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!

Microeconomics You Must Know

I have done B.Tech from IIT Kharagpur and Masters in Quantitative Economics from Indian Statistical Institute. I work in the intersection of data science, economics and statistics.

The discussion in microeconomics can be started with the conception of what is there to buy in the market, it could be books, movies, food, etc. Given the preferences and what is available in the market, one can only buy the preferred goods if he/she has the required money, i.e, budget.

Given these things, a lot of mathematical analysis goes through and then there are a lot of market imperfections.

There is a lot of agent-based study done, i.e, Game Theory when there is a strategic interaction between agents. It is a very interesting part of microeconomics.

Let's take a behavioural aspect of Microeconomics : 

Market for Lemons
There is a very interesting concept in microeconomics, called Market for Lemons. It is the economics of asymmetric information. Suppose that you go to a used car market or a shop and you have two kinds of Cars, one is a good and one is a bad. Obviously, in the beginning, you were unaware of the fact that the car is good or bad. So both of these cars will obviously have a hidden price now. The hidden price is hidden because you don’t know the type of the car. This computation can be done automatically that when one enters into a used car market, then he needs to average out the good and bad car. At the moment an individual averages out the good and bad prices, the price which you want to pay is sufficient for the bad car, but it is not sufficient for the good car. So, the good car sellers are not happy in the used car market, they do not have much of incentive to enter in a used car market unless there is a perfect price discovery. 

On studying microeconomics, it makes an individual gradually move towards psychology and behavioural economics. Now then the subject of economics becomes interesting. It no longer remains the usual political economy discussed in news which is generally considered as macroeconomics.

Economics Vs Data Science in 2023

The key concern in 2023 amongst students of Economics, is the argument that in which field, there are better job prospects, Economics or Data Science.

Looking at the scenario these days, both of these streams have become overlapping in nature.

Pursuing an advanced economics degree, Masters or a PhD, is a great route to get into high quality Data Science Jobs.

Full Stack Data Scientists

As per the current trends, the demand of full stack data scientists is very high. 

The work profiles of Data Engineer, Business Analysts and Statistical Experts, combined together form a Full Stack Data Scientist. And sometimes, a variation is Machine Learning Engineer. It could be a wise decision if you are hell bent on making data science career to think about what exactly is a full stack data scientist.

Significance of Economics

Even being in Economics stream, one can get into the field of Data Science. Studying economics gives the statistical tools, research aptitude and the subject matter of economics which is an ideal way to get into finance jobs.
On combining these skills with certifications such as CFA (Chartered Financial Analyst), FRM (Financial Risk Manager) or CAIA (Chartered Alternative Investment Analyst Association), you are going to be in great demand in the finance industry.

Altogether, it can be said that, economics exposes an individual to conventional & analytical along with creative jobs.

If one gets into a data science education, it invariably demands him/her to learn about the latest trends in technology. And economists possess impressive technical capabilities and exposure to latest technology in addition to some core skills required to be a data scientist.

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.

Macroeconomics for Economics Entrance Again

If one has to prepare for Economics entrance exam, how should one approach the Macroeconomics subject? There are not many good resources available like Hal R. Varian, which is one of the best tailor-made books for entrances in the field of microeconomics. There are good macroeconomics textbooks, which can be thought of as close-substitutes for an ideal entrance preparation textbook.

Given the resources, the right way to start with this subject is to start reading one out of these three books- Macroeconomics by N. Gregory Mankiw, Macroeconomics by Rudiger Dornbusch, Stanley Fischer, et al. or Macroeconomics by Olivier Blanchard. The ideal way would be to complete the textbook from page one to the end. Doubts are a sign of preparation. One should religiously note their doubts in a separate register to refer it back & forth. It could be a small doubt related to a concept or a topic and even can be a whole chapter. The Internet is a solution to a lot of problems and clearing doubts can be one of them. Try to understand the concept from the internet; direct answers won’t likely be available. The sources can be in the form of blogs, YouTube videos and documents. You can always follow good channels, blogs and follow current affairs to understand the application of theory in the real world.

After completing reading the textbook, the second step is to go through the previous years' question papers. The main focus could be on the most challenging entrances- ISI MSQE, DSE and IGIDR. Every individual has different preferences for choosing the college for themselves and this can be one of the ways it could be done. Zeroing down on these entrance examinations can lead to a lot of doubts and uncertainty. You can note the topics that might require a revision or any fresh topics which were not covered in the initial phase of preparation. The Internet can help clear most of the doubts if you read through relevant resources. It can be a slow and time consuming process but it would make your concepts clear and resolve new doubts if any.

All these examinations generally happen on different dates and may differ in syllabus pattern. But, the base of almost all Economics entrance examinations is based on Microeconomics, Macroeconomics, Basic Mathematics, Statistics and other relevant subjects. 

In the last few days before the exam, the strategy should be quite different from the previously mentioned one. The revision and recollection of the concepts and topics that you’ve learnt before can help you go a long way. You can always refer to the youtube playlist specifically dedicated on solving ISI MSQE question paper.

