Alice decides to do some quick analysis on the trends using Kaggle Data Science survey to see what backgrounds do the current Data Science practitioners have. A majority of data scientists have college degrees, infact a majority of them have a Masters degree. So Alice would do well to go to college. But Alice is also curious of the importance of getting a degree if she wants her dream job in her dream country. Let's look at those patterns.

In Artificial Unintelligence: How Computers Misunderstand the World, Meredith Broussard adds to the growing literature exploring the limits of artificial intelligence (AI) and techno-solutionism, furthermore showing how its socially-constructed nature replicates existing structural inequalities. Calling for greater racial and gender diversity in tech, the book offers a timely, accessible and often entertaining account that sets the record straight on what current approaches to AI are and are not capable of delivering, writes Nikita Aggarwal. This post originally appeared on LSE Review of Books. If you would like to contribute to the series, please contact the managing editor of LSE Review of Books, Dr Rosemary Deller, at lsereviewofbooks@lse.ac.uk. You don't need to speak Italian to know that something's not quite right.

Wouldn't it be fabulous to know for sure that an article you read online is authentic and contains trusted sources? If everyone used AI to fact check, fake news and data could be eliminated permanently from online news sites. Menlo Park, CA-based AI startup, Diffbot has announced an official partnership with the European Journalism Centre to combat fake news. The company is the only other US company aside from Microsoft and Google to crawl and index the entire web to create its Knowledge Graph. Journalists can access the DKG through the Data Journalism platform created by the European Journalism Centre to provide resources, materials, online courses and community forums for data journalists all over the world.

At RADAR, journalists develop templates for AI to fill in with local data. These templates include "fragments of text and logical if-then-else rules for how to translate the data into location-specific text," according to its website. Data journalists have to come up with various angles and storylines, and then do some reporting to "add broad-strokes background information and national context, which are written into a template with a basic story structure." The result is stories with a similar core structure but with local details. Using AI, one data journalist can produce around 200 regional stories for each template every week, RADAR said.

"There are lots of things on the internet that are popular but not good," says Meredith Broussard, "like racism or ramen burgers." Yet we are increasingly being offered the popular and not the good, and that's just one negative effect of believing that the technological solution is always better one. Broussard has been a data journalist and software developer, and she is now the author of Artificial Unintelligence: How Computers Misunderstand the World, out now from MIT Press. Broussard has been programming since she was 11, but she has come to realize that the long-held utopian promises haven't delivered. Now, she says, it's time to be more critical, think about the limits of computers, and stop excluding equations from the system.

Data science is a team sport. This sentiment rings true not only with our experiences within IBM, but with our enterprise customers, who often ask us for advice on how to structure data science teams within their own organizations. Before that can be done, however, it's important to remember that the various skills required to execute a data science project are both rare and distinct. That means we need to make sure that each team member can focus on what he or she does best. While each role is certainly distinct, each team member does need to have T-shaped skills -- meaning they'll need to have depth in their own role but also a cursory understanding of the adjacent roles.

This is a class in data visualization. But before we leap into making charts and maps, we'll consider the nature of data, and some basic principles that will help you to "interview" datasets to find and tell stories. This is not a class in statistics, but I will introduce a few fundamental statistical concepts, which hopefully will stand you in good stead as we work to visualize data over the next few weeks -- and beyond. We're often told that there are "lies, damned lies, and statistics." But data visualization and statistics provide a view of the world that we can't otherwise obtain. They give us a framework to make sense of daunting and otherwise meaningless masses of information. The "lies" that data and graphics can tell arise when people misuse statistics and visualization methods, not when they are used correctly. The best data journalists understand that statistics and graphics go hand-in-hand. Just as numbers can be made to lie, graphics may misinform if the designer is ignorant of or abuses basic statistical principles. You don't have to be an expert statistician to make effective charts and maps, but understanding some basic principles will help you to tell a convincing and compelling story -- enlightening rather than misleading your audience. I hope you will get hooked on the power of a statistical way of thinking. As data artist Martin Wattenberg of Google has said: "Visualization is a gateway drug to statistics." Download the data for this session from here, unzip the folder and place it on your desktop.