Students pass around and distort messages written on index cards to learn how we use signals from GPS occultations to study the atmosphere. The cards represent information sent from GPS satellites being distorted as they pass through different locations in the Earth's atmosphere and reach other satellites. Analyzing GPS occultations enables better global weather forecasting, storm tracking and climate change monitoring.

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.

Students use U.S. Geological Survey (USGS) real-time, real-world seismic data from around the planet to identify where earthquakes occur and look for trends in earthquake activity. They explore where and why earthquakes occur, learning about faults and how they influence earthquakes. Looking at the interactive maps and the data, students use Microsoft® Excel® to conduct detailed analysis of the most-recent 25 earthquakes; they calculate mean, median, mode of the data set, as well as identify the minimum and maximum magnitudes. Students compare their predictions with the physical data, and look for trends to and patterns in the data. A worksheet serves as a student guide for the activity.

In this course, the student will learn the theoretical and practical aspects of algorithms and Data Structures. The student will also learn to implement Data Structures and algorithms in C/C++, analyze those algorithms, and consider both their worst-case complexity and practical efficiency. Upon successful completion of this course, students will be able to: Identify elementary Data Structures using C/C++ programming languages; Analyze the importance and use of Abstract Data Types (ADTs); Design and implement elementary Data Structures such as arrays, trees, Stacks, Queues, and Hash Tables; Explain best, average, and worst-cases of an algorithm using Big-O notation; Describe the differences between the use of sequential and binary search algorithms. (Computer Science 201)

This Unit will introduce you to a number of ways of representing data graphically and of summarising data numerically. You will learn the uses for pie charts, bar charts, histograms and scatterplots. You will also be introduced to various ways of summarising data and methods for assessing location and dispersion.

This learning video presents an introduction to the Flaws of Averages using three exciting examples: the ''crossing of the river'' example, the ''cookie'' example, and the ''dance class'' example. Averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, however, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. During this video lesson, students will learn about three flaws of averages: (1) The average is not always a good description of the actual situation, (2) The function of the average is not always the same as the average of the function, and (3) The average depends on your perspective. To convey these concepts, the students are presented with the three real world examples mentioned above.

Students learn how to use and graph real-world stream gage data to create event and annual hydrographs and calculate flood frequency statistics. Using an Excel spreadsheet of real-world event, annual and peak streamflow data, they manipulate the data (converting units, sorting, ranking, plotting), solve problems using equations, and calculate return periods and probabilities. Prompted by worksheet questions, they analyze the runoff data as engineers would. Students learn how hydrographs help engineers make decisions and recommendations to community stakeholders concerning water resources and flooding.

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.

Working as engineering teams in this introductory pneumatics lab, students design and build working pneumatic (air-powered) systems. The goal is to create systems that launch balls into the air. They record and analyze data from their launches.

In this open-ended, hands-on activity that provides practice in engineering data analysis, students are given gait signature metric (GSM) data for known people types (adults and children). Working in teams, they analyze the data and develop models that they believe represent the data. They test their models against similar, but unknown (to the students) data to see how accurate their models are in predicting adult vs. child human subjects given known GSM data. They manipulate and graph data in Excel® to conduct their analyses.

This 10-day module builds on Grade 2 concepts about data, graphing, and line plots. The two topics in this module focus on generating and analyzing categorical and measurement data.  By the end of the module, students are working with a mixture of scaled picture graphs, bar graphs, and line plots to problem solve using both categorical and measurement data.

In this module, students move from simply representing data into analysis of data.  Students begin to think and reason statistically, first by recognizing a statistical question as one that can be answered by collecting data.  Students learn that the data collected to answer a statistical question has a distribution that is often summarized in terms of center, variability, and shape.  Throughout the module, students see and represent data distributions using dot plots and histograms.  They study quantitative ways to summarize numerical data sets in relation to their context and to the shape of the distribution.  As the module ends, students synthesize what they have learned as they connect the graphical, verbal, and numerical summaries to each other within situational contexts, culminating with a major project.

Students analyze their social networks using graph theory. They gather data on their own social relationships, either from Facebook interactions or the interactions they have throughout the course of a day, recording it in Microsoft Excel and using Cytoscape (a free, downloadable application) to generate social network graphs that visually illustrate the key persons (nodes) and connections between them (edges). The nodes in the Cytoscape graphs are color-coded and sized according to the importance of the node (in this activity, nodes are people in students' social networks). After the analysis, the graphs are further examined to see what can be learned from the visual representation. Students gain practice with graph theory vocabulary, including node, edge, betweeness centrality and degree on interaction, and learn about a range of engineering applications of graph theory.

In a multi-week experiment, student groups gather data from the photobioreactors that they build to investigate growth conditions that make algae thrive best. Using plastic soda bottles, pond water and fish tank aerators, they vary the amount of carbon dioxide (or nutrients or sunlight, as an extension) available to the microalgae. They compare growth in aerated vs. non-aerated conditions. They measure growth by comparing the color of their algae cultures in the bottles to a color indicator scale. Then they graph and analyze the collected data to see which had the fastest growth. Students learn how plants biorecycle carbon dioxide into organic carbon (part of the carbon cycle) and how engineers apply their understanding of this process to maximize biofuel production.

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."

Giorgia Lupi uses data to tell human stories, adding nuance to numbers. In this charming talk, she shares how we can bring personality to data, visualizing even the mundane details of our daily lives and transforming the abstract and uncountable into something that can be seen, felt and directly reconnected to our lives.

Broad coverage of technology concepts underlying modern computing and information management. Topics include computer architecture and operating systems, relational database systems, graphical user interfaces, networks, client/server systems, enterprise applications, cryptography, and the web. Hands-on exposure to internet services, Microsoft Access database management system, and Lotus Notes. Information Technology I helps students understand technical concepts underlying current and future developments in information technology. There will be a special emphasis on networks and distributed computing. Students will also gain some hands-on exposure to powerful, high-level tools for making computers do amazing things, without the need for conventional programming languages. Since 15.564 is an introductory course, no knowledge of how computers work or are programmed is assumed.

This unit is concerned with two main topics. In Section 1, you will learn about another kind of graphical display, the boxplot. A boxplot is a fairly simple graphic, which displays certain summary statistics of a set of data. Boxplots are particularly useful for assessing quickly the location, dispersion, and symmetry or skewness of a set of data, and for making comparisons of these features in two or more data sets. Boxplots can also be useful for drawing attention to possible outliers in a data set. The other topic, which is covered in Sections 2 and 3, is that of dealing with data presented in tabular form. You are, no doubt, familiar with such tables: they are common in the media and in reports and other documents. Yet it is not always straightforward to see at first glance just what information a table of data is providing, and it often helps to carry out certain calculations and/or to draw appropriate graphs to make this clearer. In this unit, some other kinds of data tables and some different approaches are covered.

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.