Data Science and Computational Thinking [inc Big Data and Internet of Things]
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The thirteen Ps of big data

The thirteen Ps of big data | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Big data are often described as being characterised by the ‘3 Vs’: volume (the large scale of the data); variety (the different forms of data sets that can now be gathered by digital devices and software); and velocity (the constant generation of these data). An online search of the ‘Vs’ of big data soon reveals that some commentators have augmented these Vs with the following: value (the opportunities offered by big data to generate insights); veracity/validity (the accuracy/truthfulness of big data); virality (the speed at which big data can circulate online); and viscosity (the resistances and frictions in the flow of big data) (see Uprichard, 2013 for a list of even more ‘Vs’).
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Data Science and Computational Thinking [inc Big Data and Internet of Things]
Data and computational skills and knowledge are rapidly becoming the soft skills of the 21st century - The Age of Algorithms.
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The Infographic Is Mightier Than The Sword

The Infographic Is Mightier Than The Sword | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Infographics are often regarded as the world's most-spoken language. In the field of journalism, they have the ability to unify incredibly dense and complicated subjects in a way that is attainable for all. "The pearl of journalism," explains Alberto Lucas López, one of the most celebrated infographers of this generation. Delve into his journey and discover how he changed the style guide of National Geographic.
Kim Flintoff's insight:
"With his ability to absorb and integrate all sorts of influences and narratives—both in data visualizations and classic illustrations—Lucas López quickly felt at home in the fast-paced Hong Kong environment. It was very hard for him to leave it behind. But when the call came from National Geographic, the temptation was too strong for his impetuous career-driven mindset. Honored, and a bit overwhelmed, he said yes. He was surprised to find out that National Geographic also had a highly demanding, frenetic pace. “I went from publishing for 150,000 readers to 15 million readers. There are more filters to go through,” he says. “Now I feel more uneasy about meeting deadlines than I did when I barely had any time at all.”"
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How Data Is Informing the Development of Solutions to the COVID-19 Pandemic

How Data Is Informing the Development of Solutions to the COVID-19 Pandemic | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
The fight to slow the corona virus pandemic is underpinned by a range of scientific disciplines, including mathematics, biostatistics and … Continued
Kim Flintoff's insight:
COVID-19 is being battled on many fronts.
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15 Stunning Data Visualizations (And What You Can Learn From Them)

We’re drowning in data. Everyday, 2.5 quintillion bytes of data are created. This is the equivalent of 90% of the world’s information–created in the last two years alone. Now this is what we call…
Kathryn Dalton's curator insight, October 2, 2019 8:58 PM
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ELive! Webinar | Artificial Intelligence in Education: Legal Considerations and Ethical Questions | EDUCAUSE

ELive! Webinar | Artificial Intelligence in Education: Legal Considerations and Ethical Questions | EDUCAUSE | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
This webinar will discuss how schools and other education providers can use artificial intelligence and predictive analytics to promote student success, improve retention, streamline enrollment, and better manage resources. It covers the benefits and limitations of these emerging technologies, as well as related legal obligations related to privacy, data protection, equal protection, and discrimination law. The webinar focuses on ethical questions raised by the use of artificial intelligence tools in education, highlighting various concerns and principles raised by the government, advocates, academics, and learning-science practitioners. The webinar concludes with best practices and sample policies to guide school procurement, implementation, and oversight of machine-learning systems.

Outcomes
Investigate emerging applications for artificial intelligence in higher education
Understand the benefits and limitations of new technologies
Discuss legal considerations, including student privacy, data protection, and discrimination law
Consider extralegal ethical questions
Kim Flintoff's insight:
"This webinar will discuss how schools and other education providers can use artificial intelligence and predictive analytics to promote student success, improve retention, streamline enrollment, and better manage resources. It covers the benefits and limitations of these emerging technologies, as well as related legal obligations related to privacy, data protection, equal protection, and discrimination law. The webinar focuses on ethical questions raised by the use of artificial intelligence tools in education, highlighting various concerns and principles raised by the government, advocates, academics, and learning-science practitioners. The webinar concludes with best practices and sample policies to guide school procurement, implementation, and oversight of machine-learning systems.

Outcomes
Investigate emerging applications for artificial intelligence in higher education
Understand the benefits and limitations of new technologies
Discuss legal considerations, including student privacy, data protection, and discrimination law
Consider extralegal ethical questions"
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Computational Thinking Is Critical Thinking. And It Works in Any Subject. | EdSurge News

Computational Thinking Is Critical Thinking. And It Works in Any Subject. | EdSurge News | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Computational thinking is one of the biggest buzzwords in education—it’s even been called the ‘5th C’ of 21st century skills. While it got its start as a way to help computer scientists think more logically about data analysis, lately it’s been catching on with instructors in a diverse number of subjects—from science to math to social studies.