Why I studied Quantitative Economics at Indian Statistical Institute (ISI), Kolkata

In this blogpost, I will try to give a brief overview of some of the key incidents in my life, due to which I decided to study Quantitative Economics at Indian Statistical Institute (MSQE).

While I was doing my engineering, I was actually thinking of writing a novel. And in those days, the novel writing phenomena was very popular in IITs and IIMs because of the works of Chetan Bhagat.

When I started writing, I showed it to one of my friends. She reviewed my writing and remarked, “you don’t develop your characters, they suddenly come up to the scene, do their act and vanish.”
This got me thinking and she also handed me a list of books to read. Then I went through a bunch of books in the hopes of possibly mastering the skill of novel writing.

It was just an example from a part of my life to set a context here.
So what is that context? When I was in my class 3, 4, 5 and so on, I was not really good in mathematics.
I was a very mediocre kind of student and the reason behind that is, that I never studied that well. I just studied before my exams. And maths is a kind of subject that you cannot pass if you study it just before the exams.

With the passage of time, few things changed, I started to get better at maths from class 6-7 onwards. And then in class 9-10, I was so good at maths that I was among the top 3 rankers (only in Maths) of my class and I used to be liked by my maths teacher a lot. And after that when I went to class 11-12, mathematics became challenging in nature, but physics was my refuge. It gave me the room and the freedom to apply conceptual understanding to solve physics numerical problems. It has been very well said that if you have learned physics, you will never forget it, just like you never forget riding bicycle. But with mathematics, practice is of prime importance.

So when I went into IIT with this background that I am good in maths and I really like physics, I wanted to do some kind of research. When I started studying in IIT, the research interest started dying out. There was no maths, no physics and whatever there was, it was not interesting for me.

And later on, like other IITians, I started moving towards the domain of consultants. Unknowingly, I started taking baby steps towards MSQE.
I was crystal clear in my mind that I wanted to do something in mathematics. To do something in mathematics, engineering is not a very good option. There were options like actuarial science, and statistics. But unfortunately, in those days, I was not very familiar with the domain of statistics. And actuarial science was something which required a lot of commitment.
So, out of all these constraints, I gradually developed an interest in Finance, Investment banking and had also done few internships in these domains.
Post my graduation from IIT, a major chunk of my work life was devoted to teaching physics for JEE and NEET. Due to my prolonged physics teaching stint, my interest in mathematics never subsided and while I was reading through all sorts of thing available around me, I understood that there is something in which I can do work on, i.e., Analytics.
At that time, data science had also started coming to the picture. That led me towards the courses which I can do, in which I can mix finance, mathematics and analytics. Meanwhile, I also passed CFA level 1.
In CFA level 1, the economics section was a bit tricky for me.
After that you already know my story of how I decided to do MSQE.
But what is the part between my economics and analytics interest to MSQE, that is something which even I do not know, I just have a very vague kind of a memory; it was somewhere around the month of May, 2013 that I thought I will do some kind of course related to analytics. In this regard, I talked to a friend, who was preparing for CAT. He gave me an idea that there is a course called MSQE in ISI.
Till late 2014, when I started researching about Quantitative Economics at Indian Statistical Institute (MSQE) (and to be specific, only in the month of December, 2014), I decided to study MSQE, with this idea that I want to study Economics, Finance and Analytics.

So, altogether I can say that it was not because of some allure of placements at ISI Kolkata, but it was my interest & passion towards the domain of economics and finance that drove me to the door of ISI MSQE.

So this is why I decided to study Quantitative Economics (MSQE) at Indian Statistical Institute (ISI).

Is Studying Economics a Good Career Choice? & its Scope!

Why Economics?
Many questions may arise when you think of choosing a career option. If you are someone who’s thinking of choosing Economics as an option, are you on the right track? Your motivation could be to become a central banker, you might be eyeing big investment banks or simply interested in studying the macroeconomics of nations.
There is no right or wrong answer.
One needs to be careful because merely an educational degree may not suffice; It can improve your understanding of the world but the working of the economy is quite imperfect and volatile, unlike theory.

In simple terms, Economics is about making informed decisions from available limited resources. The application of this simple concept can be seen everywhere in present complex systems starting from households to the government level which makes it important for individuals to explore this field.

What is it like to study Economics?
Economics, also known as dismal science is a blend of science and art which at times, requires analysis, research, data comprehension, political awareness and is just not limited to theoretical understanding. One needs to put in an amount of effort and it requires experience to understand anything about the economy. It is not hard to identify the multidisciplinary aspect of economics with politics. So, without understanding the political motivation for any economic commentary, it becomes hard to understand what’s happening with the economy. It becomes important to study the political underbelly of the economy before going deep into academia and research.

