Complete Data Scientist/ML Engineer Roadmap for beginners

Summary

The video script emphasizes the challenges and the unconventional path to becoming a successful data scientist. It highlights the importance of adopting the right mindset, dedicating time and effort, and standing out from the crowd. The speaker outlines a roadmap for learning programming, particularly Python, mastering data analysis tools, understanding mathematical concepts crucial for machine learning, and exploring MLOPS. The advice centers around not seeking quick fixes, but instead, committing to a rigorous, long-term learning process that includes studying documentation, solving problems, and building a strong foundation in both theory and practical applications.

Takeaways

• 🚀 Embrace the 'hardest way' mindset for success in data science, involving dedication and commitment over a significant period of time.
• 🛣️ Recognize that standing out from the crowd requires a differentiator; aim to be in the top 1% rather than following the 99% who follow conventional paths.
• 🥊 Dedicate 8 months to 1 year to fully immerse yourself in the data science domain, accepting that the journey might involve not understanding certain concepts for months.
• 📚 Learn Python, but focus on understanding its documentation and becoming a 'generalizer' who can solve problems using available features, not just memorizing from tutorials.
• 🌟 Aim to write better, well-structured code using design patterns, which are highly valued in the industry and can open up software engineering roles in Python.
• 📊 Begin your data analysis journey with essential libraries like pandas, numpy, and matplotlib, and consider learning Excel and Tableau for data visualization.
• 📈 Strengthen your data analysis skills by learning SQL, which is crucial for working with databases and a key skill in the field.
• 🔢 Develop a strong foundation in mathematics, particularly linear algebra, calculus, probability, and statistics, as they are fundamental to machine learning and data science.
• 📚 For machine learning, go beyond surface-level understanding by reading research papers and books to appreciate the depth and evolution of concepts.
• 🏭 As you progress, consider learning MLOps (Machine Learning Operations), which is becoming increasingly important in the industry and can give you an edge in job applications.

Q & A

What is the main reason for the data science job market not being fully utilized?

The main reason is that 99% of data science aspirants are doing the same thing, leading to a lack of differentiation. Only the 1% who do things differently and have a unique approach are able to secure high-paying jobs.

What is the recommended mindset for someone starting off in data science or any other field?

The recommended mindset is to understand that there is no easy or quick way to success. The only path to success is the hardest way, which involves dedication, commitment, and hard work over a significant period of time.

Why is it important to learn Python for a career in data science?

Python is a crucial programming language in the data science field. It is versatile, widely used, and has a large community and ecosystem of libraries and tools that are essential for data analysis, machine learning, and other data science tasks.

What does it mean to be a 'generalizer' in programming?

A generalizer is someone who is not spoon-fed but knows how to use existing features and technologies to solve specific problems. They are adaptable and can apply their knowledge to a wide range of tasks, making them highly valuable in the job market.

How can one stand out from the crowd in the data science field?

To stand out, one should focus on learning deeply, understanding the core concepts, and applying them in unique ways. This includes writing better, more efficient, and well-documented code, and being able to solve problems using available resources and technologies.

What are some key libraries for data analysis that one should learn?

Key libraries for data analysis include pandas, numpy, and matplotlib. These libraries are foundational and essential for handling data, performing calculations, and visualizing data effectively.

Why is SQL important for data science roles?

SQL is important because it is the standard language for managing and querying relational databases. It allows data scientists to efficiently retrieve, manipulate, and analyze large sets of data, which is a critical skill in most data-related roles.

What mathematical concepts are fundamental to machine learning and data science?

Linear algebra, calculus, probability, and statistics are fundamental mathematical concepts for machine learning and data science. They form the basis for understanding algorithms and models used in these fields.

What is the best way to learn machine learning algorithms?

The best way to learn machine learning algorithms is not just by watching videos but by reading comprehensive books and research papers on the topics. Understanding the origins and development of these algorithms provides deeper insights and a stronger foundation.

What is MLOps and why is it becoming increasingly important in data science roles?

MLOps refers to the practices for managing the end-to-end lifecycle of machine learning models. It is becoming important because it helps in the efficient deployment, monitoring, and maintenance of models, ensuring scalability and reliability of machine learning solutions.

How can one differentiate themselves in the job market after mastering data science skills?

By mastering data science skills and understanding the core concepts deeply, one can differentiate themselves by working on unique projects, contributing to the community, and continuously learning about new developments and technologies in the field.

