Data science training
My master’s in data science program experience at UBC
I am a data scientist with a background in healthcare, and academic biochemistry and neuroscience research. I plan on finding a fulfilling role with an ethical company who is helping make the world a better place. I believe in using my data science skills for social good, as well as earning to give, which is a concept I first heard about from an organization called 80,000 hours.
After completing my undergraduate degree in Integrated Science: Biochemistry and neuroscience at UBC, I was quickly drawn back to school for the Master’s in Data Science (MDS) program because I was very intrigued by the power of computational algorithms and optimizing and expediting the research collection and analysis I was doing as a lab technician and research assistant.
I was very busy with my 10-month master’s at the University of British Columbia, in Vancouver. My life looked like this:
I am very grateful for the intense learning processes that occurred. Some of the topics we covered in depth include descriptive statistics, programming for data science, data wrangling, data visualization, algorithms and data structures, inference, regression, supervised machine learning, unsupervised machine learning, feature and model selection, collaborative software development, cloud computing, SQL database design, communication, privacy, ethics, and security. We had four labs due every week on Saturday at 6pm. This means that we completed 96 labs in 8 months. Each of our 16 courses also had two exams, which I found helpful for fully comprehending the material. This was followed by a 2-month capstone project with a partnering company to solve their business problems with data science solutions. My project contained confidential data, so I am unable to share the full details. Essentially, I helped E-comm improve their data management strategy and employee engagement by creating a reproducible data integration pipeline from 4 departments for downstream analysis: 1) attrition analysis for analyzing factors affecting employee retention and 2) a short-term absence classification model to predict whether employees will be absent based on internal performance and Human Resources metrics. Thanks to my project partners George Wu, and Mohamad Makkawi.
For more information on my master’s program, check out the UBC MDS website. I was interviewed about my experience in the program, which you can read here. Please see my resume here.
When I am not at work completing data science projects or marvelling at the black-box complexities of neural networks and how they may be similar to our own human brains, I am usually out for a run, snowboarding, meditating or cooking healthy plant-based meals (and cookies). I like reading nutrition research in my spare time, and am always looking for ways to add more health and fitness into my life, while hoping to inspire others to do the same. I also volunteer with Vyve, a human connection agency, and Habit@ Lab, a sustainability-minded habit-building workshop series.
I’m happy to connect via LinkedIn, twitter, or instagram to discuss all things data science, neuroscience, nutrition, biodiversity, food security, climate change, outdoor adventures, zero waste living, and mindfulness.