Data Science and Computational Thinking [inc Big Data and Internet of Things]

Google has set aside a $25 million pool to fund research work by schools and other organizations using machine learning for "social good." Besides cash, the company's "AI for Social Good" project is also offering support from its artificial intelligence experts, credits and consulting from Google Cloud. Those chosen will also join a "launchpad" accelerator program with mentoring, support and access to Silicon Valley experts.

My goal was to start a career I was passionate about, despite my lack of funds. Because of this choice I have accomplished a lot over the last few months. I published my own website, was posted in a major online data science publication, and was given scholarships to a competitive computer science graduate program. In the following article, I give guidelines and advice so you can make your own data science curriculum. I hope to give others the tools to begin their own educational journey. So they can begin to work towards a more passionate career in data science. A Quick Note When I say “data science”, I am referring to the collection of tools that turn data into real-world actions. These include machine learning, database technologies, statistics, programming, and domain-specific technologies.

Australian organisations need to move more rapidly into using artificial intelligence applications to automate and augment manual processes, as well as skilling up an AI workforce as the technology becomes as fundamental to society as basic utilities like phone networks or electricity.

“We are a fair way behind the rest of the world at the moment,” says PwC Australia Analytic Intelligence Leader John Studley. “It is here though, and some organisations are making a start.”

Mr Studley points to the oft quoted Automation Advantage report that was produced by consultancy AlphaBeta and sponsored by Google.

“The reality is we're generating enormous amounts of data every single day; in two days we’re generating the same amount of information from year zero to 2005,” Mr Newman said during the Schneider Electric Innovation summit in Singapore.

This data is held in massive data centres, which are swiftly becoming the fastest-growing consumers of power in the country, and are expected to boom as demand grows for processing power for cryptocurrencies such as Bitcoin, and because every single Facebook post, tweet, and Instagram picture needs to be powered by something.

The 107 data centres dotted around Australia now consume around the same amount of electricity as the 807 Coles supermarkets (which have an annual power bill of about $250 million) and Woolworths' 995 stores around the country combined. And that hunger is only expected to grow.

Over the past decade, there has been much discussion of the term computational thinking. The term, popularized by computer scientist Jeannette Wing, is generally used to describe computer-science concepts and strategies that can be useful in understanding and solving problems in a wide range of disciplines and contexts. In a growing number of schools around the world, there are now efforts to help students develop as computational thinkers.

The program, run by Discovery Education and Tata Consultancy Services, offers teachers training in coding, developing algorithms and analyzing data.

Companies embarking on a big-data journey must realise they are engaging in a value exchange with their customers, one that requires them to tailor information to make a website or product more useful or interesting, while remaining vigilant about crossing a line when it comes to privacy or ethics violations.

There are lots of resources available to help students grow as computational thinkers— and many are free! Some of these platforms begin at basic levels and can be utilized by beginners as young as preschool age, while still others provide interactive experiences that utilize diverse computational thinking strategies.

Here are 10 useful computational thinking resources:

Data science has produced some of the greatest tech innovations in the past decade, but as practiced in many organizations it’s also completely unsustainable. Producing more relevant models, mitigating risk and keeping up with the pace of the field will require organizations to rethink how they do data science. Structuring data science as entrepreneurship makes it a great investment once again.

The problem is, data scientists and analysts are constantly talking about the potential for how to use this data, but too few are talking about the ethics. If we’re going to keep pushing for better systems built on big data, we need to democratize and popularize the ethical conversation surrounding them.

The main concerns
Most people understand the privacy concerns that can arise with collecting and harnessing big data, but the ethical concerns run far deeper than that.

Google, Facebook, Amazon and others make their profits in two main ways: by collecting as much data as possible from us and by controlling what we pay for, says online security expert Bruce Schneier. And what does this all depend on? A vulnerable internet.
Flaws in technology are not the only reason we have such an insecure internet. Another important reason — maybe even the main reason — is that the internet’s most powerful architects have manipulated the network to make it serve their own interests.

Data Science Week (DSW) has a 'distributed' organisational model, where organisations run and organise their own events but promote themselves via this website, the DSW logo and relevant hashtag (#DSW19) to create a week-long series of events that celebrate everything Data Science.  

This year we are not restricting events to Perth and welcome Expressions of Interest (EOI) from wherever you may be located.

Expression of interest

To submit your Data Science Week event for 2019, please complete the following expression of interest form

The International Society for Technology in Education (ISTE) announced its new ISTE Standards for Educators: Computational Thinking Competencies (CT Competencies) during the CSforALL Summit, which followed ISTE's first-ever No Fear Coding Lab, held in Detroit.

CT Competencies focus on the knowledge, skills and mindset needed to bring computational thinking (CT) to all K–12 content areas and are designed for all educators, including those who are new to computer science. The CT Competencies represent the first-ever approach to correlate and align the ISTE Standards for Educators, the K–12 Computer Science Framework and the Computer Science Teachers Association (CSTA) standards for students. Schools of education across the country will also use the CT Competencies to prepare new educators to lead the way with CT skills.

If you ever struggle to make decisions, here's a talk for you. Cognitive scientist Tom Griffiths shows how we can apply the logic of computers to untangle tricky human problems, sharing three practical strategies for making better decisions -- on everything from finding a home to choosing which restaurant to go to tonight.

Learning how to talk about data

McCaslin says the agency she was given made this project a more interesting and practical way of learning important concepts.

“We learned how to tell if certain regressions (linear, exponential, quadratic, cubic, etc.) were a good fit or not for our data from our residual plot,” she says. “We also learned how to go into depth on how to describe the data using a real-world example that we care about.”

Some of the subjects the kids chose are difficult problems, but Jenson feels like they are ready to have those challenging conversations. With the computational thinking skills to analyze data, they’re also ready to offer solutions.

“These are the kids who are going to change the world,” she says.

Read the transcript here https://bit.ly/2z6dtkO and subscribe to the podcast on Itunes https://apple.co/2mNTtI5. You can easily see the amount of calories in your peanut butter and your car's safety rating. But this publicly available data comes from hard-won battles - and the numbers behind many of our most crucial issues in healthcare, environment, and finance are either under lock and key -  or they are simply not even measured. In this month's podcast episode I interview Harvard University Professor Archon Fung PhD about what happens when we turn important data that is often hidden and contentious into a publicly available resource for the world to see.

IT WAS A perfect sunny summer afternoon in Copenhagen when the world’s largest shipping conglomerate began to lose its mind.

The headquarters of A.P. Møller-Maersk sits beside the breezy, cobblestoned esplanade of Copenhagen’s harbor. A ship’s mast carrying the Danish flag is planted by the building’s northeastern corner, and six stories of blue-tinted windows look out over the water, facing a dock where the Danish royal family parks its yacht. In the building’s basement, employees can browse a corporate gift shop, stocked with Maersk-branded bags and ties, and even a rare Lego model of the company’s gargantuan Triple-E container ship, a vessel roughly as large as the Empire State Building laid on its side, capable of carrying another Empire State Building–sized load of cargo stacked on top of it.

It will also involve the appointment of an Optus chair in artificial intelligence and three Optus research fellows focusing on applying AI technologies in areas such as regional telecommunications. This project aims to improve student outcomes and allocate funds for PhD scholarships and student projects, the telco said on Monday.

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.