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.

Will Artificial Intelligence Kill your Jobs ?

We have been observing rapid growth in AI in recent times. A lot of innovations and new technology is entering the market. With the rise of AI and similar technologies, the most discussed question currently among the masses is, ‘Will AI take away your job?’

The short answer is NO!!

Let’s understand what artificial intelligence is. AI can be considered as tools, technology, algorithm, method, etc. you call it whatever you want to call it. The main goal of AI is to mimic and simulate human intelligence. Primarily, it is used to do tasks that require human intelligence. 

In this era of technology, we humans are the medium. What does that mean? It means that we channelise our intelligence & creativity to create and produce things, with the help of tools and technology. These tools and technology can be AI, coding, gardening, carpentering, painting, etc. 

We can’t deny the fact that intelligence and creativity with the help of technology produce very efficient and effective solutions. A lot of innovations are possible when creativity and intelligence meet technology. The output of the solution varies from solving mathematical problems and solving policy issues to creating cutting-edge technology to make our lives easier.

Artificial Intelligence is great at recognizing patterns. It lacks in building something absolutely new. Most jobs and tasks require a subjective angle. Whether the task is done or not is not the question. The relevant questions are, Do I have to go with this approach? Is this solution more effective than the existing one? Do I like the work? So, to answer these questions, there is always a need for a human angle.

So, is it safe to say that AI will never take away jobs? No! AI will eat away repetitive jobs. 

Mechanical jobs can also be replaced by automation. 

AI will take away jobs that have patterns as AI is good at recognizing and learning the patterns. The truth is, most jobs and work require human intervention so AI will help them. AI will go parallel with traditional jobs that require human actions. AI can never replace experience. 

A person with a great understanding of business can use AI to bring better solutions. Understanding the customer, making the best decision for consumers, understanding the strengths of the partnership, etc. can never be done by AI or any similar technology.

AI will also create new jobs that will be technology related. AI and similar technology will boost economic growth as more and more work will be done by minimum hands in less time. Firms adopting the latest technology will gain huge profits. And the buying capacity of the consumer will also increase as the price of the product will go down increasing the demand. So, jobs will be created to cater to the demands. 

Innovations and technology have always replaced human beings in the past, especially after the industrial revolution but these innovations have also created new jobs. Always remember that Artificial Intelligence is a tool, not a threat. Make AI your friend and you will always enjoy the rise of Artificial Intelligence.

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 Coding needed for Data Science? - Role of Programming in Data Science

Data science is a combination of mathematics & statistics, programming and domain expertise. The rising penetration of high-speed internet has fueled the growth of people learning programming languages. 

So, in an era where coding is considered a life skill, one question arises: Will coding skills help in data science career? The short answer is 'Yes!', it'll help. The point to remember here is that it is only 40% of the task.
The rest is explaining the mathematical & statistical basis and findings to stakeholders and decision-makers. Domain expertise and mathematical & statistical understanding will help you along with coding skills.

Coding skills come in handy for data problems. A lot of basic things like data cleaning, data manipulation, loading libraries, etc. require programming knowledge. Good coding skills will help you circumvent the issues that initially come with data science problems. Many data scientists, regardless of their knowledge of the necessary steps required to solve the business problem, are not good at coding. So they face difficulty loading & manipulating the data, and getting the necessary libraries for implementing the desired solution.

Many data science problems can be solved with the help of libraries of programming languages like Python and R. If you are good at coding, you'll be able to troubleshoot the issues & problems very well, unlike those who are good at mathematics & statistics but don't have knowledge of loading packages, libraries, creating environments, etc. 

But when you present your solution to the end user, they generally ask very basic questions. These questions can be bucketed into two categories:

1. Domain-specific question

2. Mathematical & statistical assumptions of the solution

So, it is essential to understand the domain problem as well as the mathematics & statistics behind your solution. This puts you in a position to explain the solution to the end-user and stakeholders, it could be a data analyst or someone in a higher position in the company, like the person who is taking charge of sales or marketing. Understanding mathematics & statistics also helps in deciding the steps needed to solve a given problem. It also helps in determining which algorithm to prefer over another. 

Coding will help you to some extent. You still need a mathematics & statistics foundation and domain expertise. If you don't have one, you will have to gain that skill. 

If you ask me, I'll advise you to start with mathematical & statistical foundations. Domain expertise will come through experience. There is no substitute for experience in terms of domain expertise. But mathematics & statistics can be learned in a limited amount of time. It doesn't require 7-8 years of experience, unlike acquiring domain expertise. 9 months to 1.5 years are more than enough for you to master statistics & mathematics.

So, the conclusion of this post is that coding will help you a lot, but coding is not the entirety of data science.

