When Facebook announced a tweak to its news feed algorithm Wednesday, dating apps were put in a bind. The change that would soon prioritize friends' posts over those from publishers quickly led media industry types to declare it, once again, the end of media. But Facebook's decision doesn't solely affect news outlets. More than 50 million businesses use Facebook Pages -- from big brands like McDonald's and Nike to small shop owners to startups building the next top smartphone app. The downgrade could encourage more page owners to pay Facebook to boost their posts into the news feeds of users.

Over the course of our 12 week intensive program, Metis data science students build five projects using machine learning and statistical modeling techniques in Python, industry-level visualizations in D3, and real-world data in cloud-based SQL, no-SQL, and Hadoop databases. Their 5th and final project, built from scratch, is the project you'll see at Career Day. People come to Metis with a variety of backgrounds: about 75% come from industry, and about 50% hold advanced degrees. After successful completion of our 12 week bootcamp, our graduates have accepted data science jobs with companies such as Tesla, IBM Watson, Spotify, Capital One Labs, Target, GrubHub, Booz Allen Hamilton, WeWork, Buzzfeed, Beachbody, and more.

Natural Language Processing (NLP) of texts has been applied with different degrees of success. For example, automatic translation has attracted a lot of attention in the early stages of NLP. Nowadays, with the advent of social networks, users generate a big volume of interesting information for companies which are either in the search of user feedback for they products or in the search of personalised information to sell new ones. Thus, new NLP interesting applications appear such as sentiment analysis (extracting opinions in a user opinion about a product), user wants and needs detection or user profiling. Humans cannot process this information timely without great effort and money expenditures and computers stand up as the only alternative as they are much faster than humans.

For books added in 2016, click here. And if you are interested to see what kind of books were published just 5 years ago, click here: you will see that hot topics are changing over time, with less data mining, more deep learning and Python, just to give an example. You can also use the search box (on the top right corner on any DSC webpage) to find books on a specific topic, such as books on deep learning.

In a time where we have 3D printed rockets and genome sequencing for 1000, why are our methods for identifying and understanding emerging technology trends so abysmally antiquated? A prime example is in the below graphic of industry analyst estimates of 2 markets -- virtual reality and drones. For some reason, we're forced to settle for the random guesses of the Pundit Industrial Complex. You might compare it to throwing darts, but that would be insulting to darts. Even harder than forecasting the size of a market people already know about is the early identification of emerging technologies you should be aware of.

Investors backed more AI companies in the first quarter of 2016 (Q1'16) than in any other quarter, according to research from venture capital analysis firm CB Insights, which supports the idea that AI is the next major revolution in computing. In Q1'16, there were over 140 deals to startups focused on AI, CB Insights data wrote on its blog on Tuesday. Data startup Trifacta, DNA testing startup Pathway Genomics, and cognitive computing business Digital Reasoning Systems were among the AI-powered companies that raised equity funding rounds in Q1 from investors including Goldman Sachs, Accel Partners, Greylock Partners, and the IBM Watson Group. So far in 2016, more than 200 AI-focused companies have collectively raised nearly 1.5 billion ( 1 billion). The pick up in AI funding activity comes as businesses look to make their platforms and systems more human-like.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Artificial intelligence – fuelled by big data – and blockchain tech, are some of the technologies that could impact our world, according to Sarwant Singh, senior parter at Frost & Sullivan. Speaking at the Global Community of Growth, Innovation and Leadership (GIL Europe) conference, held in London today, Singh discussed mega trends in the technological world in an attempt to examine their implications on businesses and societies. "I believe we are now entering the cognitive area," said Singh in reference to artificial intelligence, adding: "We'll move towards super intelligence [in the future]. It is not available today". By super intelligence, Singh was referring to the possibility that computers may eventually be able to function similarly to the human brain.

Chicks born with a bit of quail brain spliced in. Rats with their brains synced to create a mind-meld computer. For two days in June, some of neuroscience's most extraordinary feats were debated over coffee and vegetarian food at the Institute for Research in Cognitive Science in Philadelphia. The idea wasn't to celebrate these accomplishments but to examine them. Martha Farah, a cognitive neuroscientist at the University of Pennsylvania, assembled a group of scientists, philosophers and policy-makers to discuss the moral implications for the animals involved.

If you've read our introduction to Python, you already know that it's one of the most widely used programming languages today, celebrated for its efficiency and code readability. As a programming language for data science, Python represents a compromise between R, which is heavily focused on data analysis and visualization, and Java, which forms the backbone of many large-scale applications. This flexibility means that Python can act as a single tool that brings together your entire workflow. Python is often the choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. Its combination of machine learning libraries and flexibility makes Python uniquely well-suited to developing sophisticated models and prediction engines that plug directly into production systems.