Do this and you will always be in demand, no matter whether it's recession or it's layoffs. Do you want to be a data scientist or an economist? Do you want to study economics or data science? Let’s understand this with the conceptual ideas about these two separate fields and how they are different and how they come together. The rigour of data science is in mathematics and statistics. Obviously, it has been adapted for data scientists and data analysts, and when we are talking about mathematics and statistics, you can not discount the use of coding. At every walk of your data science journey, you will have to do some kind of coding. It could be very elementary coding with just one or two lines for commands, it could also be forming extensive functions and then calling those functions and then on top of it, you can also have the use of classes.
A skill which data scientists need to really be expert is in handling data, so when I say handling data, it means the file can come in any format and you must know how to make it in such a way that you are able to look at the data and find out some kind of elementary insights without doing any kind of extensive or rigorous modelling. Just take the data, clean it or process it in such a way that you are able to work with it. When you are a data scientist, you always have to worry about the business problem, about the business user, how your data science solution is going to help the business. When you are thinking about these things, about optimizing your code solving the business problem, the boundaries blur between software engineer and data scientist. If you are a data scientist, you will have a lot of members in your team who will be software engineers.
Let’s come to economics. When you are studying economics or you want to become an economist, there is a lot of rigour and assumptions about what exactly is the state of the world and how do you want to model that state of the world. You have to go through a lot of abstraction and then through the help of mathematics, statistics and your economic principles, you find a kind of solution. In data science, it’s slightly different. The more you abstract, the more difficult it is to link it with the real world and the more difficulty you will have in explaining your solution to the business user. Similar to data science, even in economics, you will have a lot of mathematics and statistics. Everything in economics needs to be actually proved. In data science, a lot of things would be just done with the help of showing, showing accuracy in charts. In data science, there are a lot of problems which are domain specific and sector specific. So, you become an expert in those sectors, if the need arises so, but in economics, a lot of problems have an underlying theme of political science, behavioural sciences, psychology, history, anthropology, sociology, a lot of social sciences find representation in economics. Also, there is a room for a lot of theoretical work in economics and you will get satisfaction in it.
In data science, you have to support your theory with empirical studies always. You cannot just do theory. In data science, you always have to show your theory through some empirical work. If you study to become a data scientist or let’s say you have an experience of being a data scientist, you will be sought after by consulting firms, by IT or by any kind of a product firm, who are trying to build a solution or a product which requires data insights, predictive model, and some kind of recommendation, you get the drift?
When you are reading economics, or you are experienced in economics research, a lot of macro research firms like hedge fund firms, VC firms or simply risk consulting firms could go after you. You will be sought after, and if I just go up the value chain, if you are very good in economics, you have some kind of an advanced degree like a masters or a PhD with some amount of experience, you could work with central banks or big banks in their economic research teams, in their economist teams.
In data science and in economics, there is a lot of mix. You work with data, you work with maths, and statistics, but the moment you take the economics route, you go towards a research route, you go towards R&D teams of banks, hedge firms, financial firms and the moment you go towards the data science route, you go towards IT firms, consulting firms and product firms. And yes, this is just a rough idea, I have heard of economists working with product firms and data scientists working with hedge fund firms. It’s not like that if I study economics, a lot of jobs will be closed for me and if I study data science, a lot of jobs will be closed for me. It’s just that build your profile according to your interests. And ultimately five years down the line, you never know what type of work you would be doing, because super-specialisation is going to be the theme for the next 15-20 years. The moment you equip yourself with a lot of super specialised and highly specific knowledge about a field, you will be selling like hot cakes and will go deep into the problem which you are solving.
In this way, you will always be in demand, no matter whether it's recession or it's layoffs.
Best of Luck!
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