Outlines

00:00

🚀 Becoming a Data Scientist: The Roadmap

This paragraph introduces the challenges faced by data science aspirants and emphasizes the importance of having a differentiator. It outlines a step-by-step roadmap for becoming a data scientist, highlighting that it is not a quick or easy path but the most effective one. The speaker stresses the need for a growth mindset, dedication, and hard work, even when faced with initial difficulties in understanding complex concepts. The importance of choosing the right domain and learning the programming language, specifically Python, is also discussed, with an emphasis on learning differently to stand out from the crowd.

05:01

🌟 The Generalizer: Mastering Python

The second paragraph delves into the concept of a 'generalizer' in the context of programming and problem-solving. It explains that a generalizer is someone who can use existing knowledge and tools to solve new problems, which is highly valued in the job market. The speaker shares advice from senior engineers and emphasizes the importance of writing better, well-structured code using design patterns. The paragraph also introduces resources for learning design patterns and Python, and how these skills can open up new job opportunities, such as software engineering in Python.

10:01

📊 Data Analysis: Foundation of Data Science

This paragraph focuses on the importance of data analysis in the journey to becoming a data scientist. It stresses the need to learn key libraries like pandas and NumPy, and the significance of Excel and Tableau for data visualization. The speaker also discusses the importance of learning SQL and shares personal experiences with a platform that offers valuable SQL courses. The paragraph ends with a note on the value of community and networking with like-minded individuals in the field.

15:01

📚 Mathematics and Machine Learning: Core Competencies

The third paragraph emphasizes the crucial role of mathematics in machine learning and data science. It identifies key mathematical topics such as linear algebra, calculus, probability, statistics, and information theory. The speaker suggests learning mathematics not just by solving problems, but by understanding its geometrical aspects and beauty. The paragraph also provides advice on how to approach machine learning by reading seminal research papers and understanding the evolution of algorithms from their origins to their current sophisticated forms.

🛠️ MLOps and the Future of Data Science

The final paragraph discusses the emerging importance of MLOps in data science roles. The speaker shares personal experience working with MLOps frameworks and emphasizes the growing demand for these skills in the industry. It introduces a specific MLOps library called Ziml and encourages continuous learning and project building on top of it. The paragraph concludes with an invitation to a live webinar on project building, offering a unique approach to standing out in the competitive field of data science.

Keywords

💡Data Science

Data Science refers to the field of study concerned with the processes and systems for extracting knowledge and insights from data. In the video, it is highlighted as an area with numerous opportunities, yet one where many aspirants struggle to secure positions due to a lack of differentiation.

💡Differentiation

Differentiation in the context of the video refers to the unique qualities or skills that set an individual apart from others. It is emphasized as a crucial factor for those in the data science field to stand out and secure high-paying jobs.

💡Mindset

Mindset, as discussed in the video, pertains to the attitude or perspective one adopts towards their learning and career journey. The speaker advocates for a mindset that embraces the 'hardest way' to success, which involves dedication, effort, and a willingness to face challenges head-on.

💡Python

Python is a high-level programming language noted for its readability and ease of use, making it a popular choice for data science and machine learning applications. The video underscores the necessity of learning Python, but also emphasizes the importance of learning it in a way that distinguishes one from the majority.

💡Generalizer

A 'generalizer' in the context of programming is an individual who is not only knowledgeable about a programming language but can also apply that knowledge flexibly to solve a variety of problems. This term is used to describe someone who can think critically and adapt existing tools to address new challenges.

💡Design Patterns

Design patterns are reusable solutions to common software design problems. They are templates or guidelines that help programmers write efficient, maintainable, and scalable code. In the video, learning design patterns in Python is presented as a way to write better, more structured code, setting oneself apart from other programmers.

💡Data Analysis

Data analysis involves inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. The video stresses that data analysis is a fundamental skill for anyone in the data science field, regardless of whether they aspire to be a data analyst.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable systems to learn from and make predictions or decisions based on data. The video positions machine learning as a core component of data science, with mathematics being its foundational strength.

💡SQL

SQL (Structured Query Language) is a domain-specific language used to manage and query relational databases. It is an essential skill for data analysts and scientists to retrieve and manipulate data efficiently.

💡MLOps

MLOps refers to the set of practices, tools, and processes for deploying, monitoring, and maintaining machine learning models in production. It is becoming increasingly important in the data science field to ensure models are scalable, reliable, and efficient.

