collages (and poems!) that defy categorization. "Perception and creativity are very intimately connected," Agüera y Arcas says. "Any creature, any being that is able to do perceptual acts is also able to create."

This document provides synopses of frequently used time series of global-change data. Records are presented in multipage formats, each dealing with a specific site, region, or emissions species. The data records include tables, graphs, discussions of methods for collecting, measuring, and reporting the data, trends in the data, and references to literature providing further information. Instructions for citing specific data in Trends Online are provided for each compiled data set. Save