How ISI MSQE can help you become a Data Scientist

When you study MSQE course at ISI, you are trained with a significant amount of

mathematical and statistical skills and tools for doing economic research.

And, when you read econometrics or linear programming or let’s say you study statistics, or optimization, you have a primary objective to understand, “how do I do the economic research or economic analysis with these tools”, and “are these tools helpful for me or not”.

Now having said that, it does not mean that you have to restrict yourself to economic research. The idea here is that economics is a lot about model building and number crunching. If you try to understand it, data science is kind of pretty similar to economics and the skills are transferable to each other. So, you can transfer your learnings in MSQE as an economics student to a data scientist and rather I would say, a data professional.

I myself had qualified for both, CMI’s Master’s in Data Science program and ISI Kolkata’s MSQE program. Now, I chose MSQE program at ISI Kolkata mainly because of few reasons. I also wanted to study a bit of economics and finance. One of the things about finance is that, if you do not understand economics properly, then your finance knowledge will be like the people who come to news studios or business new channels and they keep on talking about things without making a lot of sense. So, you have to understand economics and not just the macroeconomics but the microeconomics parts as well. This is so because without a firm understanding of microeconomics, your modelling skills will suffer a lot as only when you study microeconomics properly, you try to understand the understand the significance of economic modelling in situations which involve transactions and incentives.

When you move on from microeconomics to macroeconomics, there are a lot of factors which are governed by your set of assumptions and in the case of data scientists, things are slightly different.

Data scientists are people who are more akin to throwing a lot of computation power and a lot of algorithms to data and they do not really bother about the assumptions in lot of cases. This could be controversial for a lot of people, but it’s pretty accurate that when you are a macroeconomist, your assumptions are like fundamental basis of your every analysis. If I change one of the assumptions of your model or your analysis, your whole analysis will change and that is something which is not really so much apparent in microeconomics or any domain of natural sciences.

So, that is where the departure starts happening when you move from microeconomics to macroeconomics. But that is also the part where data crunching begins when you move from microeconomics to macroeconomics. In macroeconomics, you access GDP data, inflation data and try to make sense of what will happen with these numbers and how will exchange rate, trade surplus or every other macroeconomic factors will affect these numbers.

If I have to give you a basic idea that what is really a very prevalent theme in your

macroeconomic research, its understanding of time series analysis. Now, when we talk

about time series analysis, they are essentially two types of time series. One is univariate time series, in which you just have one unit and for that unit, you are observing a particular feature at difference points of time and just noting it down.
There could also be a time series in which you have multiple units and for all those units, you are observing a feature and noting it down at all time intervals. This time series becomes a panel data time series and earlier one was a univariate time series. If you go into the deepest part of econometrics and time series, you will have to deal with a lot of panel data. This is the part which is not really touched by data scientists but it is touched by econometricians and macroeconomists.

And, the time series forecasting or analysis where you calculate the trend seasonality or do the forecasting, is actually dealt by a lot of data scientists as well.

The problem here is that, a lot of people think that you need a lot of coding expertise in data science. That is true to an extent and it depends on the company and on the role. A lot of companies require software engineers and as they want to market themselves well, they label it as a data scientist role hiding it beneath a lot of jargons. So, you need to be sure that what kind of role you are getting into, rather you have to understand whether this is a software engineering which you are applying to or really a data scientist role. Not all companies will be very honest and upfront but all good companies are pretty much upfront what they expect and require from you.

Whatever you study in MSQE, which is a quantitative economics course, a lot of things are related to number crunching, data analysis, mathematical and statistical skills and all is for economics research and you can very well use it to become a data scientist.

As I have already told that I wanted to become a data scientist but I had a keen interest in the markets and finance, that is why I chose the MSQE program.

If you have any doubt and queries regarding this topic, feel free to comment down below.

Recession Proof Career - Economics Vs Data Science

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!

Where do Economists Work ?

As the employment market has evolved throughout time, the places where economists work has also changed over this period of time. Economists now mostly work in either classic economist jobs or in the financial services roles or in the data science teams. And there are many similarities between these roles. Therefore, if we talk about traditional economist roles and financial analyst roles, we will get to see a lot of economics graduates working in large banks or financial firms as macro economists or as individuals who analyze financial data using economics principles. Both, Financial Analysts and Macro economists have a very different approach. A financial analyst would really be focused with the financial transactions, which may be about the debt market or stock market, whereas a macroeconomist would typically be concerned about thought leadership.

If we go a little further into the jobs for economists, we could find economists working for central banks, investment banks, retail banks, market research organizations, IT companies, and also in places like Google & Amazon.

If we shift our attention to financial analyst-type positions, quants can be found working in large banks and financial institutions as risk analysts and credit risk analysts. The majority of their work is therefore in regulatory models, and certainly, they are well-paying positions. So, typically, when you encounter someone who identifies as a quant, they will work in a risk model team and perform a significant amount of model validation.

Now moving to Data Science, which is considered as the hottest career of the 21st century; Numerous economists who have completed their degrees work on data science teams because they are trained to work with numbers and have strong analytical skills. The data science team may now work for a financial institution, a bank, an IT company like Google or Amazon, or anything as commoditized as the likes of TCS or Infosys. The majority of the positions that economists hold pay well, and there are many of them where you can see them working. Don't assume that economists will always do macroeconomic research because this is just a relatively tiny portion of the jobs available to economics graduates.

Along with working in the finance industry, economists also work in government agencies and in academia.

Economists at government agencies analyze and evaluate economic data, devise policies, and advise policymakers in a variety of roles. They may work for the Department of Labour, the Treasury Department, or the Federal Reserve. They use economic models to estimate the probable effects of policy decisions, evaluate the efficacy of current policies, and perform economic research. Their efforts may be employed to gauge the economy's direction, stimulate growth, and improve individuals' well-being.

Economists working in academia often teach economics classes, carry out economic research, and produce scholarly papers for journals. They could be employed by colleges, research facilities, or think tanks. They employ economic theory and quantitative techniques to analyze and evaluate data, and their research frequently focuses on particular economic themes, such as labor markets, trade, or public finance. They also act as teachers and mentors for aspiring economists. Their work advances the study of economics and informs discussions of public policy.

Overall, economists may utilize their knowledge and skills to analyze economic data in a variety of situations and industries.