💡ZenML

ZenML is an open-source MLOps framework designed to standardize the machine learning lifecycle, from data preparation to model deployment. It is highlighted as a valuable tool for those looking to excel in the data science domain.

Highlights

The current data science job market has millions of opportunities, yet a majority remain unfilled due to a lack of qualified candidates.

99% of data science aspirants follow the same path, leading to saturation and limited job opportunities, whereas the top 1% with a unique differentiator secure high-paying jobs.

The speaker emphasizes the importance of adopting a mindset of perseverance and dedication, warning that an easy path often leads to being part of the majority.

To stand out, one must be prepared to commit 8 months to a year to fully immerse themselves in the data science field.

The initial challenge of understanding complex subjects like mathematics or algorithms is a positive sign of learning and growth.

The speaker shares their personal experience of starting their journey by waking up early and studying for long hours, highlighting the necessity of hard work.

Learning the right programming language, such as Python, is crucial, but the key is to learn differently to become part of the top 1%.

The importance of becoming a 'generalizer' in programming, someone who can solve problems using existing technology and features, is stressed.

Design patterns in Python are highlighted as a way to write better, scalable, defensive, and well-documented code.

The speaker advises learning data analysis tools and libraries such as pandas, numpy, and matplotlib to build a strong foundation in data science.

Excel and Tableau are recommended for data analysis visualization, and SQL is deemed essential for working with data analytics.

Mathematics is the core strength of machine learning and data science; linear algebra, calculus, probability, and statistics are particularly important.

The speaker suggests learning machine learning not just by solving problems but by understanding its geometrical beauty and how it applies to the field.

Machine Learning Operations (MLOps) is becoming a compulsory skill in the industry, and knowledge in this area can significantly boost job prospects.

The speaker shares their personal experience with MLOps and recommends learning about the ZML library for its alignment with MLOps principles.

Building projects in a unique way that differentiates oneself from the majority is crucial, and the speaker offers a live webinar on this topic.

