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Research Highlights: ExBERT

In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review a new paper: EXBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models by researchers from the MIT-IBM Watson AI Lab and Harvard.

How to Produce Cleaner Data for Robust Pricing

In this contributed article by MIT Sloan School of Management Prof. Negin Golrezaei, found that there are ways to limit price manipulation. The key is the pricing algorithm. Instead of using bids to directly set prices, a prominent group of researchers designed an algorithm that uses censored bids– in this case a binary signal – to indicate whether the buyer wins in the prior auction or not.

Ask a TA: Everything You Need to Know About MIT xPRO’s Online Data Science Course

Eric Bradford, a Teaching Assistant for MIT xPRO’s Online Data Science Course, Data Science and Big Data Analytics: Making Data-Driven Decisions, gives his insights into the unique seven-week course. The seven-week course explores the theory and practice behind recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, machine learning, and big data analytics. The next online data science course begins Feb. 4, and is currently enrolling.

Can Online Professional Development Courses Teach Data-Driven Decision Making?

MIT xPRO’s Data Science program is segmented into six modules, each lead by a faculty member with expertise, research and teaching experience in that area. After just seven weeks, learners earn an MIT Professional Certificate in Data Science. This guest post from MIT focuses on whether online professional development courses, like its xPRO Data Science program, are successful in teaching data-driven decision making.