How to make a Data Science Resume - Tips by a Big 4 Analytics Consultant

I used to work as a data science consultant in one of the Big 4 Consulting Firms, with a master's degree in quantitative economics and B.Tech from IIT Kharagpur. Having conducted numerous interviews for data science positions, I'd like to share insights on crafting an effective data science resume.
It's not just about listing experiences, skill sets, programming knowledge, and data analysis expertise. Knowing how your resume is perceived, how it is shortlisted, and how it guides the interview process is invaluable information that can significantly enhance your chances.

A good resume should always be honest. If you include false information in your resume, a skilled interviewer will likely notice. I suggest being truthful, but that doesn't mean your resume should be empty. You can still add some interesting details to make it more appealing. First and foremost, it is crucial to define your role within the realm of data science. The data science field has three distinct profiles:

1. The Modeler:

   - This individual is primarily focused on the statistical aspects of data science, specialising in the development and optimisation of models.

   - Responsibilities include selecting appropriate models, determining optimal data sets, deciding on the number of observations, and choosing relevant features.

2. Data Engineer:

   - Specialising in the handling of data, the data engineer is responsible for tasks such as, filtering columns and rows, and managing ETL processes (extract, transform, load).

   - Proficiency in SQL, Oracle, and database management is crucial, along with the ability to collaborate effectively with data scientists to enhance the overall capabilities of the data science team.

3. Implementation Person/ML Engineer:

   - This role involves the practical implementation of models developed through the collaborative efforts of statistical data scientists and data engineers.

   - The foundational work in statistical data science and data engineering provides the basis for implementation, which includes deploying the models.

Having understood these things, talk about your education, add interesting points like certifications, courses, and projects related to data science, data engineering, or ML engineering. Next, discuss your internships and work experiences, emphasising those that match your target skills. If you don't have a lot of skills or experience yet, try to learn about what skills you need and relate your experiences to what you want to do.

Imagine you've created models but never deployed them, and now you want to show you're a good implementation person. Explore the implementation tasks carried out in the project. Then, describe in a few lines how the model was deployed. Remember, you don't have to be an expert in everything, just be honest and aware of what happened.
Avoid creating a resume by simply copying a format; craft one that is uniquely yours. I won't delve into typical issues with CVs and resumes, such as grammar, spelling errors, and accurate numbers, as these are implicitly understood.

In case an interview doesn't go as planned, interviewers often offer one or two opportunities for candidates to demonstrate their capabilities. Remember, it's not just your resume that secures a job; it's you who will contribute to the company. Demonstrating teachability and dedication can significantly impact your performance. Create a well-rounded, truthful resume, and focus on showcasing your abilities in the competitive data science job market. Don't think about what you lack; instead, emphasise how you can market yourself effectively.

Passing CFA Level 2 - Nine Mantras to Pass CFA L2 [Based on August 2022 Attempt]

In this post, I will tell you Nine Mantras for passing the CFA Level 2 examination. Those nine crucial mantras are mentioned below.

Cover the Syllabus 9 times
The first and foremast mantra is to cover the syllabus Nine times. Why nine times? There is a special reason behind it. The CFA syllabus is humongous. I am not saying that you need to prepare for the entire syllabus in a comprehensive way for nine times. You have to understand the length and breadth of all the topics and concepts in the curriculum. So, that you are in a position to relate to whatever is being asked in the examination. You do not have to take the pressure of remembering everything. You need to cover the entire syllabus as many times as you can and preferably nine or more than that.

Do not do Research on Quantitative Methods
I know quantitative methods is interesting, it has a lot of concepts and it is very demanding as well. You will be tempted to do a lot of reading on your own and try to understand that why certain things happen the way they happen and what are the assumptions and why are such assumptions used over here and what will happen if these assumptions are violated. Do not go outside of the scope of the curriculum. Quantitative methods encompasses a lot of things and with my experience of studying quantitative economics, I know that the content of quantitative methods is of two to three semesters. So, you have to understand that this is the kind of beast you are dealing with.

Corporate Issuers as the easiest topic
Corporate Issuers is the easiest topic, don’t miss it. Read everything thoroughly in corporate issuers. How can you miss something which is very easy.

Financial Statement Analysis (FSA) and Alternative Investments are text heavy
Financial Statement Analysis and Alternative Investments are heavily terminology dependent. So, there is very less mathematics and a lot of text. So, keep on reading as much as you can, but do not beat yourself up if you are not able to learn everything. 

Fixed Income, Derivatives, Portfolio Management
All these topics will consume most of your time, because they are heavy in terminology, heavy in maths and heavy in conceptual understanding as well. Do not take these topics lightly. You have to focus a lot in these three topics. 

These are the three pillars which you have to make strong- Fixed Income, Derivatives and Portfolio Management. 

Economics and Equity give the best ROI of your time invested
They will ask you to understand the concepts, remember the theory, and remember the terminology, but you will not find it difficult. You will find it easy and that will increase your score in CFA Level 2 examination. So, economics and equity are going to be your friends in your journey. 

If you leave ethics, you will cry
Try to read at least 20 to 30 minutes on a weekly basis, and it is not difficult, it is just requiring your attention. Do not leave it. 

Revise, Revise and Revise!
It means that you need to constantly revise the curriculum. So, if let’s say you have six months of preparation time slot, so revise after every couple of months. 

Calculator is your friend
Always know how to do any activity on your financial calculator, be it standard deviation, present value, future value, annuity, because in the exam, you will have to do a lot calculations and if you are not hands on with your financial calculator, you will hate yourself for it, because that is the silliest mistake you can commit. 

Why it took me 3 Attempts & 9 Years to pass CFA Level 2 ?

In this post, you will come to know about my journey of clearing CFA Level 2. Why it took me three attempts and 9 years to qualify level 2 of the CFA Certification Exams? Let’s discuss that.

Lost interest in CFA
The simplest and the easiest explanation is that I had lost all interest in the worth of CFA certification in my profile. I was into teaching. I was teaching physics. My career was not in finance. That is a big reason.

Apart from that, my 2 failed attempts were also characterised by my inefficiency or deficiency in the way of studying for this exam.

Schweser:
First inefficiency was complete reliance on schweser. Schweser is a CFA Institute Prep Provider. I completely relied on schweser for all the preparation and that is a suboptimal strategy. Schweser must only be referred for Revision and that too in the initial stages of the preparation because revision comes in two-three stages. There are stages of revision. The initial revision has to be done through schweser.

Problem Solving:
The second inefficiency from my side was solving a lot of problems. I practiced a lot of problems without understanding the concepts, that is not going to help you. Internalising the concepts and solving a lot of problems, both are needed. You cannot skip either of them.

Read a lot:
Third inefficiency was trying to read everything and remember everything of the CFA curriculum. My attempt to remember everything which was written in the CFA curriculum was a big mistake from my side. That is humanly not possible. The better approach is to get the essence of whatever is written because CFA curriculum is designed for people coming from different walks of life and all of them want to get into finance with the help of CFA certification. You need to cover each and everything written and prepare your concepts according to the type of questions which CFA is going to ask you.

So, before you get into the CFA program, try to get motivated for the program. If you are not having any worth or value for the certification, you will ultimately fumble. Even if you do not commit any kind of mistake in preparation, your lack of motivation will always be a hindrance.

 

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!

Data Science Course at Madras School of Economics (MSE)

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.

Becoming a Data Scientist at Indian Statistical Institute (ISI)

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. 

Why I Quit my High Paying Big 4 Data Science Consulting Job

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.

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.

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!

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.