Data Science in Action

Sitewise Analytics, a Software-as-a-Service company, specializing in developing site forecast models, sales impact assessments, and actionable market strategy plans for leading restaurant, retail, real estate, and healthcare chains, partnered with Master of Data Science (MDS) Vancouver students to understand which specific factors drive success (and failure) for certain restaurant brands.

As part of their Advanced Corpus Linguistics project, four MDS Computational Linguistics students wanted to investigate the specific emotion in Goodreads book reviews.

For their Advanced Corpus Linguistics project, a group of MDS Computational Linguistics students decided to track all the spells mentioned in the Harry Potter book series.

A group of MDS Computational Linguistics alumni contributed to an article that centered around automatic speech recognition for non-native English with transfer learning.

In partnership with the Animal Welfare Program at the UBC Faculty of Land and Food Systems, a group of MDS Vancouver alumni created a visualization dashboard to monitor the feeding, drinking and social behaviours of dairy cattle. Accurately visualizing and monitoring aids with the early detection of health problems and improves cattle welfare.

With more and more primary care physicians using electronic medical records (EMR), extracting insights from it to improve patient care is complex. That is why JustPractice, a BC company using high quality EMR data analysis to help physicians manage their patients, enlisted the help of students from the UBC’s Master of Data Science Okanagan program, to improve the identification of patient care gaps through the analysis of EMR data.

During his time at Bethesda, Maryland-based National Institutes of Health, Nicholas Sanders, MDS Computational Linguistics Alumnus, Class of 2021, worked on a model deployed on an internal NIH website for staff to input a given grant application and receive PAC/PO recommendations.

Working with Seattle-based AI start-up, Seasalt.ai, students from UBC’s Master of Data Science in Computational Linguistics program created a universal NER (Named Entity Recognition) system that applied transfer learning from high-resource language datasets to low-resource languages. This allowed crucial information to be extracted from previously underrepresented languages, like Indonesian, Javanese, Malay, Vietnamese, Tagalog, Croatian, and Czech, for use across a variety of Natural Language Processing tasks.

As a result of a question posed by UBC Linguistics and French, Hispanic, and Italian Studies faculty, a group of UBC MDS Computational Linguistics students embarked on the first phase of a project that could improve graded readers for any language. The capstone project focused on A1, A2, and B level documents for the Spanish language, but it laid the foundation for the creation of a universal reader.