One reason for its emerging popularity? It’s engaging.
Kim Flintoff's insight:
Computational thinking is one of the biggest buzzwords in education—it’s even been called the ‘5th C’ of 21st century skills. While it got its start as a way to help computer scientists think more logically about data analysis, lately it’s been catching on with instructors in a diverse number of subjects—from science to math to social studies. One reason for its emerging popularity? It’s engaging.
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How Much the Everyday Changes When You Have Kids

How Much the Everyday Changes When You Have Kids | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
I compared time use for those with children under 18 against those without. Here’s where the minutes go.
Kim Flintoff's insight:
Data Visualisation that will not surprise any parent/guardian 

An interesting tidbit for students to engage with a dataset on family dynamics...
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Bio-computer example of ‘risky’ technology that should be funded: researcher

Bio-computer example of ‘risky’ technology that should be funded: researcher | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Half-living, half-synthetic bio-computers will soon be able to reason and multi-task like humans, paving the way for a world where computers can help solve ‘unsolvable’ problems, if QUT researcher Associate Professor Dan Nicolau has his way.

Nicolau, who recently published a paper in the Royal Society’s Interface Focus, was awarded a $978,125 Australian Research Council Future Fellowship last year to develop the technology he hopes will disrupt computation – a living, breathing device made from living things.
Kim Flintoff's insight:
"Half-living, half-synthetic bio-computers will soon be able to reason and multi-task like humans, paving the way for a world where computers can help solve ‘unsolvable’ problems, if QUT researcher Associate Professor Dan Nicolau has his way. Nicolau, who recently published a paper in the Royal Society’s Interface Focus, was awarded a $978,125 Australian Research Council Future Fellowship last year to develop the technology he hopes will disrupt computation – a living, breathing device made from living things."
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A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One

Data science is an immensely powerful tool in our data-driven world. Call me idealistic, but I believe this tool should be used for more than getting people to click on ads or spend more time consumed by social media.
In this article and the sequel, we’ll walk through a complete machine learning project on a “Data Science for Good” problem: predicting household poverty in Costa Rica. Not only do we get to improve our data science skills in the most effective manner — through practice on real-world data — but we also get the reward of working on a problem with social benefits.
Kim Flintoff's insight:
Data science is an immensely powerful tool in our data-driven world. Call me idealistic, but I believe this tool should be used for more than getting people to click on ads or spend more time consumed by social media.
In this article and the sequel, we’ll walk through a complete machine learning project on a “Data Science for Good” problem: predicting household poverty in Costa Rica. Not only do we get to improve our data science skills in the most effective manner — through practice on real-world data — but we also get the reward of working on a problem with social benefits.
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Designing 2030 is designing for the future today –

Designing 2030 is designing for the future today – | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
What kinds of experiences should school-aged learners have with data? What must be done now and anticipated in the future in order to make these experiences possible?
Kim Flintoff's insight:
What kinds of experiences should school-aged learners have with data? What must be done now and anticipated in the future in order to make these experiences possible?
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Pedagogy of Play Blog

Pedagogy of Play Blog | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Play and playfulness have caught the attention of educators around the world. Play and playfulness in schools?! As we struggle to prepare students for a quickly changing world filled with uncertainty, the risk-taking, imagining, inventing and learning from mistakes that learning through play fosters are essential dispositions that schools must promote. The Pedagogy of Play (PoP) project offers guidance and inspiration for educators asking the question: how can we bring more play and playfulness into our classrooms and schools? We are working on a framework that supports teachers and school leaders in creating cultures where playful learning thrives.
Kim Flintoff's insight:
"Play and playfulness have caught the attention of educators around the world. Play and playfulness in schools?! As we struggle to prepare students for a quickly changing world filled with uncertainty, the risk-taking, imagining, inventing and learning from mistakes that learning through play fosters are essential dispositions that schools must promote. The Pedagogy of Play (PoP) project offers guidance and inspiration for educators asking the question: how can we bring more play and playfulness into our classrooms and schools? We are working on a framework that supports teachers and school leaders in creating cultures where playful learning thrives."
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5 Resources to Inspire Your Next Data Science Project

Have you ever wanted to start a new project but you can’t decide what to do? First, you spend a couple hours brainstorming ideas. Then days. Before you know it, weeks have gone by without shipping anything new. 


This is extremely common for self-driven projects in all fields; data science is no different. It’s easy to have grand ambitions but much more difficult to execute on them. I’ve found the hardest part of a data science project is getting started and deciding which path to go down. 

 
In this post, my intention is provide some useful tips and resources to springboard you into your next data science project.

