MDS Spotlight: Meet Daniel Jimenez, MDS Computational Linguistics, Class of 2024
Prior to entering the UBC Master of Data Science (MDS) Computational Linguistics program, Daniel Jimenez was working as a software developer with Amazon. However, it was Jimenez’s strong desire to pursue a career in Natural Language Processing (NLP) and Artificial Intelligence that led him to the MDS Computational Linguistics program.
Jimenez liked how the MDS Computational Linguistics program taught the latest in NLP trends that he didn’t find in other programs.
“My favorite MDS Computational Linguistics course was ‘Trends in Computational Linguistics’ because the professor put a lot of effort into teaching the latest advancements in NLP,” he said.
Also, Jimenez described learning data science and computational linguistics in-person on the UBC campus as incredibly valuable.
“Being on campus allowed me to ask questions directly to the professors, get immediate answers, and this accelerated my learning significantly,” he noted.
While Jimenez found the program very demanding, it was incredibly rewarding at the same time.
“The rigorous curriculum and hands-on experience make it worth the effort, as it significantly enhances your knowledge and skills in data science and computational linguistics.
One of those rewarding experiences was the capstone project aspect of the program.
“What I liked most about my capstone project was the intriguing problem we had to solve. Additionally, working on a project within the health industry was particularly exciting for me, as it's an area I am very interested in pursuing a career in,” Jimenez explained.
Another important thing Jimenez got out of his capstone project was learning that Machine Learning problems often contain many surprises. “Sometimes, the strategies you use to solve the problems do not perform as expected, making it a field full of mysteries and continuous learning.”
As he begins his job search, Jimenez found the workshops from MDS Computational Linguistics’ career advisor provided him with some valuable insights on how to craft a compelling personal brand, highlighting his strengths, and presenting his expertise in a way that resonates with potential employers.
Another support that Jimenez had during the program was UBC’s commitment to providing an inclusive and supportive environment to ensure student success.
“For those with physical or cognitive disabilities, UBC offers numerous accommodations to ensure you can attend classes and take exams in the most comfortable way possible. Don't hesitate to reach out for these resources if needed,” he noted.
Post program, Jimenez is looking to pursue a position as an ML/AI Engineer that will allow to build and create practical applications that impact people.
As for the most important thing that Jimenez learned from the MDS Computational Linguistics program that he will take into his career as a data scientist and computational linguist is the discipline of reading research papers every week. “This habit is essential for staying informed about the latest breakthroughs and gaining a deep understanding of the topics directly from the source.”
Another aspect of the program that has prepared Jimenez for a career in data science and computational linguistics is it has provided him a solid foundation of knowledge and skills.
“It has also instilled the discipline necessary to continuously update my expertise, which is crucial given the rapid advancements in these fields,” he added.
Daniel’s Top 3 Tips on Succeeding in the MDS Computational Linguistics Program:
- Don't be afraid to ask for help: Reach out to faculty, classmates, and mentors whenever you need assistance. In my case being part of the accessibility center saved me!
- Have a schedule and a strict routine: Staying organized and disciplined will help you manage your time effectively and keep up with the demanding coursework.
- Don't be too hard on yourself, just enjoy the process: Embrace the learning journey, and remember to take breaks. For me, going to the pool every morning helped me to stay on track.