How to Find Purpose of Your Life

Deciding what you really want to do in life depends on a range of factors, from your desires and compulsions to your constraints. Figuring out your constraints is the first step in understanding what you want to achieve. Once you identify these constraints, you can understand the limitations of your desires. Recognising these limitations helps you set boundaries, which protect your goals and aspirations.

Establishing Boundaries:
Setting boundaries is crucial because they restrict your search and focus your quest. If you don't know what you want to achieve, creating boundaries will help narrow down your options and clarify your direction. Once you have a broad direction, what you're looking for will become clearer and easier to envision.

Embracing Mistakes:
As a young person, making mistakes is a natural part of the learning process. These mistakes help you understand your limitations and guide you toward your true interests. Each mistake and learning experience, funnels your efforts into a more desirable situation. This iterative learning process is key to discovering what you really want to do.

Balancing Action and Patience:
It's important to be impatient with your actions but patient with the results. Continuously try new things and explore different interests to understand what truly excites you and what your predispositions are. Everyone's journey is unique, and different approaches work for different people. The main idea is to stay focused on your goals despite any distractions.

Handling Distractions:
Distractions are inevitable, but they should be seen as minor irritations rather than major obstacles. Think of distractions as playful interruptions, like a child trying to have fun with you. While they might trouble or irritate you, they shouldn't derail you from your purpose. Your ability to maintain focus in one direction is essential for finding your purpose.

Finding Your Passion:
For me, mathematics is my passion. Whether it's working with an Excel sheet or writing R code, I get excited about numbers. This passion extends to related fields like psychology, economics, and the philosophy of economics. Others might have different interests, such as cricket, and they might know a lot about cricket statistics. This knowledge can be applied to various careers.

Combining Passion and Purpose:
While it's important to find something enjoyable and enriching, it's equally important to apply this framework to your career and life goals. By continuously engaging in activities you like, you increase the chances of discovering your life's purpose. This process involves a lot of trial & error, but it's through these experiences that you find what truly matters to you.

Finding your purpose in life is a journey that involves setting boundaries, embracing mistakes, balancing action with patience, and handling distractions. By focusing on what excites you and continually exploring new interests, you can eventually discover your true calling. Remember, everyone's path is different, and what works for one person might not work for another. The key is to keep iterating & learning until you find what truly fulfils you.

Tools for Youtube Channel Managers & Freelancers

When it comes to creating eye-catching thumbnails for videos, many beginners start with Canva.Canva is a versatile design tool that can be accessed both as a website and a mobile app. It's user-friendly and offers a range of templates and design elements that make it easy for anyone to create professional-looking thumbnails without any design experience.

The main difference between Canva's website and its mobile app is the ease of use. On the website, switching between layers is smooth and straightforward, which can get a bit tricky on the mobile app. Despite these minor differences, both platforms offer similar features, and you can choose the one that best suits your workflow.

After mastering Canva, many creators transition to Photoshop, as it is considered the industry standard for graphic design and offers much more flexibility and control over the design elements. Unlike Canva, Photoshop allows for detailed customisation, which is essential for creating unique and professional thumbnails.

Learning Photoshop:
Switching from Canva to Photoshop can be challenging because Photoshop is a more complex platform. It has a steep learning curve, and watching tutorials on YouTube might not be enough to get comfortable with all its features. If you have someone to guide you in person, it can significantly speed up the learning process. However, with dedication and practice, you can become proficient in Photoshop within a month or two, particularly for creating thumbnails.

Other Tools for Thumbnail Design:
Besides Canva and Photoshop, there are AI tools available that can generate thumbnails. However, these tools are generally not very reliable. Canva serves as a great starting point, while Photoshop is where you can truly polish your skills and create high-quality thumbnails.

 
Managing YouTube Channels and Analysing Analytics:
As a content creator, managing your YouTube channel and understanding analytics is crucial. There are two main tools that creators use to analyse their YouTube statistics: TubeBuddy and VidIQ. Both tools provide valuable insights and make it easier to understand your channel's performance.

TubeBuddy and VidIQ offer premium plans that allow you to access detailed data about your channel. They can analyse your channel's insights and provide personalised suggestions based on your audience's behaviour. These tools help you understand metrics like, 'when your audience is most active', which can be crucial for scheduling your uploads to maximise engagement & views.

 

Using TubeBuddy and VidIQ:
While YouTube itself provides raw data about your channel's performance, TubeBuddy and VidIQ take it a step further by interpreting this data and giving you actionable insights. For example, YouTube might tell you that your audience is active around 6 p.m. on a particular day. TubeBuddy and VidIQ can help you pinpoint the exact time and day for optimal engagement, making your upload schedule more precise and effective.

