Machine Learning and Big Data

Prior to Lantern, I had specialized in UofT’s Analytics stream, which was just a theoretical exposure to 4 data science courses. At Lantern, I practically developed my coding skills. Serious students who dedicate time and effort (at least 20-30 hours a week) will develop the fundamental coding skills in Python and R within a 3-month time period, and then they can build up more complex Machine Learning and in-depth analytical projects supervised by instructors all of whom are industry professionals. The class size is approximately 20 students for general courses and gets smaller for more advanced courses. Throughout the semester, students get career advice, resume review, soft skills catch up, mock interviews and should also deliver a few presentations about the projects they have developed. The instructors are busy professionals, but they generously exchange ideas and supervise the projects and usually accept mentorship requests. The scopes of the projects vary from 1 month for the basic courses, to 4 months for the advanced courses. The system is very co-operative and in my personal case, I have learnt a lot from my classmates, while collaborating on different projects. Lantern has skin in the game and charges a small registration fee relative to the norm of the industry; its bulk of income depends on future graduates’ success in landing a decent career.

I was one of the fortunate ones that became aware of Lantern at its early stage. At the time, I was confused about my career – How was I going to combine my Physics PhD, CFA results and various other stuff, into an attractive package in the job market?

It turns out this is the million-dollar question that Lantern was built to solve. For many confused STEM graduates, this is an opportunity to not only convert one’s hard learnt STEM skills into those actually applied in the industry, but also a good chance to know the industry from veterans and make your own allies in up-hill battle of job hunting.

To myself, I was particularly struck by two things: How much your instructors care about your success, and How much you may learn if you work hard. And it seemed these two were intertwined. Data Science, Machine Learning and Mathematical Finance were sexy yet challenging topics to master, especially in the limited amount of time. The instructors did an amazing job in the sense you quickly get to know the basics and the anatomy of the knowledge so one knows where to go.

At this point, it is up to you to utilize the resources and achieve your goal. Classmates were often the first line of help: people in a similar level of progress may encounter similar problems. Instructors steer the wheel with you and connect you to their friends in the industry - many with job openings to fill. Now this is showtime: exhibit what you’ve got in the past few months, and more importantly demonstrate your work ethics and your ability to learn, which is crucial in these quickly evolving fields.

The best part of my Lantern experience, is you develop a sense of family in this demanding process. Indeed many of my instructors and classmates became friends, and as a Lantern alumni you are always welcome to come back home.