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.