Although these tools do not have the rights to change your video's title, thumbnail, or description, they offer suggestions on how to optimise them. This can be incredibly helpful for new creators who are still learning how to navigate YouTube's analytics and improve their channel's performance.

Conclusion:
Starting with Canva is a great way to begin designing thumbnails due to its user-friendly interface and accessibility. As you grow more comfortable with design, transitioning to Photoshop can open up new possibilities with its advanced features and customisation options. In addition to mastering these design tools, understanding and analysing your YouTube channel's performance is crucial. Tools like TubeBuddy and VidIQ can provide valuable insights and help you make data-driven decisions to optimise your content and grow your audience. The combination of design skills & analytics,  can enable you to create compelling thumbnails and manage a successful YouTube channel.

How ISI MSQE can help you become a Data Scientist

When you study MSQE course at ISI, you are trained with a significant amount of

mathematical and statistical skills and tools for doing economic research.

And, when you read econometrics or linear programming or let’s say you study statistics, or optimization, you have a primary objective to understand, “how do I do the economic research or economic analysis with these tools”, and “are these tools helpful for me or not”.

Now having said that, it does not mean that you have to restrict yourself to economic research. The idea here is that economics is a lot about model building and number crunching. If you try to understand it, data science is kind of pretty similar to economics and the skills are transferable to each other. So, you can transfer your learnings in MSQE as an economics student to a data scientist and rather I would say, a data professional.

I myself had qualified for both, CMI’s Master’s in Data Science program and ISI Kolkata’s MSQE program. Now, I chose MSQE program at ISI Kolkata mainly because of few reasons. I also wanted to study a bit of economics and finance. One of the things about finance is that, if you do not understand economics properly, then your finance knowledge will be like the people who come to news studios or business new channels and they keep on talking about things without making a lot of sense. So, you have to understand economics and not just the macroeconomics but the microeconomics parts as well. This is so because without a firm understanding of microeconomics, your modelling skills will suffer a lot as only when you study microeconomics properly, you try to understand the understand the significance of economic modelling in situations which involve transactions and incentives.

When you move on from microeconomics to macroeconomics, there are a lot of factors which are governed by your set of assumptions and in the case of data scientists, things are slightly different.

Data scientists are people who are more akin to throwing a lot of computation power and a lot of algorithms to data and they do not really bother about the assumptions in lot of cases. This could be controversial for a lot of people, but it’s pretty accurate that when you are a macroeconomist, your assumptions are like fundamental basis of your every analysis. If I change one of the assumptions of your model or your analysis, your whole analysis will change and that is something which is not really so much apparent in microeconomics or any domain of natural sciences.

So, that is where the departure starts happening when you move from microeconomics to macroeconomics. But that is also the part where data crunching begins when you move from microeconomics to macroeconomics. In macroeconomics, you access GDP data, inflation data and try to make sense of what will happen with these numbers and how will exchange rate, trade surplus or every other macroeconomic factors will affect these numbers.

If I have to give you a basic idea that what is really a very prevalent theme in your

macroeconomic research, its understanding of time series analysis. Now, when we talk

about time series analysis, they are essentially two types of time series. One is univariate time series, in which you just have one unit and for that unit, you are observing a particular feature at difference points of time and just noting it down.
There could also be a time series in which you have multiple units and for all those units, you are observing a feature and noting it down at all time intervals. This time series becomes a panel data time series and earlier one was a univariate time series. If you go into the deepest part of econometrics and time series, you will have to deal with a lot of panel data. This is the part which is not really touched by data scientists but it is touched by econometricians and macroeconomists.

And, the time series forecasting or analysis where you calculate the trend seasonality or do the forecasting, is actually dealt by a lot of data scientists as well.

The problem here is that, a lot of people think that you need a lot of coding expertise in data science. That is true to an extent and it depends on the company and on the role. A lot of companies require software engineers and as they want to market themselves well, they label it as a data scientist role hiding it beneath a lot of jargons. So, you need to be sure that what kind of role you are getting into, rather you have to understand whether this is a software engineering which you are applying to or really a data scientist role. Not all companies will be very honest and upfront but all good companies are pretty much upfront what they expect and require from you.

Whatever you study in MSQE, which is a quantitative economics course, a lot of things are related to number crunching, data analysis, mathematical and statistical skills and all is for economics research and you can very well use it to become a data scientist.

As I have already told that I wanted to become a data scientist but I had a keen interest in the markets and finance, that is why I chose the MSQE program.

If you have any doubt and queries regarding this topic, feel free to comment down below.

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!