Photo courtesy: Flickr/CollegeDegrees360

Was one of your New Year’s resolutions to build up your knowledge, skills and talents for the new digital world? If so, there are plenty of online options to help you achieve your goals, and at no cost to you, from the crop of MOOCs (massive open online courses) that’s sprung up.

The Semantic Web Blog scoured some of them to present you with some possible courses of study to consider in pursuit of your goals:

Coursera:

• Data scientists-in-training, Johns Hopkins Bloomberg School of Public Health assistant professor of biostatistics Jeff Leek wants to help you get a leg up on Big Data – and the job doors that understanding how to work with it opens up – with this applied statistics course focusing on data analysis. The course notes that there’s a shortage of individuals with the skills to find the right data to answer a question, understand the processes underlying the data, discover the important patterns in the data, and communicate results to have the biggest possible impact, so why not work to become one of them and land what Google chief economist Hal Varian reportedly calls the sexy job for the next ten years – statistician (really). The course starts Jan. 22.
• We’ve seen a lot about robots in the news over the last month, from the crowd-funded humanoid service robot Roboy, the brainchild of the Artificial Intelligence Laboratory of the University of Zurich, to Vomiting Larry, a projectile vomiter developed to help scientists to better understand the spread of noroviruses. If you’d like to learn about what’s behind robots that can act intelligently (sorry, Larry, but you might not qualify here), you want to learn more about AI. And you can, with a course starting Jan. 28 taught by Dr. Gerhard Wickler and Prof. Ausin Tate, both of the University of Edinburgh.
• Siri, where can I go to find out more about natural language processing? One option: Spend ten weeks starting February 11 learning about NLP with Michael Collins, the Vikram S. Pandit Professor of Computer Science at Columbia University. Students will have a chance to study mathematical and computational models of language, and the application of these models to key problems in natural language processing, with a focus on machine learning methods.

Read more

A recent article examines the shortcomings of sentiment analysis and how semantic analysis can help. According to the article, “For years, sentiment has been a widely used measure of how customers view a company’s products and services. But sentiment analysis has inherent flaws. First is what it cannot tell you because it only considers a small amount of the available data. Only about 25 percent of posts actually contain sentiment, either positive or negative, which means three out of four posts are neutral, revealing no sentiment, and are effectively being ignored by the analysis. Thus, decisions are being based on what only a quarter of the posts are saying. Another problem with sentiment is statistical confidence in the data. Simply stated, all methods of sentiment analysis rely on example data that, whittled down, reveals a low level of confidence about the sentiment being identified, either positive or negative. Data with such low confidence is a poor foundation for sentiment analysis.” Read more