Most frequent questions and answers
The tuition fee is CAD 15,000
Yes, please see our Income Share agreement (ISA)
For DSR Berlin, 100 % of our graduates find a Job within three months of graduating. Connect to our graduates to know more; you can see the alumni (and their LinkedIn profiles) https://datascienceretreat.com/graduates/
It depends. You can certainly land a job by doing most interviews remotely, but the consensus seems to be that being around helps your search, because you can attend meetups, have lunch/coffee meetings with potential employers, etc. In an ideal case you should be able to stay for three months after the program ends. But plenty of people who didn’t stay got about the same results
We suggest visiting visa as it provides 6 months staying option in Canada which can be converted to work visa upon getting a job offer letter.
(1) Study python/engineering. We expect you to have about 1000 hrs of experience programming so that your programming skill is not a bottleneck. If during the interview we realize you don’t have enough background, we will send you back to improve yourself and interview again in a month. During that time we ask you to write something that shows you can program. It doesn’t have to be ML or data science related. It’s much better if it’s not a jupyter notebook. We want to see how you organize your code, write comments, commit messages, etc.
(2) You can prepare some basic ML questions. The first few chapters of a book would be good. One that has pretty good explanations is Kuhn’s ‘advanced predictive modeling.’ Unfortunately, it’s in R, so please skip the code examples and exercises.
We should contact you within a week. Then you select a date for the interview, and if you pass, you get the answer the same day. Onboarding starts that same day so that if you have to start moving with Visa/accommodation, you can.
Apart from basic technical questions on python and basic ML, we will be asking you questions about the following topics:
– What is your motivation for seeking a career in data science now?
– Why are you applying to AI Deep Dive?
– How high is your competence in coding, in data, and business?
– What are you learning currently?
– What are your plans after completing AI Deep Dive?
About 1000hrs programming experience (not professionally) programming. Some basic knowledge of ML (what is overfitting, cross-validation… the first few chapters of a book/videos in a MOOC)
You will get a teaching calendar at the beginning of the course. Please note that because our teachers have full time jobs in the industry, and coordinating their busy schedules is an operations challenge, we will have to rearrange some classes, or replace them with a different one. Please plan for some flexibility in the teaching calendar. Rest assured that any changes (if any) would not compromise teaching quality.
We have videos for some past projects on the homepage, and also on our youtube channel.
If you are not willing to work very hard or don’t have any inclination at all for technical work, you may not want to do the program.
If you want to live in a place where there isn’t any activity in data science, then you will have a hard time finding a job and doing the course may not be worth it.
Teachers prepare materials and deliver them.
Mentors don’t teach. They work ‘very part-time’ and engage participants when they see a project they like. That is, it’s all about personal connection whether a mentor will work with you.
Remote mentors can answer questions (if it lands on their expertise area) via email or chat. They are usually really busy so don’t expect them to give you immediate answers. You are much more likely to get an answer if your question is something they know well and is not ‘Google-able.’
We are considering adding an extra type of mentor that is there just for technical problems. But for general mentors, don’t expect them to sit at your keyboard and write code with you.
Teachers can be mentors too.
It’s impossible to tell how every MOOC does a capstone project, but from the small sample we’ve seen, a capstone project is a ‘bottled’ one where the student gets a problem, dataset, and maybe even code skeleton. A portfolio project at AI-deep-dive is an original piece of work; you need to identify a problem worth solving, get or produce data, run a model, and communicate the results in a demo day. Some portfolio projects are products (i.e., people can use them), in a very rudimentary form (that is, they are a proof-of-concept, not really production-ready).
We have pretty detailed stats from 5 years running DSR in Berlin. About 86% of the participants get a job in 3 months, and close to 100% after six months. There are some who don’t, but they have extraordinary circumstances. For example, they don’t want one because they are starting a business. Others need to live in a part of the world where there’s absolutely zero opportunity for a data scientist. Others may have family obligations that are incompatible with holding a job.
We don’t have stats about Toronto yet, but we expect them to be comparable to Berlin.
It’s tough to generalize, but we’ll give it a try. Both places have offices from big tech companies, but these are not the main drivers of economic activity. More conservative industries that are adding machine learning to their processes are common. Given that Canada sells to the US market, it has to compete with US companies. So what you know works in the US market could be a proxy for Canada. The EU market is different, very focused on industries like pharma, banking, and mobility that are very regulated and averse to risk. In EU things move slower and there are more incentives to keep things from changing rather than to innovate. That has advantages (job security) and disadvantages (the latest technologies will not be in use).