Why Should Engineers Learn Data Science Differently?

With data science becoming immensely popular and inviting attention from everywhere possible, it is quite obvious for us to be curious about how to learn data science. However, getting your question answered isn't that easy as it sounds. When you throw this question of – how to learn data science at the experts around you, you might end up having the same set of answers. And this is exactly where the problem lies. Learning something, be it something as simple as toasting bread, might not be an easy task to accomplish for every individual. A person with sound cooking knowledge and hands-on experience wouldn't find it difficult. On the same lines, learning a new concept (academically) is a different kind of experience for people from different backgrounds.

Considering data science, people from different backgrounds should have a different method to follow to learn and master it. This is because someone from an engineering background has an idea about data science, machine learning, artificial intelligence, etc. at least to some extent. On the other hand, a commerce student would require different training altogether. Here we will talk about why should engineers adopt a different method of learning data science.

Most of the engineers are already familiar with most of the statistical and algebraic basics of data science and AI. Yes, there are a lot of courses out there that focus on learning theories and math behind this science. But, a point worth noting is that for most of the engineers, those courses and materials are repetitions of what they already know. And since they are already aware of all this, it might be discouraging for them at the beginning, which you wouldn't want, right?

What could be an alternative then? There cannot be a better method to employ than engineers focusing more on tools, methods, and computer skills. It is by virtue of these tools and skills that one would be in a position to see mathematics and logic behind algorithms. And then, all of this would actually make sense to you.

Languages play a pivotal role to be well acquainted with data science. On that note, R and Python are two of the most important languages as far as data science is concerned. Make use of all the possible resources and grab as much knowledge as you can, without sacrificing on any of the concepts. You should be smart enough to understand which topics are worth paying attention to and which are the ones that aren't of much use. Talking to the data science professionals will help you get through this.

Also, pay considerable attention to machine learning as well. This is where you can manage to carve a niche for yourself. A good chunk of courses is available that can help you gain knowledge about machine learning. Now, here is the catch – you could always go advanced and make your way into learning deep learning. Deep learning is yet another favorite area of data scientists as it caters to a wide range of applications. As there isn't any limit to the knowledge one can gain, engineers looking forward to a heap of opportunities in the field of data science can think of exploring yet another advanced concept – Reinforcement learning.

A few tips to make your learning journey a lot more interesting, fun, and a roadmap to success – try participating in contests and competitions that test your knowledge,  focusing on one expertise is good but definitely give a shot at different fields as well and lastly brushing up the concepts related to statistics, algebra, etc. will always help you in the long run.