Tools Archives - ��Ӱֱ��

CODAP

Anne Patel — Thu, 05 May 2022 03:37:36 +0000

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Zoom In – Data Science skills

Anne Patel — Wed, 26 Aug 2020 21:11:49 +0000

is an integrated platform which uses data to build students scientific understanding. Great for teachers looking at cross-cutting skills to engage learners and develop evidence based reasoning.

There are large datasets, that can be analysed in CODAP (free online data analysis tool), plus science investigations, lesson plans and rubrics to help monitor students progress.

“In each unit students learn to read, interpret and find patterns in data displayed in linked sets of tables, graphs, and maps. The��Zoom In! platform supports the development of important analytical skills – assessing evidence in relation to claims, considering issues of data quality, and exploring alternative data interpretations – and helps users to articulate their findings in writing.”

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DataVizProject – Ferdio

Anne Patel — Tue, 25 Aug 2020 04:51:57 +0000

What? The offers the ability to easily create your own data visualizations. Check out the tabs at the top first, which make some great data science concept links.

Why? Because data is better than anecdote and “every piece of statistical information needs a representation—that is, a form. Some forms tend to cloud minds, while others foster insight” (Gigerenzer & Edwards, 2003, p. 258).

Who?�� – are an infographic and data visualization agency in Copenhagen. They add value to data and information by telling visual stories that inform and inspire. In other words you send them your data they analysis it, and tell you stuff you may not know already. This is where the saying “Statisticians get to play in other peoples back yards” comes from.

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Awash in Data

Anne Patel — Fri, 21 Aug 2020 03:40:38 +0000

, is a free open-source e-book authored by the fabulous Tim Erickson. Designed for both teachers and students, this rich, interactive resource walks users through introductory lessons in data science with�� (Common Online Data Analysis Platform). Embedded CODAP documents bring each lesson to life. Give it a go – create a data science club at your school!

From Awash in Data…

Introduction

This book leads you through a few introductory lessons in data science. You can think of it as a self-guided textbook for teachers or students. If you’re a teacher, you might assign chapters, problems, or projects for students to read and do.

In this book, you will use��, the Common Online Data Analysis Platform, to do your data analysis. CODAP is free and web-based, that is, it runs in your browser. You do not even need to sign in or make an account to use it.

Smelling Like Data Science

Data Science is becoming a big deal in our society. It’s a hot profession with lots of well-paid jobs. Even if you are not a data scientist, data science is in your life. Every time you do a web search, get directions on your phone, or see a recommendation for a movie, a song, or a brand of ketchup, somebody did some data science to bring you that information. When you hear about the latest unemployment figures or the trends in income inequality, that comes from data science too. And when you hear people worry that some technological convenience will lead to greater surveillance and loss of privacy, once again, data science would make it possible.

It sounds like we had better learn about data science—but it must be super complicated, right? Data science involves huge data sets, and sophisticated computing techniques like machine learning and artificial intelligence. Doesn’t it take years of study—and buckets of talent—to learn that stuff?

As with anything, it does take years to be an expert. And experts may disagree about talent. But there are underlying ideas and ways of thinking that you can experience right now—and that’s what we hope this book will give you. We’ll use medium-sized data sets—at most a few thousand cases at a time, along with a few common-sense techniques, and a drag-and-drop data platform, to help you get an idea of what data science, for lack of a better term,��smells like. When you’re done, you’ll be able to use that “sniff test” to recognize a data science problem; you’ll have a better idea what went into the data that you see and use, making you a more critical and competent citizen; and you’ll be better able to study data science in earnest, if you so desire.

How should we start?

And even before that, what��is��data science, really?

As of the Spring of 2020, the COVID Spring, no one really knows. Everyone pretty much agrees that it lives somewhere in the borderlands between Statistics and Computer Science.

We can use our emerging sniff test to recognize it. We’ll use two main ideas:

A data science problem often begins with a feeling of being��awash in data.
Data science uses��data moves��to manipulate data.

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ClassCalc – web-based graphics calculator

Anne Patel — Thu, 18 Jun 2020 00:01:43 +0000

ClassCalc is a with full support for all STEM & Stats subjects. Online tutorials are available, such as this video explaining some of the , ClassCalc also produces box plot, histograms, regressions and statistical tests.

ClassCalc has a built-in lockdown browser (US$12/student/year) that lets teachers lock student phones onto ClassCalc during class & tests minimising cheating/distractions (even remotely!).

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Probability distribution explorer

Chris Wild — Sat, 13 Jun 2020 10:38:21 +0000

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StatKey

Anne Patel — Mon, 06 Apr 2020 03:20:07 +0000

The Lock’s are a statistical family. They developed the StatKey tool to accompany their texts. StatKey has a range of tools for most statistical test. These are accompanied by video tutorials and presentations by the Lock family. Datasets (US) are embedded or there are features to import your own.

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Data Science for Everyone: The Truth ��Ӱֱ�� Cats and Dogs

Anne Patel — Thu, 09 Jan 2020 02:29:14 +0000

Data science is all about integrating statistical and computational thinking with data. Anna Fergusson’s (University of Auckland) hands-on workshop explored one of the learning tasks Anna designed to introduce students to measures of popularity on the web and APIs.

Participants used familiar statistics tools and approaches, such as data cards, collaborative group tasks and sampling activities, and also try out some new computational tools for learning from data. Statistical concepts covered include features of data distributions and informal inference.��

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SCAMPY

Chris Wild — Thu, 12 Sep 2019 00:12:12 +0000

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Eikosogram

Chris Wild — Mon, 15 Apr 2019 04:52:43 +0000

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