UTS-AEI

Adrienne is a Marvel movie fanatic: Her favorite films all involve the Hulk, Thor or Black Panther. Brandon prefers animated features like Inside Out, The Incredibles and anything with Buzz Lightyear. I like both kinds, although I'm probably closer to Adrienne than Brandon. And I might skew a little toward Cora, who loves thrillers like Get Out and The Shining. Whose movie preferences are closest to yours: Adrienne's, Brandon's or Cora's? And how far are your cinematic tastes from those of the other two? It might seem strange to ask "how far" here. That's a question about distance, after all. What does distance mean when it comes to which movies you like? How would we measure it?

Journalists know they need to get better with data and statistics, but they have a long way to go Only 25 percent of journalists surveyed said they were "very" well equipped to interpret statistics from sources, and only 11 percent said the same about doing statistical analysis themselves.

When creating a machine-learning algorithm, designers have to make many choices: what data to train it on, what specific questions to ask, how to use predictions that the algorithm produces. These choices leave room for discrimination, particularly against people who have been discriminated against in the past. For example, training an algorithm to select potential medical students on a data set that reflects longtime biases against women and people of color may make these groups less likely to be admitted. In computing, the phrase "garbage in, garbage out" describes how poor-quality input leads to poor-quality output. In this case we might say, "White male doctors in, white male doctors out."

Most of the poor people in the world are women. In no country on earth are women economically equal to men, and Australia is no exception. Research from Acoss and the University of New South Wales last year showed that a higher share of people living in poverty in Australia are women. The experience of living below the breadline in our very wealthy nation is a gendered one, for reasons that are complex and intertwined. As women progress through life, they encounter a series of barriers and setbacks that simply do not encumber men in the same way. The cause of gendered poverty is structural. It is entrenched in our workplace settings, and embedded in our personal relationships. It is at play at every stage of a woman's life, from childhood to the grave, making its mark on our education, our employment, our homes, our familial responsibilities and our retirement options. At its heart is the simple fact that women do the lion's share of caring for others. Caring is women's work, and our society does not value women's work.

Nearly every ad you see online is tailored just for you. These digital ads are powered by vast, hidden datasets that allow advertisers to make eerily accurate guesses about who you are, where you've been, how you feel and what you might do next. While targeted ads may be familiar by now, how they work - and the power they have - often seems invisible. We decided to lift the veil on this part of the internet economy, so we bought some ad space. We picked 16 categories (like registered Democrats or people trying to lose weight) and targeted ads at people in them. But instead of trying to sell cars or prescription drugs, we used the ads to reveal the invisible information itself.

In our teens and early twenties, we're still figuring out what we want to be when we grow up. As we get older, we start to settle into a career. In between, we switch jobs in the search. Based on data from the Current Population Survey, this is when people make the switches and the jobs they switch to.The chart above shows the rate by age, relative to the total number of people who switched to each job. So you see a lot of switching in the early years, and then things seem to settle down at older ages. If someone takes a new job when they're older, it tends towards management or jobs that require more education.

Australian farms generate huge volumes of agricultural data. Examples include the types of crops being grown, crop yields, livestock numbers and locations, types of fertilisers and pesticides being used, soil types, rainfall and more. This data is typically collected through the use of digital farming machinery and buildings featuring robotics and digital technologies, artificial intelligence, and devices connected to the internet ("internet of things", or IoT). But a recent review from the Australian Bureau of Statistics and the Australian Bureau of Agricultural and Resource Economics highlights the patchy and fragmented nature of existing government and industry approaches to agricultural data. What that means is Australian farmers are currently not adequately protected from their farm data being collected and used without their knowledge or consent.

Interesting discussion of wealth vs income inequality, and our perceptions of wealth inequality and how to tackle it. The Essential Report survey highlights the lack of awareness most of us have about the level of wealth inequality that exists, and also our confusion over what policies are best suited to dealing with it.

Most people pass through some type of public space in their daily routine - sidewalks, roads, train stations. Thousands walk through Bryant Park every day. But we generally think that a detailed log of our location, and a list of the people we're with, is private. Facial recognition, applied to the web of cameras that already exists in most cities, is a threat to that privacy. To demonstrate how easy it is to track people without their knowledge, we collected public images of people who worked near Bryant Park (available on their employers' websites, for the most part) and ran one day of footage through Amazon's commercial facial recognition service.

Excellent NYT interactive - where do you draw the line on how your data is used by social media companies and smart devices?

At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes - and other people may be able to learn from them, too. After a deep dive into our archive, I found several instructive examples. I grouped our crimes against data visualisation into three categories: charts that are (1) misleading, (2) confusing and (3) failing to make a point. For each, I suggest an improved version that requires a similar amount of space - an important consideration when drawing charts to be published in print.