Kim Flintoff's insight:
In this post, my intention is provide some useful tips and resources to springboard you into your next data science project.
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Hands-on Machine Learning Model Interpretation –

Interpreting Machine Learning models is no longer a luxury but a necessity given the rapid adoption of AI in the industry. This article in a continuation in my series of articles aimed at ‘Explainable Artificial Intelligence (XAI)’. The idea here is to cut through the hype and enable you with the tools and techniques needed to start interpreting any black box machine learning model. Following are the previous articles in the series in case you want to give them a quick skim (but are not mandatory for this article).
Kim Flintoff's insight:
Interpreting Machine Learning models is no longer a luxury but a necessity given the rapid adoption of AI in the industry. This article in a continuation in my series of articles aimed at ‘Explainable Artificial Intelligence (XAI)’. The idea here is to cut through the hype and enable you with the tools and techniques needed to start interpreting any black box machine learning model. Following are the previous articles in the series in case you want to give them a quick skim (but are not mandatory for this article).
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How to Data Science without a Degree –

I want to show you how to become a Data Scientist without a degree (or for free). Ironically, I do have a degree — one that was even made for Data Science (Master’s in Analytics from Northwestern)…
Kim Flintoff's insight:
"I want to show you how to become a Data Scientist without a degree (or for free)."
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Google Engineers 'Mutate' AI to Make It Evolve Systems Faster Than We Can Code Them

Google Engineers 'Mutate' AI to Make It Evolve Systems Faster Than We Can Code Them | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to cre
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Data science should learn to speak domain languages

Data science should learn to speak domain languages | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
HSE University’s Laboratory of Methods for Big Data Analysis utilises machine learning to benefit academia and industry
Kim Flintoff's insight:
"HSE University’s Laboratory of Methods for Big Data Analysis utilises machine learning to benefit academia and industry The digital revolution is accelerating, and data form the backbone of innovation and industrial development. As a result, governments and businesses are developing machine-learning algorithms and other data-driven techniques that offer new services and products to citizens. Science is also benefitting from an explosion in data, as researchers in different fields generate information in unprecedented quantities. But off-the-shelf data analysis solutions are often not up to the complicated rigour required by scientists, which is where the Laboratory of Methods for Big Data Analysis (LAMBDA) comes in."
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AI Needs Your Data—and You Should Get Paid for It

AI Needs Your Data—and You Should Get Paid for It | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
A new approach to training artificial intelligence algorithms involves paying people to submit medical data, and storing it in a blockchain-protected system.
Kim Flintoff's insight:
There are many emerging models for more ethical data acquisition.  Some include giving you free access to the systems that generate the data (genomic information for example) and giving you ownership of that data and then paying you to use the data when its required in various studies and applications.
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No coding required: Companies make it easier than ever for scientists to use artificial intelligence

No coding required: Companies make it easier than ever for scientists to use artificial intelligence | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it

The trend toward off-the-shelf AI has risks. Machine learning algorithms are often called black boxes, their inner workings shrouded in mystery, and the prepackaged versions can be even more opaque. Novices who don't bother to look under the hood might not recognize problems with their data sets or models, leading to overconfidence in biased or inaccurate results.
Kim Flintoff's insight:
"The trend toward off-the-shelf AI has risks. Machine learning algorithms are often called black boxes, their inner workings shrouded in mystery, and the prepackaged versions can be even more opaque. Novices who don't bother to look under the hood might not recognize problems with their data sets or models, leading to overconfidence in biased or inaccurate results."
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Big Data Science: Establishing Data-Driven Institutions through Advanced Analytics | EDUCAUSE

Big Data Science: Establishing Data-Driven Institutions through Advanced Analytics | EDUCAUSE | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
Data analytics can drive decision-making, but to optimize those decisions, stakeholders must couple effective methods with a shared understanding of both the domain and the institutional goals.
Kim Flintoff's insight:
"From improving student success to forming optimal strategies that can maximize corporate and foundational relationships, data analytics is now higher education's divining rod. Faculty and administration alike make daily decisions that impact the future of our institutions and our students. Departments establish curriculums; labs invest in new technologies; we admit students, hire faculty, monitor meal plans, and define security protocols. How can we optimize those decisions over the coming years? How do we know if we are meeting our goals? Can we use our data to make better decisions? I think the answer is yes, but only if we couple the use of state-of-the-art analytical methods with a focused approach to how and when we engage our data to make decisions. Our data strategy must reflect not only our institutional goals, but also the novel ways in which we can now collect and analyze data to attain those goals. Part of my role as a data scientist at Cornell University is to help guide this strategy by establishing a common understanding of, and vocabulary around, the data-driven decision-making process."
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Breaking Down the 8 Key Concepts of Computational Thinking

Breaking Down the 8 Key Concepts of Computational Thinking | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it

However, there are still many more educators who do not feel comfortable with computational thinking concepts than those who do. To address this, Digital Promise has led workshops with hundreds of teachers from all academic disciplines to introduce them to computational thinking practices. Drawing from both learning sciences research and feedback from educators, we developed this framework to support teachers in identifying where their students can leverage computational thinking. Within these eight key concepts, teachers of science, math, language arts, social studies, and art have found intersections with what their students are expected to know and know how to do.

