Recently, a new horizon in data analytics, prescriptive analytics, is becoming more and more important to make data-driven decisions. As opposed to the progress of democratizing data acquisition and access, making data-driven decisions remains a significant challenge for people without technical expertise. In this regard, existing tools for data analytics which were designed decades ago still present a high bar for domain experts, and removing this bar requires a fundamental rethinking of both interface and backend. At Einblick, an MIT/Brown spin-off based on the Northstar project, we have been building the next generation analytics tool in the last few years. To overcome the shortcomings of existing processing engines, we propose Davos , Einblick's novel backend. Davos combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover, Davos optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that Davos can greatly empower data analytics for new needs.

In [1] we previously described an innovative approach to teaching programming fundamentals to undergraduate students in a course called Programming Studio. This required program is designed to be given shortly after CS2. We teach topics such as high quality code design, proper problem de-composition and methods of code documentation, as well as practical subjects such as correct usage of various types of version control and a variety of programming languages that are not usually introduced in other CS courses. Here we build on what was previously written with the new methods of assessment, types of assignments, and the lessons learned from expanding the course from less than 75 students to nearly 150 students.

We are excited to introduce CoMoTo -- the Collaboration Modeling Toolkit -- a new, web-based application that expands and enhances well-known software similarity detection systems. CoMoTo is an end-to-end data management, analysis, and visualization system whose purpose is to assist instructors of courses requiring programming exercises to monitor and investigate the extent of student collaboration, both allowed and illicit. We describe CoMoTo's interface, which was designed to facilitate scrutiny of collaboration data projected along student, course, assignment, etc. attributes, and to allow for interactive visualization of pairwise similarity measures via a dynamic graph. We also elaborate on the details of CoMoTo's implementation. Finally, we briefly discuss two use cases that foreshadow CoMoTo's broad utility in student code analysis, not only for plagiarism detection, but also for investigating early student coding styles, and for evaluating software similarity detection systems, themselves.