Transcripts

there are millions of data science

opportunities but most of them are not

being filled up but at the same time

there are millions of students but

they're not even able to get a single

data science job or an internship it's

because 99% of the data science

aspirants are just doing the same thing

and that 1% who is doing things

differently and having a differentiator

in them are actually grabbing highp

paying jobs I'm going to give you this

stepbystep road map that not only helps

you become data scientist but transforms

you into one and it's not something

quick or easy way is the hardest way to

become the data scientist and after you

complete this road map you'll be not

just having one role as a data scientist

but throughout this road map you will

see different roles will'll be opening

up for you so if you want to apply in

between to any of the companies for a

specific set of roles then for sure

you're open to but before proceeding to

the technical details I'm going to make

one thing specifically clear the mindset

which my viewer or my student should

have if they're watching this video if

you cannot agree with this mindset or if

you don't want to then for sure please

feel free to leave the video so the

mindset which I really want my students

to adopt over the period of a time is if

you're starting off in any of the field

whether it be data science webd or

anything you should be very clear that

there's no smart way or probably the

quickest or easy way sort of thing

there's only one way which comes into

the play which is the hardest way it can

definitely be backed up by several other

factors but there's only one way which

can lead you to success which is the

hardest way so if your journey is

feeling very easy comfortable

non-differentiated that you're doing the

same thing which everybody is doing then

probably you'll be again doing that what

99% of the people are doing over here so

make up a mindset from now to 8 months

to 1 year you should entirely dedicated

and committed to this domain and it may

happen that you're not able to

understand even a single thing for

months when I was starting off I was

also not able to understand things like

mathematics or probably algorithms for

straight 3 to four months but it's

completely all right it's indicating

that you're actually learning if you're

not able to understand because you're

putting in effort to understand that

thing and you should continue this over

the period of a months and you should

put your day and nights off your

everything off your every dist

instruction which you have I will tell

you when I was starting off my journey I

used to wake up in the morning around

5:00 a.m. and then I used to study till

10:00 p.m. in the night and of course I

used to take breaks about 1 hour in

between people just see the output of

the success people don't see what

exactly the input which is required and

after you become successful you can

think about whatever way you want

currently I work in a smart way not the

hardest way so it's like once you become

something then think about how you want

to do things and now if you have made up

the mind mindset then only proceed with

the barer the first thing into this

domain which you should definitely

consider is what domain you want to go

in some of the domains are data

scientist machine learning Engineers

envelops engineer data analyst data

analyst consultant and much more and

this road map is applicable to most of

the data and machine learning related

roles the first and the foremost thing

is to learn the right programming

language which is python but everybody

on YouTube is telling you to Learn

Python and every road map has it to be

honest yes it's definitely required but

but what really matters is that how you

are learning this which means that that

will make you the different from those

99% of the people out there and you have

to learn differently to become that top

1% but if you already have a knowledge

of python and your little bit of

programming what you can really do is

simply go ahead and then skip this step

if you're new to the programming the

first thing which you should do is

simply go on YouTube and search Learn

Python one shot see any of the videos

I'm not vouching any creators see any

any of the videos you should be able to

at least write code which is print hello

world and if you already have a basic

knowledge about programming then it is

not required at all for you will it make

you a difference of course not it will

make you that 99% of the people who are

doing the same thing now what you should

do you should search online and go

python documentation now when you go to

the python documentation you will see

the take of table of contents and if you

follow that you should be able to become

a pretty good coder but wait there's a

catch there's a line written or a phrase

is written that it says that this

documentation is not a comprehensive and

it does not covers each and every single

features of python but if you complete

this you should be able to write

understand and run your python code

which means it is telling a very crucial

Insight which only few Engineers or few

aspirants are able to DCT Cod what it is

really saying is you to become a

generalizer so let's talk about what

exactly the generalizer means so say for

example you know the core and the Crux

of uh python now it says now start

working on problems now if that problem

if you've already learned about how to

solve that from what you have read in

the table of contents or the

documentation good to go and Implement

that but if you don't know then you

should come back to the documentation

see in more specific details if that

feature is available to solve the

problem and if not how you can use

existing features in a way that solves

the specific problems and that's what

generalizers are generalizers are not

spoon feeder know everything even I

don't know everything but if I have a

problem how I can solve this with the

existing technology which is available

and that's what generalizers are usually

meant for and generalizers are only

getting jobs and that's the gold mine

advice I am working for the past 3 to

four years

and this is the advice which I got from

senior Engineers from Amazon Google IBM

and much more and this is something

which I want you to implement in your

life now you have completed your

programming track you are probably

better than some of the people but to

become the actual G in Python what you

should actually do is write better code

and that's where design patterns in

Python comes into the rescue which

really means that that you should be

able to write a code in a way that is

scalable defensive well documented much

more features of python so design

patterns is very rare which I see in the

python or data science aspirant but this

is one of the most important part for

any of the job roles out there everybody

can write code but what matters is how

well and how nicely you present your

code which is well structured and the

code

qualities and that's the most important

thing so if you're able to do this

you're are probably the actual G and

probably better than those 99% of the

people I've given you some of the best

resources best books around design

patterns and python in the description

rbox Bel in the PDF format you should be

able to go and check that out and each

and every R stated in the video should

be easily able to find out over there

but wait you have literally opened a new

job role with this which is software

engineering in Python and if you want so

you can just go ahead and then start

applying to this these kind of jobs and

that's it but now if you want to

continue your journey then you should

continue listening the video you should

start post programming track you should

start with data analysis ASAP whether

you want to become data analyst or not

this is the most important thing out

there it really involves identifying

