How Data Science Projects Pay Off

Key Takeaways

i've made a lot of videos about data science projects on my youtube channel i've talked about which ones to start with to learn from which types of projects can land you a job and how to go about finding new projects that are original it recently occurred to me that i haven't made a video explicitly talking about why you should do these projects and how they pay off for you in learning your job prospects and possibly financially this is that video at the end i'll also give you some real world examples of data science projects paying off so be sure to stay tuned for that the first way that data science projects pay off is by compounding your learning it's great to learn through courses in fact i encourage it but you should apply this knowledge through projects of your own it is proven that we learn most when we stretch our capabilities projects are the perfect playing field to take you to the edge of what you're comfortable with in addition to that projects can help you to narrow the scope of your learning one of the biggest challenges that i see new data scientists facing is that there's just so much to learn in the domain and it can get very overwhelming projects allow you to narrow down the entire feature space to figure out what's relevant to the specific problem that you're working on this has certainly helped me to break through many of the hang-ups that i've had during my own data science journey when it comes to learning projects offer you rare immediate feedback during projects you'll run into obstacles you'll quickly need to see the areas that you need to work on when you start confronting these things you'll develop the skill of learning exactly what you need to solve the problem at hand and i'm certain that you'll find this skill far more valuable than just gaining a broad high level understanding of the overarching concepts of data science finally through projects you'll better understand how different parts of the data science life cycle are connected often we learn about data collection data cleaning data analysis feature engineering model building and model production in isolation when we learn about these things independently we rarely get a glimpse into the handoffs between these processes i would argue that these transition points are some of the most important to understand as a new data scientist they also happen to be where most of the problems arise in real world data science before we move on to the next payoff let me know in the comment section below what project you're working on letting others know your project makes it far easier to make groups and to get these things actually built now landing a job is probably the most obvious way that data science projects pay off if you don't already have a job in a relevant field it can be extremely difficult to gain experience to land your first data science position in my mind data science projects are legit real world experience many hiring managers see this the same way as i do actually in some ways projects can show things that on-the-job experience often can't if you do a data science project you have to flex the critical thinking muscles of identifying a clear and relevant problem this is an extremely valuable skill on the job this happens occasionally but more often than not the problem statement is already fairly well defined data scientists that have demonstrated this skill are in high demand on teams something that i've gone pretty in-depth into is how difficult it is to evaluate data science talent because data science is a hybrid of multiple fields each company values coding skills math abilities and business intuition at varying levels the projects in your portfolio can showcase these aptitudes far better than almost all of the traditional interviewing techniques it is very likely that you'll be asked about your projects during the interview too and guess what they're a great conversation starter honestly i could probably go all day on the value of projects and getting you hired but the last real area that i wanted to touch on is related to your marketability by creating projects and sharing them you can effectively have recruiters coming to you rather than you doing all the work reaching out to them there are also great icebreakers for cold reach out conversations more on this with my podcast episode with jeremy harris if you're not listening to the ken's nearest neighbors podcast already definitely give it a try i have some pretty awesome guests coming in although i don't think it should be the main focus of your work there are also direct financial returns associated with data science projects if you're creating something that's useful to you it's very likely that it might also be useful to other people as well you may just create a company without even realizing it to be completely transparent most of the time side income comes from the content creation side of data science projects this could be making a post on medium making a video on youtube or instagram or something like that if enough people interact with any type of content on the internet it's possible to monetize it it could be said that my entire youtube journey was kicked off by a data science project that i did my very first video was in fact a project that i created for one of my deep learning classes my youtube project really pays off if you like this video and subscribe to the channel so we can beat the youtube algorithm together i would have never guessed in a million years that a simple video that i made two three years ago would have kick-started this incredible community that i'm you know so grateful to be a part of today if you have entrepreneurial goals projects are also a way to prototype your idea and potentially make it a reality thousands of ideas start as personal projects and morph into real companies oculus and github are project success stories to know okay at the beginning of the video i promised some examples fortunately the examples are racking up at a pretty alarming rate as i interview more data scientists for the ken's nearest neighbors podcast each week the first example comes from this week's podcast eugene yan his performance in a kaggle competition and a conversation at a meetup directly led to a work opportunity with a startup my other friend nick wan had one of his pet projects published in the local newspaper which also led to a new data science job opportunity and finally my work in building golf models in my free time directly led to my opportunity with my current company the full stories on these are links below the broader topic of data science projects is huge and i recommend checking out some of the other videos that i've made on the topic that i've linked in the description i go in depth into what makes a great data science project what types of projects to work on and how to differentiate your projects from the pack thank you so much for watching and i hope that this video helps to excite you about your next data science project until next time good luck on your data science journey