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

Summary

This video details the speaker's journey into becoming a data scientist, offering advice for aspiring data scientists in 2024. With a strong math and physics background, the speaker shares their academic struggles and eventual discovery of data science through an AI documentary. They emphasize the importance of hard work, learning in small chunks, and the practical steps taken to master machine learning, Python, SQL, and data science packages. The speaker also discusses the challenges of landing the first job and suggests three key strategies to stand out: maintaining a GitHub profile, writing blog posts, and participating in Kaggle competitions.

Takeaways

• 😀 The speaker emphasizes the allure of being a data scientist, especially with the rise of AI, and offers personal insights into the profession.
• 🌟 The journey into becoming a data scientist is unique for everyone, but the video aims to provide inspiration and general guidance.
• 🏫 The speaker's background in mathematics and physics was foundational to their path in data science, highlighting the importance of a strong STEM foundation.
• 📚 Early exposure to complex scientific concepts through shows like 'The Big Bang Theory' sparked an interest in physics, leading to academic pursuits in the field.
• 🎓 Despite not getting into their top university choices, the speaker's determination led to a place at the University of Surrey, where they learned the value of hard work.
• 🔬 The realization that physics research wasn't as envisioned came from a year of research at the National Physical Laboratory, prompting a shift in career focus.
• 🤖 A documentary on DeepMind's AlphaGo inspired the speaker to explore AI, leading to the discovery of data science as a field of interest.
• 📈 The speaker's physics background provided a solid foundation in prerequisite knowledge for data science, such as linear algebra, calculus, and statistics.
• 💻 Learning Python and SQL was crucial for the speaker's transition into data science, with practical experience gained through online courses and projects.
• 🔑 The speaker applied to over 300 roles to secure their first data science job, underscoring the importance of persistence in job hunting.
• 🏆 Standing out as a data scientist can be achieved by having a GitHub profile, writing blog posts, and participating in Kaggle competitions, showcasing practical skills and initiative.

Q & A

What is the speaker's background in terms of education and family influence?

The speaker comes from a strong math and science background with a family history of studying physics and engineering. They were naturally inclined towards STEM subjects due to their family's academic pursuits.

What sparked the speaker's interest in physics?

The speaker's interest in physics was sparked at the age of 12 when they watched 'The Big Bang Theory' and became fascinated by topics like quantum mechanics, gravity, and general relativity.

What were the speaker's A-Level results and how did it affect their university choices?

The speaker achieved four A*s in Maths, Physics, Further Maths, and Chemistry, and three As and five Bs in other subjects. They initially applied to Oxford, Imperial, Nottingham, Manchester, and Southampton, but only received offers from Manchester, Southampton, and Nottingham. They chose Manchester as their firm choice and Southampton as their insurance choice.

How did the speaker's performance in A-Levels affect their university admission?

The speaker received a C in Physics in their A-Levels, which led to rejections from both Manchester and Southampton, their firm and insurance choices, respectively. They were then offered a place at the University of Surrey through the clearing process.

What was the speaker's experience like during their undergraduate studies at the University of Surrey?

At Surrey, the speaker improved their work ethic and achieved first-class honors in their first two years. They were accepted into a master's program, which included a year of research at the National Physical Laboratory.

How did the speaker discover data science and what inspired them to pursue it?

The speaker discovered data science after watching a documentary on DeepMind's AlphaGo on YouTube. They became fascinated with how AI was trained and started researching machine learning, reinforcement learning, and related fields.

What prerequisites did the speaker have that helped them in learning data science?

The speaker had a strong foundation in linear algebra, calculus, and statistics from their physics background, which allowed them to dive directly into machine learning and understand the algorithms.

What was the first course the speaker took to learn machine learning, and how did it impact their learning journey?

The first course the speaker took was Andrew Ng's 'Machine Learning Specialization'. It provided them with a solid foundation in machine learning algorithms and introduced them to programming in Python.

How did the speaker learn Python and which resources did they use?

The speaker learned Python by asking a university lecturer for course notes from a computational physics course and taking an online course called 'Tutorial Sprint Python'. They learned basic Python syntax, functions, loops, and classes.

What was the speaker's approach to learning SQL and how did it benefit them?

The speaker learned SQL through an online course called 'Tutorial Sprint SQL'. The course covered everything they use in their day-to-day job and helped them prepare for interviews and work as a data scientist.

What strategies did the speaker use to secure their first data science job, and how challenging was it?

The speaker applied to over 300 roles during their final year of university to secure their first data science job. They believe that securing the first job is a numbers game and requires persistence, practice in interviews, and taking assessments.

What advice does the speaker give for standing out as a data scientist, especially for those looking for entry-level jobs?

The speaker recommends having a GitHub profile showcasing projects, writing blog posts to demonstrate learning and curiosity, and participating in Kaggle competitions to show the ability to solve business problems using data science.

