Madras School of Economics is a very prestigious institute established in Tamil Nadu. The two year PGDM program at Madras School of Economics has two streams.
Research and Business Analytics The specialisation in Research and Business Analytics offers electives such as machine learning, artificial intelligence, big data, and domain specific analytics courses such as medical analytics, in order to cater to industry demand in fast-growing areas.
Finance/ Financial Engineering The specialisation in Finance includes in-depth theoretical and empirical coursework in asset pricing and corporate finance. Electives offered include stochastic calculus, derivatives pricing, computational techniques in finance and market microstructures.
In the first year at Madras School of Economics, the course structure is pretty similar for both the streams. Only in the second year, the stream specific courses start kicking in.
If you look at the course structure, of the research and business analytics stream at MSE, you will see that it competes with the PGDBA course at ISI-IIT-IIM, Masters in Management (M.Mgt) in business analytics at IISc, Bengaluru, and Master of Science in Data Science at CMI.
Course wise, it looks good. And the delivery of the course will definitely be open to subjective judgement. If you look at the placement statistics of the batch 2019-21, the highest of ~16 lakhs and the average of ~10 lakhs, with a 100 percent placement record, is better than the M.A. Program at MSE.
All the flagship Master's programs at ISI are essentially designed to be research oriented. Due to hiring spree in data science, these programs are very popular amongst the recruiters. You can easily get hired for a data science job, if you pursue these from ISI.
They are:
- Master of Statistics (M.Stat) - Master of Science in Quantitative Economics (MSQE) - Master of Mathematics (M.Math) - M.Tech in Computer Science
There are other programs at ISI, which are more data science oriented. Let’s now discuss those programs as well.
- PGDBA (Post Graduate Diploma in Business Analytics) PGDBA is the ideal competitor for IISc’s master’s program of management in business analytics. And in some way, it is much better than the IISc’s mgmt. program, because of the advantage that PGDBA has of three institutions, ISI, IIT and IIM. You will never get the advantage of ISI, IIT, and IIM in any other program as of now.
- MSQMS (Master of Science in Quality Management Science) It is the two-year full time residential Post Graduate program offered jointly by ISI Bengaluru and ISI Hyderabad. The last semester is devoted solely for industrial internship. The course positions itself as a course which is meant to create data scientists. The placement is also very similar to other post graduate courses at ISI.
- M.Tech QROR It is a very popular course of ISI Kolkata and specifically among engineers. This course is essentially about Statistical Quality Control, Reliability and Operations Research. If you want to know more about these terms, before data science was an established career, people in finance or people in operations research, were already doing data science. Hence, you can be sure that this course is also equally good, if you want to get into data science jobs. The internships or the final placements are pretty similar to what is offered to other ISI graduates.
- M.Tech in Blockchain and Crypto ISI has recently started a two-year master’s program which is formally known as M.Tech in Cryptology and Security. If you go through the placement data, you will understand that as the flavour of ISI is in data science, most of the roles which are offered to M. Tech CrS students, are data science roles and that is going to change with the industry awareness for sure.
- PGDSMA Post Graduate Diploma in Statistical Methods and Analytics is a one-year diploma course run by ISI North East Centre in Tezpur and ISI Chennai. This course is primarily designed to cater to jobs which has application of statistics and data analytics as their requirements. Placements are quite good, but not like the other master’s program of ISI, because PGDSMA is a 1 year post graduate diploma.
Everything was going great and streamlined. I was the brain of the data science team at my workplace and that usually happens in a good company. When you have a talent, you get the recognition for it. And when I was working in PwC, I was a data science consultant there. And I was the mastermind behind all of the data science projects in which I was involved. My colleagues like Managers, Associate Directors, Directors, Partners, used to come to me and ask me various concepts and various application of data science and statistics. It was a very great phase of my career.
Down the line, you have to think as to where this is going towards. How this is going to be in medium to long term. When I thought of the medium to long term prospects, my career goals were slightly in different direction. I wanted a more senior kind of data science position, a more statistical data science position, a position which has more applications of economics and finance.
So, my projects involved a mix of data science, marketing, programming, business analysis, and much more. One specific project which gave me a lot of learning as far as 'R programming' is concerned, was a really game changing project for my career. I learned a lot of programming just from that project. That project involved so many R codes, those codes were not simple, but like a puzzle. Once you unravel the key, everything starts fitting into place and then you understand the big picture and then you get the essence of the whole exercise and why it is done. So this was the scenario in my big 4 or PwC data science consulting job.
The offer which I got after, is in a market research firm. The kind of work which I do in this firm now, is in the intersection of economics, statistics and data science.
Everyone has their own personal journey. So, you might want to get into programming side of data science or you might want to get into the model development or statistics side of data science or you might get into a domain specific data science. None of these categories fit really well into my personality traits or my aptitude. In my opinion, it is the mix of all these three and that is what really drives my career moves. In future also, my career moves or project choices will be governed by that idea, that would refine my understanding of my expertise.
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.
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.
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.
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.
