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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.
The fight to slow the corona virus pandemic is underpinned by a range of scientific disciplines, including mathematics, biostatistics and … Continued
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…
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
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.
I compared time use for those with children under 18 against those without. Here’s where the minutes go.
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.
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.
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?
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.
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.
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).
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)…
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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
HSE University’s Laboratory of Methods for Big Data Analysis utilises machine learning to benefit academia and industry
A new approach to training artificial intelligence algorithms involves paying people to submit medical data, and storing it in a blockchain-protected system.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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