 Once you become a data scientist, it's a great feeling knowing that your days, weeks, months even of learning and studying has finally come to fruition and you've landed that first role. However, this is just a start. Chances are if you're watching this video, you don't want to be any data scientist, but you want to be a great one. In fact, you don't want to be a great one, you want to be one of the top 1% out there. And in this video, I'm going to go over several reasons or several things that you can do to become that top 1% and get ahead of 99% of other data scientists out there. Let's get into it. Now, a concept that the book atomic habits made popular is that improving 1% every day greatly compounds over time. If you improve 1% every single day, then at the end of the year, you will be 38 times better than you were from the beginning. However, if you declined 1% every day, you'd be virtually a zero at the end of the year. This just shows that making a small incremental positive changes in your life can greatly grow to bigger things in the long run. Now, I'm sure you're thinking this sounds really good on paper, but what does it mean practically? Like, you know, it's a good idea that improving 1% or compounds, you see this exponential growth, but how do you really implement this in practice in your day jobs and data scientist? Well, the best way in my opinion is that you simply learn a new thing every single day in your job. Imagine if you look back at the year and you say, hang on, I have learned 365 new things this year. That's amazing. For example, instead of using pandas for your data manipulation analysis tasks, try something like Spark or, you know, Polars, learn a new package for the same processes. Again, you're learning a new skill and it's kind of in your daily flow. So you're not taking extra time out of your day to develop this new idea. If you work a lot in the terminal or command line, try and automate or write aliases for certain commands or execution kind of patterns you do on a frequent basis. When you're doing a code review, ask why someone's implemented something in a certain way. It'll open you up to new ideas and new ways of implementing certain things. If you work in a cross-functional team, pair with a software engineer to get some productivity tips for how to better use your IDE. A good way to keep yourself accountable of this continuous improvement is basically at the end of each day, just write down something new you've learned. There is nothing wrong with doing your day-to-day tasks to a high level. However, to really become an elite data scientist and prepare yourself to that top tier, you need to start taking ownership of products and projects. What this means is that you take a more proactive approach to improving systems and processes. Instead of being told what to do, you actually seek ways to improve things or find out things which aren't working very well and suggest improvements for them. Having this initiative will improve your problem solving, leadership and an array of other skills. Not to mention it will most likely catch the attention of seniors and stakeholders within the company. So, let's give some examples on how you can implement this in your day job. Now, this is mainly targeted towards the juniors and mid-level, but I'm sure the seniors among you will probably find some inspiration on how you can use this in your job as well. When working on a code base, if you find a bug or something that doesn't look quite right, mention it and implement a fix for it. Automate any manual processes, such as getting data or monitoring a model. Volunteer to lead projects no matter how small they are. Research model improvements and bring those to any planning sessions that you may have. Organize things like hackathons that are centered around improving something within the company. The main point is to try and seize the ownership of anything that may come your way. Even though data science is a very technical job, soft skills are essential to help you move up the ranks. All data scientists have great fundamentals in math encoding, but if you can clearly communicate and articulate, your ideas will have a lot more influence. Being able to explain mathematical concepts like neural networks to non-tech savvy stakeholders is truly a superpower. In most companies, data scientists work in cross-functional teams full of software engineers, analysts and product managers. All of these roles have varying levels of understanding of data science. If you can work smoothly within these teams, then you will ensure that the work is carried out in the most efficient manner. Perhaps one of the best benefits that having great soft skills bring you is that of trust and influence. If people trust you, then you can have greater influence on decision making within the company and team. Now, improving your soft skills can be quite difficult because some of them are just simply human nature. However, the following suggestions should apply to most people. If you can, take opportunity to present to multiple audiences. Don't just present to data scientists, present to software engineers or stakeholders or people who just have no concept of the tech field whatsoever. This will get you to understand how you can tailor your presentation style and context to different audiences with different related knowledge to data science. Make an effort to interact with people who you may not have frequent meetings with but are somewhat impacted by the work you do. And finally, try and contribute activity to meetings. Don't be another fly on the wall. Let's say you're a data scientist who specializes in recommendation systems. Now, this is a great area to be in and there's demand for the skill. However, do you know what's even better than being a data scientist who specializes in recommendation systems? Well, it's a data scientist who specializes in recommendation systems and also understands basic software engineering principles and cloud systems to deploy them all to production. What I'm talking about here is the idea of skill stacking. Indeed, describe skill stacking as the concept that individuals can make themselves more valuable by getting a wide range of skills instead of pursuing one skill or talent. In other words, you develop skills which complement and cross over your existing knowledge to making more valuable in the market. There are many great data scientists out there, but how many do you know that also know software engineering, web development, or even MLOps to a really good level? You don't even need to go that broad. For example, if you're a data scientist who knows a lot about recommendation systems, why not try to learn something like computer vision? So now you're a specialist in two different areas that complement each other within the same field. This crossover of skills makes you much more valuable to a company as you can basically do bits and bobs of other roles as well as your own one to a really good level. The question obviously comes in, how do you go about acquiring these skills? Well, the first step is that you find a skill or a technology that you want to learn. If possible, try and find projects or opportunities within your current company that you can get involved with their user's current skill. And if you can't learn this skill or technology in your day job, then you're going to have to learn and study outside of ours. You basically want to find ways of implementing and incorporating this learning of this adjacent skills in your day to day work. To be honest, one of the most simplest ways to become a top 1% data scientist is just to invest more time honing your skills than others are willing to do. Nowadays, hustle culture is seen as a bad thing. And I'm not insinuating that's what you do. Obviously, don't try to learn everything under the sun and spend all day studying. That's not productive nor very good for you and you're likely to burn out very quickly. However, there is something to be said for working an extra hour or two each day to help refine and also learn new skills. Obviously, the question that arises is how do you go about acquiring more time to develop your abilities? Well, this question can be a whole video in itself. And the fact is a whole video in itself. And many people have done videos about time management and making time for learning. But I want to go over a few ways that I implement myself. And I still do to this day to make time for learning new skills. Now, the first one is that if you are a data scientist who works from home a lot, or at least a couple of days a week, then using that commute time as your learning time is a great way to add learning into your daily routine. If you can, wake up an hour earlier. Obviously, don't sacrifice your sleep for this, rather just shift your sleeping pattern. Time block learning segments into your calendar to really encourage you to learn things as it's kind of scheduled in your day. Now, don't get me wrong, you can of course become a great data scientist by performing really well in your day job and using our out of hours time to do anything else that you rather do. I want to reiterate that there is nothing wrong with this approach. And I'm well aware that people have other pressing priorities to do than working on data science in their spare time. This is more for people who are obsessed by data science and really want to get into the top echelon of practitioners. I mean, do you really think top researchers like Andrei Kapafi simply worked nine till five to get what they are today? I bet probably not. You might have noticed a theme around the five topics I've just discussed. They all revolve around two things. That is basically continual improvement or continual learning and basically making time or getting extra time into learning these new things. And I'm sure that's no secret. To become really good at something, you basically got to keep improving and dedicate more time towards it. I mean, there really is no shortcut in becoming great at something. You simply just have to put in more time and more effort than other people over the long run. If you enjoyed this video and want to see more videos like this on this channel, then make sure you click the like and subscribe button and I'll see you in the next one.