Kim Flintoff's insight:
However, there are still many more educators who do not feel comfortable with computational thinking concepts than those who do. To address this, Digital Promise has led workshops with hundreds of teachers from all academic disciplines to introduce them to computational thinking practices. Drawing from both learning sciences research and feedback from educators, we developed this framework to support teachers in identifying where their students can leverage computational thinking. Within these eight key concepts, teachers of science, math, language arts, social studies, and art have found intersections with what their students are expected to know and know how to do.
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IoT Programming and Big Data

IoT Programming and Big Data | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
About this course Skip Course Description
The Internet of Things is creating massive quantities of data, and managing and analysing it requires a unique approach to programming and statistics for distributed data sources.

This course will teach introductory programming concepts that allow connection to, and implementation of some functionality on, IoT devices, using the Python programming language. In addition, students will learn how to use Python to process text log files, such as those generated automatically by IoT sensors and other network-connected systems.

Learners do not need prior programming experience to undertake this course, and will not learn a specific programming language - however Python will be used for demonstrations. This course will focus on learning by working through realistic examples.

What you'll learn
Appreciate the software needs of an IoT project
Understand how data is managed in an IoT network
Apply software solutions for different systems and Big Data to your IoT concept designs
Create Python scripts to manage large data files collected from sensor data and interact with the real world via actuators and other output devices.
Kim Flintoff's insight:
This course will teach introductory programming concepts that allow connection to, and implementation of some functionality on, IoT devices, using the Python programming language. In addition, students will learn how to use Python to process text log files, such as those generated automatically by IoT sensors and other network-connected systems.
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Dynamic Data Science

Dynamic Data Science | Data Science and Computational Thinking [inc Big Data and Internet of Things] | Scoop.it
The age of data is upon us! Complex datasets underpin nearly every aspect of modern life, demanding data fluency by all students. This set of dynamic data science activities is designed for grades 5-14. By working with data frequently and repeatedly, learners develop experience and competence, gaining fluency with the data moves necessary for structuring, examining, and diving into data, and ultimately building excitement for their ability to work with data.
Kim Flintoff's insight:
The age of data is upon us! Complex datasets underpin nearly every aspect of modern life, demanding data fluency by all students. This set of dynamic data science activities is designed for grades 5-14. By working with data frequently and repeatedly, learners develop experience and competence, gaining fluency with the data moves necessary for structuring, examining, and diving into data, and ultimately building excitement for their ability to work with data.
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Machine Learning Kaggle Competition Part One: Getting Started

In the field of data science, there are almost too many resources available: from Datacamp to Udacity to KDnuggets, there are thousands of places online to learn about data science. However, if you are someone who likes to jump in and learn by doing, Kaggle might be the single best location for expanding your skills through hands-on data science projects.
Kim Flintoff's insight:
In the field of data science, there are almost too many resources available: from Datacamp to Udacity to KDnuggets, there are thousands of places online to learn about data science. However, if you are someone who likes to jump in and learn by doing, Kaggle might be the single best location for expanding your skills through hands-on data science projects.
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Essential Cheat Sheets for Machine Learning and Deep Learning Engineers

Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have right resources to learn from. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from different sources. Do visit the Github repository, also, contribute cheat sheets if you have any. Thanks.
Kim Flintoff's insight:
Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have right resources to learn from. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from different sources. Do visit the Github repository, also, contribute cheat sheets if you have any. Thanks.
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No, Machine Learning is not just glorified Statistics

The main point to address, and the one that provides the title for this post, is that machine learning is not just glorified statistics—the same-old stuff, just with bigger computers and a fancier name. This notion comes from statistical concepts and terms which are prevalent in machine learning such as regression, weights, biases, models, etc. Additionally, many models approximate what can generally be considered statistical functions: the softmax output of a classification model consists of logits, making the process of training an image classifier a logistic regression.
Kim Flintoff's insight:
“The main point to address, and the one that provides the title for this post, is that machine learning is not just glorified statistics—the same-old stuff, just with bigger computers and a fancier name. This notion comes from statistical concepts and terms which are prevalent in machine learning such as regression, weights, biases, models, etc. Additionally, many models approximate what can generally be considered statistical functions: the softmax output of a classification model consists of logits, making the process of training an image classifier a logistic regression.”
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How to Learn Data Science: Staying Motivated. –

Over the last few weeks, I’ve taken a break from writing to focus on applying to internships. But as I was driving to class today, a question began to bother me.
Kim Flintoff's insight:
Advice on how to be more consistent in your educational journey.
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