Outlines

00:00

🌟 Journey to Becoming a Data Scientist

The speaker shares their personal journey into the field of data science, which is currently a highly sought-after career due to the rise of AI. They aim to provide inspiration and guidance for aspiring data scientists, emphasizing that everyone's path is unique. The speaker's background in mathematics and physics, sparked by an interest in STEM subjects from a young age, led to academic achievements and eventual disillusionment with physics research. This realization prompted a shift towards data science after watching a documentary on AI by DeepMind, which piqued their interest in machine learning and its applications.

05:01

📚 Learning Path in Data Science

The speaker outlines their learning process in data science, which involved leveraging their existing knowledge in mathematics, statistics, and physics. They took Andrew Ng's Machine Learning specialization course and transitioned from Fortran to Python, learning essential programming concepts and data science-specific packages. The speaker also learned SQL and engaged in practical projects by applying machine learning models to datasets from Kaggle. They highlight the challenge of securing the first job in data science, having applied to over 300 roles, and stress the importance of persistence and hands-on experience.

10:02

🚀 Standing Out as a Data Scientist

To stand out as a data scientist, the speaker recommends three key actions: maintaining an active GitHub profile to showcase skills and projects, writing blog posts to document learning and demonstrate interest in the field, and participating in Kaggle competitions to apply data science skills to real-world problems. They argue that these activities offer significant rewards relative to the effort invested and can differentiate candidates, especially those seeking entry-level positions. The speaker concludes by encouraging viewers to take these steps and to follow for more insights into breaking into the field of data science.

Keywords

💡Data Scientist

A data scientist is a professional who utilizes scientific and statistical methods to extract insights from structured and unstructured data. In the video, the speaker details their journey into becoming a data scientist, which is central to the theme of career development and the rise of AI and data-driven decision-making.

💡STEM

STEM stands for Science, Technology, Engineering, and Mathematics. It represents a broad educational and professional domain that the speaker was naturally inclined towards, as indicated by their family background in math and physics, and is foundational to the video's narrative of pursuing a career in data science.

💡Machine Learning

Machine learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. The speaker's interest in machine learning was sparked by a documentary on AI, and it is a key component of the data science field they eventually entered.

💡Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The speaker mentions it as one of the concepts they found interesting, which is part of the broader theme of learning and applying complex algorithms in data science.

💡Deep Learning

Deep learning is a branch of machine learning based on artificial neural networks with representation learning. The speaker's fascination with how AI was trained to be a world champion in Go led them to explore deep learning, which is integral to the video's message about the allure and complexity of AI technologies.

💡Programming Language

A programming language is a formal language comprising a set of instructions used to produce various kinds of output. The speaker discusses their experience with Fortran and Python, emphasizing the importance of learning programming languages in the context of data science.

💡Python

Python is a high-level, interpreted, and general-purpose programming language that is widely used for back-end web development, data analysis, machine learning, and more. The speaker's learning of Python is highlighted as a crucial step in their journey to becoming a data scientist.

💡SQL

SQL (Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system. The speaker mentions learning SQL as part of their skill development for a career in data science.

💡GitHub

GitHub is a web-based hosting service for version control using Git. It is a platform where developers can share code and collaborate on projects. The speaker advises having a GitHub profile to showcase one's coding projects and skills, which is a practical tip for job seekers in the data science field.

💡Kaggle

Kaggle is an online community of data scientists and machine learning practitioners that provides a platform for predictive modeling and analytics competitions. The speaker recommends participating in Kaggle competitions as a way to demonstrate practical data science skills and problem-solving abilities.

💡Blog Post

A blog post is an individual written article or story posted to a blog. The speaker suggests writing blog posts as a means to document and showcase one's learning process and interests in data science, which can help in standing out as a job candidate.

Highlights

The speaker details their journey into becoming a data scientist amidst the rise of AI.

A strong math background influenced by family members with degrees in math and physics.

Early interest in physics sparked by watching The Big Bang Theory and further research on quantum physics.

Achieved four A*s, 3 As, and 5 Bs in GCSEs, reflecting a natural inclination towards STEM subjects.

Lack of work ethic in school days and the realization of being overconfident in abilities.

Rejection from top universities and acceptance into the University of Surrey through clearing.

The importance of hard work realized during university studies, leading to first-class honors.

Master's program involvement in research at the National Physical Laboratory on acoustic thermometry.

A shift in career interest from physics to data science after watching a documentary on AlphaGo.

The discovery of data science as a field that intersects with various disciplines like math, statistics, and computer science.

Learning machine learning algorithms and the importance of prerequisite knowledge in linear algebra and calculus.

Taking Andrew Ng's Machine Learning specialization course and transitioning from Fortran to Python.

Learning Python syntax, functions, loops, and classes through university resources and online courses.

Acquiring SQL skills through online courses and its importance in day-to-day data science tasks.

Building simple projects on Kaggle to apply machine learning models and gain practical experience.

The challenge of securing the first data science job with over 300 applications during university.

Starting the first data science role at an insurance company and the importance of hands-on experience.

Advice on standing out as a data scientist by having a GitHub profile, writing blog posts, and participating in Kaggle competitions.

Emphasizing that there is no one-size-fits-all path to becoming a data scientist and the value of personal effort.

Transcripts

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