what kind of data when exploring how many kids fail a course

How to teach data science to kids?

Jun 21, 2017 · Joshua, we need to get you a job overseeing school data programs! As a teacher, relevant data included observation of student work. There’s a place for data, of course. But constantly collecting test score information isn’t very useful. Also, there are many privacy concerns with survey questions.

Can a five year old understand big data?

Jul 17, 2020 · Here is a fun activity to teach kids some of the critical concepts of data science or processes involved in Big Data. Data Collection: First, ask your kids to collect any commonly used object or thing is your house (legos, leaves, clothes, utensils, etc.). Let us consider a pile of clothes in the house. Make sure it is a collected heap of ...

Do you study data types for fun?

Oct 02, 2015 · Activity 2: Find the Perfect Dog (Ages 10-16) This activity teaches kids how to view and interact with data to answer questions or solve problems. You can also try this with younger kids if they’re already enthusiastic about data and/or have a patient parent or teacher to guide them. In this activity, kids use a data set of popular dog breed ...

How can parents and teachers get kids excited about data?

Mar 30, 2020 · In this module we'll introduce a 5 step process for approaching data science problems. Steps in the Data Science Process 3:42. Step 1: Acquiring Data 6:21. Step 2-A: Exploring Data 4:19. Step 2-B: Pre-Processing Data 8:27.

Activity 2: Find the Perfect Dog (Ages 10-16)

This activity teaches kids how to view and interact with data to answer questions or solve problems. You can also try this with younger kids if they’re already enthusiastic about data and/or have a patient parent or teacher to guide them.

Additional Resources

Want to tell your kids about other kids using data? Meet young Ben Radburn, who visualized the most thrilling roller coasters around the world.

Why is exploring data important?

In summary, what you get by exploring your data is a better understanding of the complexity of the data you have to work with. This, in turn, will guide the rest of your process.

What is the Big Data course?

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

What is Hadoop framework?

It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world!

What is the best way to look at data in a preliminary analysis?

Visualization techniques also provide a quick and effective, and overall a very useful way to look at data in this preliminary analysis step. A heat map, such as the one shown here, can quickly give you the idea of where the hotspots are. Many other different types of graphs can be used.

What is summary statistics?

Summary statistics are quantities that capture various characteristics of a set of values with a single number or a small set of numbers. Some basic summary statistics that you should compute for your data set are mean, median, range, and standard deviation. Mean and median are measures of the location of a set of values.

What is the first step after getting your data?

The first step after getting your data is to explore it. Exploring data is a part of the two-step data preparation process. You want to do some preliminary investigation in order to gain a better understanding of the specific characteristics of your data. In this step, you'll be looking for things like correlations, general trends, and outliers.

Why do we plot outliers?

Plotting outliers will help you double check for errors in the data due to measurements. In some cases, outliers that are not errors might make you find a rare event. Additionally, summary statistics provide numerical values to describe your data.

What is ordinal data?

Ordinal data. The key with ordinal data is to remember that ordinal sounds like order - and it's the order of the variables which matters. Not so much the differences between those values. Ordinal scales are often used for measures of satisfaction, happiness, and so on.

Why is interval data important?

Interval data is fun (and useful) because it's concerned with both the order and difference between your variables. This allows you to measure standard deviation and central tendency. Everyone's favorite example of interval data is temperatures in degrees celsius. 20 degrees C is warmer than 10, and the difference between 20 degrees ...

What is the difference between quantitative and qualitative data?

Quantitative vs Qualitative data - what's the difference? In short: quantitative means you can count it and it's numerical (think quantity - something you can count). Qualitative means you can't, and it's not numerical (think quality - categorical data instead). Boom!

What happens if you don't have a true zero?

You can also have negative numbers. If you don't have a true zero, you can't calculate ratios. This means addition and subtraction work, but division and multiplication don't.

Is ratio data negative?

Ratio data is very similar interval data, except zero means none. For ratio data, it is not possible to have negative values. For instance, height is ratio data. It is not possible to have negative height. If an object's height is zero, then there is no object. This is different than something like temperature.

Can temperature be divided into decimals?

Like the weight of a car (can be calculated to many decimal places), temperature (32.543 degrees, and so on), or the speed of an airplane. Now for the fun stuff.

How To Tell Your Kids About Data

A more-than-generous depiction of the proportion of data scientists to data rows. Photo from Pixabay

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I’d like to reiterate that, while the points I’ve raised have been expressed via examples in continued STEM education, I believe that using data as a motivation for early STEM education teaches valuable concepts that are extendable to interpreting information of any kind.

Advocate for Policies and Legislation

California Assemblymember Debbie Look on the Committee for Education says, “In analyzing bills that come before the California Legislature, I am often looking for data relating to the education, health, and well-being of children in California.” She uses her data analyses to illustrate the scope of a particular issue facing children and youth in California in order to advocate on their behalf.

Assess Community Needs

"I am a guest speaker at California State University, Channel Islands, for their public health nursing program on child abuse recognition. I use data from Kidsdata to present child abuse statistics to Bachelor of Science in Nursing students to give them a better understanding of the incidence of child abuse in their specific area.

Strengthen Grant Proposals

The United Way of the Bay Area uses kidsdata.org to bolster grant proposals and the agency's community library. India Swearingen, the agency's evaluation and insight director frequently uses the narrative context that accompanies every indicator.