UBC MDS

Recently, my team and I wrapped up our two-month long capstone project with Unbounce. The completion of the capstone marks the end of my Master of Data Science — Computational Linguistics program (MDS-CL) at the University of British Columbia, as well as the transition from being a student, the encapsulation of my identity for the past seventeen years, to being a new graduate, a new identity that still takes a little getting used to. In between job hunting and trying to maintain a semblance of a normal life in the midst of a global pandemic, I’d like to reflect on these past two months and the lessons that I’ve gained.

During the last two months of the UBC Master of Data Science program (typically May & June each year) we have Capstone projects, in which our students work in teams of ~4 students with an external capstone partner and a UBC mentor to address a question facing the capstone partner’s organization using data science. In 2019 we added in a weekly “capstone seminar series” where we invite data science experts from academia and industry to give a seminar on a relevant data science topic. In this blog post I’ll provide a short description and some key take-aways from each of this year’s seminars. The schedule for the 2020 capstone seminar series is shown in Table 1 below.

R and Python are the two leading languages used in industry and academia for data analysis. Thus, to best prepare students in the University of British Columbia’s course-based, professional Master of Data Science (MDS) program to be competitive and perform on the job market, we have made an explicit decision to teach both languages.

Students are concurrently introduced to R and Python in the first month of MDS, and they work in both languages for all 8 months of their coursework. In some courses, students learn the same skills in both languages (e.g., general programming, data wrangling, visualization, software development and packaging) and in other courses we specialize in one language in order to go more deeply into that topic (e.g., statistical inference in R, and machine learning in Python). In the 2-month Capstone project at the end of the program, students work with a partner from industry, government or not-for-profit. At the start of the project, the students and partner agree on which programming language will be used. This decision is sometimes based on what best suits the project, and other times is chosen based on what the Capstone partner’s organization already uses. Other times the projects involve a mix of both languages.

How can we train effective data scientists? Traditional lecture/lab-based courses typically involve prescribed and well-defined examples, and we found this format very effective for foundational courses that focus on a particular area of statistics, machine learning or computer programming. However, real-world data science differs greatly from these courses: data is messy, the tasks are not perfectly defined, knowledge must be integrated from various areas of data science, and collaboration with others is typically required. Our Master of Data Science (MDS) program ends with an 8-week full-time Capstone course, which provides much of the needed hands-on experience. However, based on student and instructor feedback, it was evident that we needed to do more. Thus, to augment and prepare students for Capstone, we have started transforming some of our traditional lecture/lab-based courses into smaller project-based courses as well.

One of the things that drew to me data science is its applicability to pretty much any field you can name: technology, healthcare, finance, retail, education, government, entertainment, agriculture, real estate, etc. There’s no domain too large or small and no organization that would not benefit from having a data scientist (or a team of data scientists!) on staff to solve interesting problems.

I recently completed the Master of Data Science program at the University of British Columbia, a 10-month intensive program focused on computing, statistics, and machine learning. After 8 months of coursework, the program concludes with an 2-month capstone project. My capstone team worked with Vancouver-based meal kit company Fresh Prep to build a predictive model for customer ordering. Fresh Prep can use our model to understand which of their customers are likely to order in a given week, and target their marketing strategies in an attempt to increase order rates.