How I Became A Data Scientist (No CS Degree, No Bootcamp)

it's no lie that being a data scientist is probably one of the coolest jobs out there at the moment particularly with the rise of AI last year in this video I want to detail my journey into how I became addicted scientist and offer advice for how you become one in 2024 it's important to mention that not everyone's journey is the same but hopefully this video will strike some inspiration or some general guidance on how you could become a data scientist we'll start with covering my background how I discover data science How I Learned data science how I got my first job and general advice for you looking to break into the field so let's get into it I come from quite a heavy math background my mom has a math degree both my grandparents studied physics and my great Grandad was even engineer so I was always naturally inclined and pushed into that direction of stem subjects I remember when I was 12 years old is what really got me into physics I was watching The Big Bang Theory and they were talking about things such as quantum blute gravity general relativity and these topics were really interesting so I went and Googled all about them obviously I didn't know much of anything at that point but they were still very interesting to me and that's when I decided to pursue physics for my gcss I got four a Stars 3 A's and 5 BS which is not bad at all however it wasn't exactly genius level and at that time I thought was much smarter than I was and my work eth think was really poor my 4 a stars were in maths physics further maths and chemistry so those were the four subjects I took to a level at a level nothing much changed I was still being quite lazy wasn't working too hard because in reality I thought I was smarter than I was I was quite delusional at the time and I thought I can get into the best universities even though like I said I wasn't working very hard so in the UK you get AO choice of five universities that you can apply to so my five choices were Oxford Imperial Nottingham Manchester and Southampton now obviously both Oxford and Imperial both rejected me as they are the top institutions in the world and our point in time my grades and work ethic like I said went very good however Manchester Southampton and notam all gave me offers I firmed Choice Manchester which required me to get AAR AAR a and my insurance Choice was Southampton where I needed 3 A's so results day comes around for my a level results and let's just say I didn't do too well as I was expecting I got a star in maths a b in further maths but a c in physics so for someone who wants to study Physics at University that's not great and Manchester and Southampton both rejected me in the UK we have something called clearing now clearing is where on results day universities may have some space on certain courses and people who didn't get into their firm or Insurance Choice can apply to those universities and luckily the University of Sur offer me a place to study Physics of astronomy come September 2017 at s is where I really learned that there is no substitute for hard work I know it's a cliche but it's true in my first two years I had a much better work ethic than I did in my school days and in my first year I got off first and in my second year I also got off first at the end of my second year I got accepted onto the master's program as part of the Masters program you have to do a year of research it's kind of like a mini PhD and my year was done at the national physical laboratory in Teddington or MPL for short and my thesis was on measuring air temperature gradients using acoustic thermometry during this year I enjoyed it however physics research from that small snippet I had wasn't quite I envisioned it being like and in real reality I found things to move a lot slower than I would have liked and that wasn't quite for me and I kind of fell out of love of physics I still remember to this day exactly how I discovered data science after I com back from a day of work at npl a video appeared on my YouTube homepage and it was deep Minds alphao documentary on where they trained an AI bot to be the go world champion leak of all after watching a documentary I became fascinated about how they train this this AI like what Al GRS did they use and what kind of process did they take I was looking into reinforcement learning deep learning markof chains all these things obviously at that point in time I didn't understand everything but I found it also interesting so I looked online of basically opportunities and what kind of people or what kind of professions use machine learning and that's how I stumbled across data science like most people I had the age- old question how do I learn data science data science cuts and intersects into so many field maths statistics computer science that it seems overwhelming however if you break down you're learning small chunks is very doable coming from a physics background I pretty much had all the prerequisite knowledge I needed I new linear algebra I new calculus and I new statistics so that means I could straight into the machine learning and and understanding how the algorithms work the first course I took was Andrew n's course called The Machine learning specialization I took this course back in 2020 and this is when it was still the 2012 version and all the exercises were in octave or mat lab it's been revamped and it's got more Cutting Edge algorithms in there such as reinforcement learning recommended systems and it's also taught in Python at this point in time I only only had experience in one programming language and that was Fortran so we got taught Fortran in my first 2 years of University and for those of you who don't know what Fortran is it's probably one of the oldest highlevel programming languages out there it was written in the 1950s with it being my first programming language it made me not really like coding that much because everything was manual hard there's not many packages available for Tran reflecting on it learning