This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.

High-quality, low-cost online courses could be used to shift schools away from being "exam factories" and help students keep pace with the threat of automation, according to a new report by the Institute of Directors. The report argues that the internet allows schools to be more flexible and adapt learning towards "a future in which more and more work is taken over by robots or computers". Related: Welcome to the robot-based workforce: will your job become automated too? "The cost savings, convenience and flexibility that online learning offers has the potential to revolutionise education provision, but only if businesses and the education sector work together to capitalise on the potential of computer-based teaching applications to support employees in their pursuit of lifelong learning," the report said. Last year the CBI's director general also called for GCSEs to be scrapped and A-levels to be augmented by vocational courses. The report also calls for new tax incentives to encourage people to return to education, and to make it easier for employers to invest in their staff.

Over the past few years the field of Machine Learning has entered the general parlance. From free massive open online courses to image recognition benchmarks being broken and decades of Atari games being mastered. During the same period developers have witnessed the release of several popular open source frameworks and libraries. The chart below shows different open source machine learning projects by initial commit date and programming language. The size represents the popularity of a project based on number of Github stargazers.

Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches.

It's also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you're analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination--charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what's been presented.

When Moocs burst onto the scene five years ago, many predicted business schools' demise. Wharton professors Christian Terwiesch and Karl Ulrich wrote Moocs are a "Trojan Horse" with the potential to "destroy" the full-time MBA. But rather than killing the campus, they have become an example of the whizzy digital innovations being embraced by even the oldest Ivy League institutions. "You can expect us to take engaged learning to another level where we implement technology. We're already moving in that direction," says Alison Davis-Blake, dean of the University Of Michigan's Ross School of Business. "Online education is one part of it," says Soumitra Dutta, dean of Cornell University's Johnson School of Management.

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by having Q & A interviews with experts such as Mike Olsen, Hal Varian, Tom Davenport, and data scientists at LinkedIn, Facebook, Yelp, and more. The Data Analytics Handbook was not only instrumental in giving us the understanding we needed to feel confident in what we were creating; but was downloaded over 25,000 times, gave us dozens of contacts, and an immediate group of early adopters. Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others.

Very interesting collection of resources compiled by DistrictDataLabs, featuring books, online courses, articles across multiple categories: data science, probability and statistics, machine learning, R, Python, big data, DataViz, and NLP.

This class is self paced. You can begin whenever you like and then follow your own pace. It's a good idea to set goals for yourself to make sure you stick with the course. Take a look at the "Class Summary," "What Should I Know," and "What Will I Learn" sections above. If you want to know more, just enroll in the course and start exploring.

If venture capital and research funding are any indication, artificial intelligence will play a leading role in shaping our future. And few tech innovators in the private or public sector have been as prominent in defining that role as Andrew Ng, chief scientist at China's search giant Baidu. Ng has taught AI at Stanford, led the Google Brain project, founded online education pioneer Coursera, and just last year took his post at "China's Google" in hopes of figuring out how to teach computers to see and hear, and to do that for the world's most populous country. Small wonder why China represents such a huge opportunity for machine intelligence applications. Baidu is the world's fifth most trafficked website.