Strategies to Effectively Interpret and Evaluate Data Visualizations

Short Description:
A short course for students to increase their proficiency in analyzing and interpreting data visualizations. By completing this short course students will be able to explain the importance of data literacy, identify data visualization issues in order to improve their own skills in data story-telling. The intended outcome of this course is to help students become more discerning and critical users of data, graphs, charts and infographics.

Word Count: 9819

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Students make predictions about the stories and analyze story elements, compare and contrast the different stories, distinguish between fact and opinion, and draw conclusions supported by evidence from their readings.

Experiment with a simulated Crookes tube for qualitative results similar to Thomson's experiments in which the electron was discovered.

Using a website simulation tool, students build on their understanding of random processes on networks to interact with the graph of a social network of individuals and simulate the spread of a disease. They decide which two individuals on the network are the best to vaccinate in an attempt to minimize the number of people infected and "curb the epidemic." Since the results are random, they run multiple simulations and compute the average number of infected individuals before analyzing the results and assessing the effectiveness of their vaccination strategies.

Students gain experience with the software/system design process, closely related to the engineering design process, to solve a problem. First, they learn about the Mars Curiosity rover and its mission, including the difficulties that engineers must consider and overcome to operate a rover remotely. Students observe a simulation of a robot being controlled remotely. These experiences guide discussion on how the design process is applied in these scenarios. The lesson culminates in a hands-on experience with the design process as students simulate the remote control of a rover. In the associated activity, students gain further experience with the design process by creating an Android application using App Inventor to control one aspect of a remotely controlled vehicle. (Note: The lesson requires a LEGO® MINDSTORMS® Education NXT base set.)

We can regard the wider incentive structures that operate across science, such as the priority given to novel findings, as an ecosystem within which scientists strive to maximise their fitness (i.e., publication record and career success). Here, we develop an optimality model that predicts the most rational research strategy, in terms of the proportion of research effort spent on seeking novel results rather than on confirmatory studies, and the amount of research effort per exploratory study. We show that, for parameter values derived from the scientific literature, researchers acting to maximise their fitness should spend most of their effort seeking novel results and conduct small studies that have only 10%–40% statistical power. As a result, half of the studies they publish will report erroneous conclusions. Current incentive structures are in conflict with maximising the scientific value of research; we suggest ways that the scientific ecosystem could be improved.

With your mouse, drag data points and their error bars, and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, cubic, or quartic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.

With your mouse, drag data points and their error bars, and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, cubic, or quartic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.

CurvedLand is an applet for showing what the world would look like with different geometry. It is named CurvedLand in tribute to the science fiction novel, Flatland, by Edwin Abbott, which describes the adventures of a two-dimensional being who is visited by a stranger from the third dimension.

One of the central ideas of Einstein's theory of relativity is that space and time curve in response to the matter and energy within them. A curved space is one that doesn't obey the usual laws of Euclidean geometry: the angles of a triangle don't generally add up to 180 degrees, the circumference of a circle isn't pi times the diameter, parallel lines can either converge towards each other or move apart, and so on.

Since the geometry we observe is very close to Euclidean, however, it is hard for most of us to picture what this difference would mean physically. If you draw a circle and a diameter, how could the ratio be anything other than pi? To answer this question, imagine that as you move around in space the shapes of objects appear to distort. This is what happens in curved space. If you draw a circle around yourself and then start walking around it to pace out the circumference, it will look to you like you are walking along a constantly changing ellipse.

CurvedLand illustrates this distortion as it would appear in a two-dimensional curved space. The structure is similar to a mapping program. You can place objects of different shapes in different places in the world and then move around the space to see what they look like from different perspectives.

Chemical reactions play an important role in our digestive system. This activity will allow you to observe one of those reactions, known as a decomposition reaction.  Decomposition is a reaction that breaks down a complex substance into simpler substances. You will be using the enzyme amylase to break down a starch into a simple sugar. This lab will simulate what is happening in your mouth when your saliva (which contains the enzyme amylase) begins to break down the complex carbohydrate starch.

To investigate the movement of water into and out of a polymer.  Gummy Bears are made of gelatin and sugar.  Gelatin is a polymer that forms large three-dimensional matrices which give structural support to jellies and jams, and a lot of other things you use every day. This process will simulate what happens in your body cells.

Students investigate sound in their environment, particularly how sounds impact their lives. At the beginning of the unit, students use online simulations to investigate the properties of sound and learn about the components of a sound wave. They identify the different sounds in their environment and place them into categories for analysis. Students complete a project where they develop a research question, collect data in the field about different sounds, and analyze their data. They use what they have learned to create a digital product that makes recommendations about teens and sound. At the end of the unit, students share their products and take an exam over the science content.

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.

Are you ready to leave the sandbox and go for the real deal? Have you followed Data Analysis: Take It to the MAX() and Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis?

In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis.

For your project, you will pick one raw dataset out of several options, which you will turn into a dashboard. You will begin with a business question that is related to the dataset that you choose. The datasets will touch upon different business domains, such as revenue management, call-center management, investment, etc.

CODAP (Common Online Data Analysis Platform) is an open-source data visualization and analysis tool made available by the Concord Consortium. It's available at https://codap.concord.org/. CODAP can be used across the curriculum to help students summarize, visualize, and interpret data, advancing their skills to use data as evidence to support a claim.

This professional learning resource includes guides to get started, tutorials that demonstrate the features and functionality of CODAP, sample lessons, and links to online forum sites.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization.
We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses.
MITx Online
This course draws materials from 14.310x Data Analysis for Social Scientists, which is part of the MicroMasters Program in Data, Economics, and Design of Policy offered by MITx Online. The MITx Online course is entirely free to audit, though learners have the option to pay a fee, which is based on the learner’s ability to pay, to take the proctored exam and earn a course certificate. To access that course, create an MITx Online account and enroll in the course 14.310x Data Analysis for Social Scientists.

Short Description:
Data analytics is a rapidly evolving field. In today's labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, "a new online course" if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.

Long Description:
Data analytics is a rapidly evolving field. In today’s labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, “a new online course” if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.

Word Count: 2054

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Short Description:
Data analytics is a rapidly evolving field. In today's labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, "a new online course" if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.

Long Description:
Data analytics is a rapidly evolving field. In today’s labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, “a new online course” if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.

Word Count: 2038

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.
This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to-use software and cases.

A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments.
This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.