Aleks Krotoski explores why science is being drowned out by Snake Oil online, and how the balance can be shifted to keep desperate people from being exploited. But despite there being more scientific information online than ever, in the modern day the power of the internet has completely flipped. Verified science and medicine are crowded out by a plethora of misinformation and snake oil salesmen. From the relatively harmless quackery such as infrared light treatments or 'wellness' focused diets, to conspiracy theories around vaccinations that are influencing political policy, and have resulted in outbreaks of dangerous, preventable diseases across the world - what is happening online is having a tangible impact across the globe. Aleks Krotoski explores how the infrastructure of the internet allows medical misinformation to thrive, finds out how people can be drawn into communities centred around medical misinformation and conspiracy theory, and how both scientists and every day internet users can redress the balance online.

Any great story means visualization and detail. It takes the small additions of those details to build a picture in someone's mind to truly make the story complete. The same goes for analytics and data. Data is just a collection of numbers until you turn it into a story. Showing reports and dashboards can be overwhelming without adding a narrative to the data. Any great insight explains what happened, why it is important and how you can use it to turn it into something actionable. Data visualization is using data and statistics in creative ways to show patterns and draw conclusions about a hypothesis, or prove theories, that can help drive decisions in the organization.

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google's Chief Economist Dr. Hal R.Varian stated, "The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that's going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian's astute assessment.

Whether it's quitting smoking, reducing alcohol intake or making healthier dietary choices, many of us have habits we'd like to change. But it's really hard to know which treatment path to take. To advise their patients on the best of course of action, doctors sometimes compare treatments using something called the "number needed to treat" (NNT). In deciding whether to embark on a course of treatment, NNT can help. But the term is easily misunderstood by patients, and doctors as well. So it's useful to break down what NNT means.

When talking about the gender pay gap people tend to talk about the median figure rather than the mean. The mean is calculated by adding up all of the wages of employees in a company and dividing that figure by the number of employees. This means the final figure can be skewed by a small number of highly paid individuals. The median is the number that falls in the middle of a range when everyone's wages are lined up from smallest to largest and is more representative when there is a lot of variation in pay.

My name is Mary. This is quite a popular girla name in many countries. So, since childhood I've rarely been surprised to meet my namesake in daily life. But movies were a different matter! However, after hundred of watched movies and TV series I assumed that most of the characters had pretty ordinary names as well. James Bond, Jeff Lebowski and Sarah Connor hardly could impress anybody with their first names. Does it mean that common for real life names are popular in the cinema world as well? To find out the answer, I took 50 most popular female and male US names for each decade since 1960 and compared their frequency in real life with popularity in movies & TV shows released in the same years.

There's one particularly cunning type of pricing strategy that marketers use to get you to switch your choice from one option to a more expensive or profitable one. It's called the decoy effect. Imagine you are shopping for a Nutribullet blender. You see two options. The cheaper one, at $89, promotes 900 watts of power and a five-piece accessory kit. The more expensive one, at $149, is 1,200 watts and has 12 accessories. Which one you choose will depend on some assessment of their relative value for money. It's not immediately apparent, though, that the more expensive option is better value. It's 50% more powerful but costs almost 80% more. It does have more than twice as many plastic accessories, but what are they worth? Now consider the two in light of a third option. This one, for $125, offers 1,000 watts and nine accessories. It enables you to make what feels like a more considered comparison. For $36 more than the cheaper option, you get four more accessories and an extra 100 watts of power. But if you spend just $24 extra, you get a further three accessories and 200 watts more power. Bargain! You have just experienced the decoy effect.

For decades, researchers and physicians tended to think about pregnancies as either planned or unplanned. But new data reveals that for a significant group of women, their feelings don't neatly fit into one category or another. As many as one-fifth of women who become pregnant aren't sure whether they want a baby. This fact may reshape how doctors and policymakers think about family planning. For women who are unsure, it doesn't seem enough for physicians to counsel them on pregnancy prevention or prenatal care. "In the past we thought of it as binary, you want to be pregnant or not, so you need contraception or a prenatal vitamin," said Maria Isabel Rodriguez, an obstetrician-gynecologist at Oregon Health and Science University whose research focuses on family planning and contraceptive policy. "But it's more of a continuum." The new data comes from a recent change in the Centers for Disease Control and Prevention's big survey of new mothers, now allowing them to answer a question about their pregnancy desires by saying "I wasn't sure." It shows that some women want to avoid making a decision about becoming pregnant, or have strong but mixed feelings about it.

University students, like many in society, demonstrate bias against women and particularly women from non-English speaking backgrounds. That's the take home message from a new and comprehensive analysis of student experience surveys. The study examined a large dataset consisting of more than 500,000 student responses collected over 2010 to 2016. It involved more than 3,000 teachers and 2,000 courses across five faculties at the University of New South Wales (UNSW), Sydney.