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!

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!