ISI MSQE Job Profiles - Prospects after Masters in Quantitative Economics from Indian Statistical Institute

Are you curious about the job profiles that are offered at Indian Statistical Institute (ISI)?
If yes, you are on the right place. Here, I will introduce you to some of the potential job profiles offered to an ISI MSQE graduate.

Financial Risk
There are various investment banks and financial institutions which recruit various young minds for the financial risk roles.The CTC offered under this job profile depends on the company; It varies a lot from company to company.

The job profile offered under the financial risk role is the most relevant role for an MSQE student.

Insurance & Actuary
Though this job profile is not directly related to the MSQE curriculum, but it’s a perk of studying in ISI that companies do come in ISI for recruiting students from M.Stats for the field of Insurance & Actuary. So sometimes, they recruit students from MSQE as well. It will be prudent if you clear few actuarial papers to fulfil the eligibility criteria required by some companies in the field of Actuary & Insurance. It’s an undeniable fact that these jobs pay a lot.

Data Science & Analytics
Honestly speaking, there is a lot of buzz about Data Science, ML, AI, etc. Also there is a lot of confusion among youth about real definition of Data science and Machine Learning. Students remain confused that does data science has any relation to Computer science & Software Engineering.
So, as a result of these misconceptions, sometimes, there is expectations mismatch as well.
There are a lot of tech companies who come to Indian Statistical Institute for fulfilling their hiring needs related to Data Scientist and Data Analyst Profiles.

IIT v/s ISI v/s IIM
Many of you might wonder whether the roles offered in ISI MSQE is somewhat similar to the roles offered in Top IIMs and IITs? So in my opinion, the roles offered in ISI MSQE is between what an IITian could get and what an IIM graduate could get. What I am trying to say is that there are few roles which are offered to IITians, ISI MSQE students as well as IIM MBA graduates. But there are very few roles which is common in all the above three categories. Majority of roles offered to MBA graduates are quite irrelevant for MSQE graduates. Companies coming to IIMs mostly have more or less business outlook, sales outlook, marketing outlook and more like a manager outlook in their hiring. But companies coming to recruit ISI MSQE students search students for technical roles from the analytical perspective. But in spite of all these facts, there are many profiles for which an IIM and ISI graduate both can be recruited. It's not all about the curriculum, but the batch strength also matters a lot. Maximum strength of an ISI MSQE batch is approx. 30 but on an average there are around 300 students in each batch of IIM MBA program. So job profiles is also very diverse for IIM students.

In my opinion, out of all these job profiles discussed above, financial risk is the most relevant role for an ISI MSQE student. Apart from these profiles, you can also go for research, PhD, or public sector jobs after doing your masters in quantitative economics from Indian Statistical Institute (ISI).

 

Certifications Vs Internships Vs Degree to get a Data Science Job in 2023

Data science has become a niche word in the job market.
Being the versatile field it is, Data Science is getting an unflinching attention from today’s Youth. Despite its wide scope across the globe, it is still the proverbial 'Rocket Science' to the people, especially if you are beginning afresh. This is merely due to the widespread availability of resources and options.

Diving into Data Science, while it sounds a lot intimidating, it needn’t have to be that way. Just a few steps in the appropriate direction and you are good to grab the biggest of opportunities.

Let us look into the simplest & effective preliminaries of securing a Data Science job. Most Data Science jobs require very less knowledge. It demands various skill sets like programming (R, Python, Julia, etc.), Excel, Presentation, etc.
How do you develop these skill sets?
Well, here are 3 ways which can get you closer to your dream job in Data Science;

1. Certifications: Certification courses have gained a huge momentum in recent years. They generally provide content and assessment on a package basis. But their easy availability might often dent the weightage of the certificate obtained and may not prove to be effective.

2. Internships: Internships are beneficial as well as valuable since you not only sharpen the required skills but also do the work of Data Scientists by doing the practical fieldwork. This, unlike certification, reduces the time lags between the time of learning the skill and actually applying it.

3. Traditional Learning: In Traditional structure of learning of full time programs, an ideal mix of theory, technical skills and soft skills provides a fertile ground for your Data Science Career. This is the simplest and the least uncertain path to learn the skills related to Data Science & ensure a job. The USP of this type of learning is final placements. It also gives a cohort of people which is effective in discussing and learning skills.

However, these aren’t the only ways to learn the necessities of Data Science. Whilst either of the above strategies may work for you, a mixture of all the above might be the only way for the other people.
This is being said so that the information provided above proves only to be a food for thought and does not deter your thirst for Data Science.

Data Science is an emerging field and evolves every single day, so it is essential to stay relevant. While it doesn’t just have to be a degree or a course, it can be as simple as keeping tabs on the recent developments in the field.

Remember, it is the “Small steps, every day.” So, make sure you take that first step with this very little ounce of knowledge you just gained.
Best Wishes!