data resources identifying wrong

connections troubl soting Excel writing

complex equal queries and much more to

get started with this domain I would

highly suggest you to first of all learn

the necessary libraries which is the

most important part of it and one of my

director where I was working in the past

they said there's no point in learning

machine learning if you don't know these

necessary libraries which is pandas naai

and map these are the bearback and

probably the pillars or the foundations

which you should definitely have into

your toolbox I've given you some of the

in depth the hard resources and the

lengthiest thing we you should should

follow in order to become a generalizer

know the core in the Crux and know how

to solve a problem using these libraries

but the best thing is that you can

literally learn all of this via their

official documentation or specific topic

based books and the links are in the PDF

and post that you should consider

learning about Excel which is still the

most important and still applicable to

most of the companies out there and you

should also consider learning Tableau

because there's no point in working as a

data analysis and you don't know Tableau

because you should analyze data and if

you're not able to visualize and

understand data then there's no point

about that so Tableau is one of the most

important de developer tools which you

should have into your toolbox and then

post that you should consider learning

SQL The King The King makers and that's

the most important part as I say I

personally don't host any data analytics

course but I have one of the platform

which I personally went through and

asked them the access to through the

course they were very interest to give

me the access to the course and I went

through it and it was amazing things

over there so I personally like that so

I would suggest you to take a look this

is not such a promotion or sponsorship

this is just that I personally like that

I've seen so many students from there

succeeding so probably you can it's

better to give it a shot so you should

not consider paying initially you should

go to the free course see how exactly

they're teaching what they're teaching

and then you can make some decision

going forward because I'm a big fan of

first of validating and seeing if it's

really something for you so that's it

about if you want to go for the uh good

resources which I personally find in the

paid enrollments but if you want to see

some free stops I personally mentioned

some of the books some of the resources

which I find it personally good in terms

of the PDF which you can find in the

description B box below and to be honest

I personally like course careers

features which I exactly have at Anon

which is resume forting 101 student and

instructor coaches which I don't

generally find in other platforms and

another thing which I really like about

them is they have the kind of a network

of the community because I suggest you

to get into the community where you can

literally connect Network and talk with

like-minded people now once you're done

with the data analysis and you're

comfortable with that you should start

applying to new set of jobs ASAP which

is data analytics analytical consultancy

and other such roles like SQL Developer

Tableau developer and much more now it's

time for mathematics the core and

strength of machine learning and data

science so there are some of the topics

which is extremely important the first

one is linear algebra which is pretty

much used into the space calculus and

probability and statistics there's one

more known as information Theory but

it's a little advanced stuff it will

come over the later period of a time but

these three things are extremely

important for you to know my course

teaches all of this but my course

enrollments are closed as of now so I

would suggest you to fill up and

interest if you want to know when the

next patch comes up and there's one

trick which I really want to give you is

learn mathematics not by solving things

because in machine learning you will not

solve by hand you should learn machine

mathematics for machine learning is by

understanding its beauty it's by

understanding its geometrical aspect

then only you can relate how mathematics

is going to be used into the space of

machine learning and data science so

once you're done with the maths part

particularly now it's the time for core

machine learning I have entire video on

the road map for machine learning which

has got a 300K plus views which is quite

amazing till now but there's one trick

which I want you to know before

proceeding to that road map is that say

for example you have a topic known as

regression analysis so most of the

people on YouTube what they really do

they just go and just see one to two

hour of YouTube videos which is really

really kind of everybody is doing that

but you know you have a specific 600

pager book on every topic which you see

in machine learning I don't want you to

complete everything but I want you to

take a look and probably complete 50% of

it because most of the people don't have

a probably time and probably that that

guts to complete those sort of books I

had the time and the guts to complete

that say for example you're learning

decision tree or such set of examples

what you should do you should go to

their research paper from where it is

originated from you will see the beauty

that people coming from a very some lame

thing and then trying to convert that to

a literal powerful systems which is

pretty pretty amazing which is pretty

exciting even if I'm talking right now

it's just giving me so chills that how

something very very small is able to

build the foundation is able to

generalize to a level that it is in a

billions of parameters today towards

like gpts clots are coming towards so

I'd highly suggest you read the '90s

research papers which was published

because they tell you their type of

technology which you already know and

how they use that in order to build this

and that's the best advice which I could

give you into this once you're done with

this stuff you're probably open for the

machine learning most of the machine

learning engineering data scientist or

consultant roles but here's the couch I

say most of them not all of them to make

all of them you should learn a

technology which is now becoming the

compulsory things into the jobs and

probably coming becoming the bonus thing

which will really help you which is

mlops I'm a big fan of mlops I have

worked as one of the largest framework

with the best frameworker in emops which

is jml and uh over there I have worked

as emops in ja and I know the importance

of it and the importance that the

company really gives to the emop skies I

personally have a specific road map

about envelops into the YouTube channel

all of the links can be found in the

description you can go and check that

out so I definitely want you to take a

look at the emops video which I

published but post that there's one

thing which I would want you to learn to

know more better about emops is learning

about a library known as ziml again it's

one of the best framework not because

I've worked but because of their kind of

libraries and the concept which they

Implement from envelops principles so I

highly suggest about zml the specific

road map for zml is published into the

PDF but for every part you need to

continuously revisit that revise that

and build projects on top of it I've not

told you how to build projects but to be

honest if you want to know more about it

I am hosting a live webinar which is 1.5

hour webinar you can find the date

everything in the description about how

to build a projects in a unique way that

that will differentiate you from those

99% of people so if you're interested

definitely check that out but yeah

that's definitely it thank you so much

I'll catch you up in another video bye