July 17, 2019 at 12:17ok.tech: Data Explain # 2
On August 7, ok.tech: Data Talk # 2 will be held at the Moscow office of Odnoklassniki. This time the event will be dedicated to education in Data Science. Now there is such a hype around working with data that only the lazy did not think about getting an education in the field of Data Science. Someone believes that without a university degree it is impossible to become a data analysis specialist, there are supporters of the opinion that you can learn how to work with data through courses, others adhere to the position that a good data specialist is one who constantly practices and uses a versatile approach . We will gather representatives of different opinions on our site and give them the opportunity to discuss on this topic.
The event will be held in the format of a discussion between the speakers. This time, Evgeny Sokolov (HSE, Yandex.Zen), Dmitry Bugaychenko (OK.ru), Peter Ermakov (Lamoda, DataGym), Dmitry Korobchenko (Nvidia, GeekBrains, SkillBox, DigitalOctober) and Victor Kantor (Mail.ru) will be with us Group, Data Mining in Action). We invite everyone who is interested in the topic of education in Data Science to join the event and express their point of view. Studied at the courses - come and tell us what it gave you. You think that without PhD it is impossible to analyze data - come and tell why. Do you think that a data specialist should be able to write in the food industry - come and discuss it.
→ Registration for the event
Under the cut expert opinions and schedule.
Evgeny Sokolov, HSE, Yandex.Zen
Now there are many options for training in data analysis: there is something closer to the “technical school”, where they teach just to use ready-made tools, there are opposing opinions about how to look at ML as a mathematical thing, not a craft. I believe that, first of all, you still need to learn the craft, because without this it is impossible to motivate the student, and then at work he will use these skills 80% of the time. But at the same time, it is extremely important then to teach him the right way of thinking and a deep understanding of the methods - without this, the student simply will not become competitive in the labor market.
Dima Bugaychenko, OK
For DS education, I would highlight several important “challenges” that distinguish DS from other areas. Firstly, this is dynamics. Everything changes very quickly and therefore you can’t learn, get a diploma and become a DS, you can only constantly study to stay with them. Secondly, it is the synergy of very different disciplines. You need to understand the mathematical essence of the methods, and be “on you” with technology (if we are talking about DS, not about a monkey sticking a stick in XGBoost). And, thirdly, this is a very high demand for educated DS from the industry, together with a large gap in expectations between the industry and the academy in Russia, which, in particular, leads to the emergence of a large number of “schools” from major market players.
Peter Ermakov, Lamoda, DataGym
I really love teaching, especially the moment when it is possible to tell the complex in simple language, and in the eyes to see understanding. Over the past 10 years, I managed to teach in 26 launches of three commercial courses, two universities, inside the company and to conduct an open educational project. And now I'm creating a commercial 3-month machine learning course on DataGym.ru. All types of education are good in their own way. And commercial courses are no exception. These are other opportunities, a different entry threshold, a different level of motivation and time spent.
Dmitry Korobchenko Nvidia, GeekBrains, SkillBox, Digital October
My position is that there are no areas that would have all the possible advantages. I can’t say that one thing drives, but this is another - no. I’m more likely for matan, and also math normal mathematics. I don’t really like it when people use tools without understanding how they work (at least at an average level). But I think that in some business cases this will be justified. Especially considering the democratization of AI. Regarding Cuggle, I can say that I know a lot of people (including myself) who have developed quite well in the region without resorting to this resource. But I think that he still gives an additional boost in certain skills.
Victor Kantor, Mail.ru Group, Data Mining in Action
Every year, as part of the offline Data Mining in Action course alone, about a thousand people get to know machine learning. About 100 thousand people have taken part in online courses launched only by my colleagues (and in the world, obviously, there are still a lot of other courses). Of course, those who do not just “get acquainted”, but get to the end and become, for example, a Junior Data Scientist, are much fewer, but anyway, just a crazy amount of people come to data analysis, so there’s no need to hire a person to the initial position very difficult. But problems begin at the middle level and higher - the search for an employee immediately becomes long, painful and, as a result, expensive. What to do with this is the question that I am doing now.
timetable
18:30 - 19:00 - Registration of participants
19:00 - 19:05 - Intro from Alexey Chernobrov
19:05 - 20:00 - Controversy on the topic of education in Data Science
20:00 - 20:20 - Coffee break
20: 20 - 21:30 - Continuation of the controversy
→ Registration for the event