forr was kind of a blessing in disguise because it really got me to really think programmatically and like I said everything had to be done from scratch and so when I went about learning python it was so much easier for me the way I learned python was by simply contacting one of the lecturers at my University who taught a computational Physics course basically asked them for the course note and it was just an introduction to python in reality any intro to python course would have been sufficient I also took the tutorial Sprint uh python course and it basically Tau me all the things that was in those lecture notes the main things I learned were python syntax functions Loops classes all kind of the regular things you need to know behind a programming language to design or build anything I then went to learn a bit more of the data science specific packages numpy pandas M poop lib and also psyit learn these were done on the kagle courses and these are very useful these are kind of like the main packages you would use day to day as a data scientist after python it was then to learn the other language of data science which is SQL the way I learned SQL was that again I took the tutorial Sprint uh online course to SQL it took me around a few days and to be honest that course literally covers everything I use now in my day-to-day job it teaches you all the basics and more some that you will likely use in any interview and also in most jobs uh nowadays when you're a data scientist after upscaling in machine learning Python and SQL I then basically started building some really simple projects what I did is that I would get some data set from kagle and I would just randomly apply just loads of machine learning models to these data sets I will link in the description below a lot of these projects but comparing them to my ability now they weren't very good but they allowed me to get my hands dirty and just try out loads of models I built linear aggression listic regression decision treats just a range of algorithms and it really taught me how they work and how to apply them to a real life problem the hardest part by far is securing the first job you dedicate a lot of time to learning all these skills with the hope that you will learn that first role I'm not joking when I said I appli to over 300 roles in my final year of University trying to get this first data science job so when it comes to your first role I I honestly believe it's purely a numbers game you really just have to put yourself out there have practice in interviews have practice in these take of assessment to land their first role I've got my first role at an insurance company kind like mid-level sites in the UK it wasn't some fancy Fang or Quant hedge font like I said it was just a regular firm that was you know really good and I worked with some amazing people you don't need to work at one of these top companies particularly at the start because in reality is in some of these smaller companies you may learn more because you may be asked to do more things be more Hands-On of a lot of the infrastructure like anything in life it's really up to you to Excel and put an effort you can not grow at all in big companies and you can grow a lot in small companies the final thing I want to discuss is how you can stand out as a data scientist in my opinion these three things are very simple to do and they give you so much more rewards than the effort you put into them the first one is make sure you have a GitHub profile and populate it on the screen is what mine looks like again mine's not that fancy but it does have some you know it looks good and it has some nice things added to it what what languages I know U what I do and some basic reposts of my past projects you can do the same easily in fact just my template and add some basic repos of you basically learning python it doesn't need to be too complicated but I promise you most people applying for entry-level jobs won't even have this the second one is write a blog post I'm still amazed at why people think this is so much harder than it really is the goal of writing a blog post particularly if you're just trying to land a job you're not trying to make the post go viral it's more just to Showcase your learning and show that you're interested in your you're curious and willing to document your work the simplest way you can write a blog post is for example say you learn how to implement functions in Python write a blog post about how you implement functions in Python it really is how simple don't over complicate it the final one which is a bit more tricky and that is enter a kagle competition and do reasonably well now what kagle will show to the employer is that you're able to break down a business problem into code in a data science way and that's really useful because your job as a data scientist is to unify business with data and to solve that problem the thing I want to stress is that there is no one best way to become a dicta scientist but my journey hopefully gives you some inspiration or some guidance or even some tips on how you can tailor your learning or the steps you can take to become one in 2024 I highly recommend you action on those three key things I mentioned at the end of the video that is get GitHub profile write a simple blog post and maybe even enter a kago competition these three things will set you apart from pretty much every other candidate particularly for entry level jobs so I really really recommend you try them if you enjoy this video and want to learn more about data science and how to break into data science then make sure you click the like And subscribe button and I'll see you in the next video

Channel: Egor Howell

Views: 62,593

Keywords: Career, data science, learning, coding, coding career, become a software engineer, software, data, data scientist, machine learning, programming, how to code, learn to code, python, sql, how to become a coder, data analyst, artificial intelligence

Id: QbeY0_y2C54

Length: 12min 28sec (748 seconds)

Published: Fri Jan 05 2024