There are a lot of imperfections ingrained into the system. One question that might cross your mind is, how to plan, to equip yourself with this kind of challenge present? In an ever-changing society, solely dependent on an educational degree in Economics is insufficient to understand the macroeconomic underpinnings of any nation.
For example, an angel investor looks at the economy differently from an academician but that doesn’t make either wrong.
There are a lot of aspects which can’t be pinned down directly to the theory of economics. Therefore, it becomes important to get dirty with numbers to figure out the trends and comprehend the macroeconomic dynamics.

Should you pursue Economics?
After knowing all this, should you pursue Economics? Yes, because if you were not interested in Economics, you wouldn’t have been reading till now. However, if you have been persuaded away from Economics after reading this piece, you probably should rethink because it requires a philosophical bent of mind.

The best place to learn Economics is not in textbooks but in the political discourse of nations, and the study of businesses. Since microeconomics builds the framework for understanding the economy as a whole, it is preferable to study it. To sum up, it is not one explicit thing but an interest in multiple fields which is needed to succeed in the field of Economics. As new technologies are emerging, the contribution of economics to problem-solving and policymaking will increase more than ever.

MSQE Preparation Plan

This plan is made for 9 months. That being said you can shrink the plan depending upon your time availability, proportionately.

Go to Plan (PDF)


Indian Statistical Institute(ISI) has a stellar Quantitative Masters Program in Economics, popularly known as MSQE.In the above PDF, I have given a rough plan to clear the entrance exam for the same.
I secured AIR-7 in this exam(2018).
Book Suggestions:
Maths : https://amzn.to/3e4WyiA | https://amzn.to/3ab0jlg
Tomato (Test of Mathematics at the 10+2 Level ) : https://amzn.to/3ab0jlg
Statistics : https://amzn.to/31Zd7Jd (I'll be adding more books in future)
Econometrics (Statistics for ISI / DSE) : https://amzn.to/3i5fJK5 | https://amzn.to/2HvS4WE Microeconomics : https://amzn.to/2QpP2Vn
Macroeconomics : https://amzn.to/37w1kTM | https://amzn.to/30Tk7qy

PS : The WhatsApp group is no longer operational.

How I Got Admission in MSQE Program at Indian Statistical Institute

It all began in the month of December, 2014. After researching about various courses and career paths since I got into IIT, it took me roughly 7.5 years to decide that I want to do a Masters in Quantitative Economics(MSQE) from the Indian Statistical Institute. My first pointers for MSQE preparation came from a friend who has worked for UBS both as an I-Banker and as well as a Quant.  

January 2015

After gaining the information about preparatory books & coaching institutes, I was all set to start my MSQE Entrance Journey. The first thing I did was to read Varian (Check in Amazon from here) & Mankiw (Check in Amazon from here) like story books in 20-25 days; This was a vital part of my preparation as I am engineer so it was necessary to understand the turf of economics. I also enrolled myself in a coaching institute. 

February 2015

A lot was going on in my life and to add more to it, I wasn't learning much in the coaching institute, perhaps it was structured for economics undergraduates. 

March 2015

As, I had to commute for 1.5 hours one side from my home to the institute, plus the suboptimal coaching experience, I stopped going to coaching and started practising Snyder along with solving previous year question papers. 

April 2015

In no time the exams season started; didn't apply to IGIDR as I wanted to be in Delhi. ISI Delhi, DSE, JNU were my only bets. Sigh! 

May 2015 - July 2015

Whether the exams went great or not never occurred to me and when the results were announced, I couldn't qualify any of the three exams. 

August 2015

Started working again after a break of around 1 years and thought of MSQE preparation along with it.

Sept 2015 - April 2016

A lot of good stuff started happening in my life. I started working for a startup as their Video Lecturer and helped them in animation & stuff too. Needless to say, I could only prepare for MSQE half-heartedly in these months. 

May 2016 - July 2016

Missed IGIDR cutoff by just 0.6 marks. Yes, that's 0 point 6, less than 1!!!

Decided to STOP preparing for MSQE/Economics entrances!

Aug 2016 - Dec 2016

Started a data science project in my company - related to performance assessment analytics in studies as I was working in a coaching institute cum Edtech startup ; Also did a small gig as a data science faculty in a small management institute. 

Jan 2017

Got married!

Feb 2017 - June 2017

Switched jobs, as my Data Science project was not getting support of founders. This was not good for intellectual satisfaction. Decided to prepare for MSQE again!!!

June 2018

And after 1 year, I was inside Indian Statistical Institute giving interview. I messed it up completely. More on that some other time

2 July 2018

After almost 3.5 years, good news greeted me on the screen. I got an All India Rank 7 in ISI Kolkata MSQE Merit List. Dream Fulfilled! 

Also, you can watch the video format of this journey in my YouTube Channel below. Don't Forget to Like, Subscribe & Share!

This is a personal journey and you could be more interested in how to prepare for MSQE. So, here is my blog post about the "Essence of MSQE(Indian Statistical Institute) Entrance Exam"