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The post-covid landscape
One of the most interesting things about COVID-19 is that for the first time most people were forced to work remotely. This was a very interesting experiment in remote working, and it helped both individuals and companies understand what works what doesn’t. Some people like remote work, some do not. Some people missed human interaction, some did not. Like with all things, remote working comes with benefits and drawbacks. It can be more efficient, since people spend less time commuting. At the same time, it can be less it can be less productive or fun, since the element of human interaction is lost.
The education system was also affected, with many universities, like Cambridge University, running courses mostly online until summer 20201. This has forced many people and institutions to rethink education. Is online better than offline? What is gained and lost when students are taught in this way?
What is the best way to learn data science?
I have been a firm believer for a long time that traditional education does not really work well for data science. Universities can provide a great education, but at the same time, we saw the rise of bootcamps, which promise to turn someone into a developer or a data scientist in a few months. The reason that bootcamps have proliferated is because they are filling in a gap that has been missing from higher education. The same can be said for MOOCs. Here are some examples of gaps that these offerings are filling:
- For many things, you don’t need a teacher. There is lots of content online, which can help you learn more easily.
- Students in a classroom have different learning styles. Lecturers have to try and adapt to everyone, which is a difficult task and might end up leaving some people unhappy.
- Not everyone finds it easy to concentrate in a classroom for long periods of time.
That being said, university degrees are still the choice that can provide the greatest depth. They also help students integrate into a social network of people that share the similar interests and goals. The social aspect can have unique benefits, which is often lost with online courses. This is why we have seen university courses co-exist with MOOCs and bootcamps.
But is this the best way to learn data science?
Enter Beyond Machine
I believe that the best way to learn data science is to blend different modes of learning. That’s why I decided to create Beyond Machine based on my many years of experience in data science education. Beyond Machine blends the best parts from university courses, MOOCs and bootcamps.
It is the result of many years of experience in all aspects of data science education: from universities, to executive workshops to bootcamps. What are the benefits?
- It is flexible: It can fit around anyone’s schedule, and can be done in as few as 3 months, or as long as up to 1 year or more.
- Topics which are best for solo-learning, like coding, are being taught online.
- Lectures are online, but are supported by additional content, so they can fit around anyone’s schedule.
- There are offline face-to-face workshops in order to facilitate social interaction.
- There is 24/7 mentoring and support, which helps speed up learning.
- The students get the chance to work on projects of their own choice, which enables them to do things they like, or related to their work.